Metis offers data science training via 12-week full-time immersive bootcamps, evening part-time professional development courses, online resources, and corporate programs in various US cities. The Metis Data Science Bootcamp is designed by industry practitioners and provides students with on-site instruction, access to an extensive network of speakers and mentors, events, and ongoing career coaching and job placement support. Graduates will be comfortable designing, implementing, and communicating the results of a data science project, will grasp the fundamentals of data visualization, and will get exposure to modern big data tools and architecture such as the Hadoop stack. Successful Metis alumni will have skills in Python, Bash, algorithms, linear regression, machine learning, databases, D3, Hadoop, Hive, and Spark. The data science curriculum is delivered through project-based, hands-on, collaborative learning.
To apply for the Metis Data Science Bootcamp, applicants need to have experience with programming and statistics, and complete 25 hours of academic pre-work. Metis offers an Admissions Prep course for those who need to brush up on their algebra, calculus, math, and Python skills. Metis is looking for students eager to get their hands dirty by learning new technologies and solving real-life problems, and who have the skills needed to secure entry-level jobs in the Data Science field. Graduates leave fully qualified for a data scientist job, with placement programs available to all graduates.
Metis is also authorized to enroll international students with M-1 visas, which allow non-U.S. students to attend technical and vocational programs in the U.S. International students who are already in the U.S. on an F-1 visa may also transfer to Metis.
Recent Metis Reviews: Rating 4.84
Recent Metis News
- February 2019 Coding Bootcamp Podcast
- 2 Years Later: Where are These Metis Grads?
- Getting Started in SQL: A Primer with Metis
Considering applying to our Data Science Bootcamp but need to build or brush up on your basic skills first? Our Beginner Python & Math for Data Science course was designed for you, the beginner looking for an introduction to the building blocks essential to developing data science skills or forging a new career in the field. This course sets you on the right track, covering everything you’ll encounter during the bootcamp application process. You’ll learn: Basics of Python programming Common Python libraries: NumPy, Pandas, Matplotlib Foundations of linear algebra, calculus, probability and statistics Please note, the cost of Beginner Python & Math for Data Science can be applied to the cost of the bootcamp once you apply. We welcome all students looking to brush up on data science basics, not just those looking to apply to the bootcamp. Please visit our website to learn more and don't hesitate to contact us with any questions about Beginner Python & Math for Data Science, the Data Science Bootcamp, or any other inquiries.
- Start Date
- None scheduled
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- Minimum Skill Level
- Absolute Beginner
- Placement Test
- Data Science, Git, Python, SQL, Hadoop, Data Visualization, Data Analytics
In PersonFull Time40 Hours/week12 Weeks
- Start Date
- None scheduled
- Class size
- San Francisco, New York City, Chicago, Seattle
- We partner with Skills Fund, an innovative financing company that offers financing options for students accepted to our bootcamp. Visit our website to learn more.
- We offer a $3,000 scholarship for women, members of underrepresented groups, the LGBTQ community, or veterans of U.S. military personnel. Visit our website to learn more.
- Minimum Skill Level
- Prep Work
- Once students are enrolled in the bootcamp, they are granted immediate access to our prework materials, a structured program of 25 hours of academic pre-work and up to 35 hours of set-up is designed to get admitted students warmed up and ready to go.
- Placement Test
This course takes you one step closer to becoming a data scientist by offering a subset of the topics covered in our Data Science Bootcamp. You’ll get a well-rounded intro to the core concepts and technologies taught within the bootcamp, including basic machine learning principles and hands-on coding experience. Plus, you’ll put it all to practice through a mini data science project of your own. We’ll cover the following: Data acquisition, cleaning, and aggregation Exploratory data analysis and visualization Feature engineering Model creation and validation Basic statistical and mathematical foundations for data science We welcome all students looking to brush up on data science basics, not just those looking to apply to the bootcamp. Please visit our website to learn more and don't hesitate to contact us with any questions about Introduction to Data Science, the Data Science Bootcamp, or any other inquiries.
- Start Date
- None scheduled
- Class size
- Online, San Francisco, New York City, Chicago, Seattle
- Minimum Skill Level
- Students should have some familiarity with basic statistical and linear algebraic concepts. In Python, it will be helpful to know basic data structures.
- Placement Test
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I jumped at the chance to go back to the technical track of my career after spending far too long in technical management. My academic background has been in computer science and math, and I started professionally (a while ago) as a data dev and analyst.
Of the four or five data science immersives I researched, including the other two in-person full time programs in Seattle, Metis is the one that places more rigor and time in the data science aspect of the curriculum, as opposed to the data engineering side of it: data science first, the tools to do it second. Most other programs dedicate too much time to hashing over ETL and development best practices. Those are absolutely necessary in real-life data science positions (ask me about it over beer some time), but they should be a pre-requisite, as they are in Metis, in order to effectively immerse you in data science without distractions during the program.
That doesn't mean you end up being a stats nerd with no practical programming skills, on the contrary. The day starts with a programming brain teaser to fire up the neurons as the coffee kicks in, and the evening ends with you turning in a solid piece of working code that excercises the morning's data science theory and tools lecture.
I'm also always amazed at the quality of the people that make it into the program. From all walks of life, age groups and skill sets, but the common thread is that they have the intuition for data science, plus the drive to learn and do what needs to be done to master it.
And the people working for Metis are also top-notch: skilled, knowledgable, good instructors, administrators, and boosters for the data science professions in the business econsystem.
This Bootcamp has been one of the most rewarding experience. I got to learn alot and was introduced to so many new techniques regarding Data Science.
I had the pleasure to learn from Alice Zhao and Lara Katten. Both instructors are knowledgeable and are helpful through out the 12 weeks. Both of them are super humble for how much they know. They are perfect compliments.
The best analogy descibing this Bootcamp would be, say there are about 50 different doors where each door pertains to different topics in Data Science (e.g. Git, Data Viz, NoSQL, SQL, Supervised Learning, Unsupercised Learing etc). METIS opens these doors for you and helps you take the first step on the other side, then it is up to you to choose the topics you want to delve deeper into.
If you are thinking of making a career change to Data Science, please consider METIS Bootcamp. It will save you a lot of time and money.
P.S. Everybody has their own interpretation of ratings. Please note I never give a perfect rating, for me a four star is a very high rating.
Metis turned me into a Data Scientist! I worked as a business analyst for about two years and had some exposure to analytics. But I lacked hands-on experience and a good understanding of machine learning. I tried to change my career path on my own but didn't have much luck. Trying to self-teach and rebrand, all on my own, was daunting and very stressful. I'm really glad that I found Metis and decided to commit to it.
Yes, you might be able to find all the materials online and learn everything on your own. But what makes Metis such a good experience is the instructors and career advisors. They really do care about your success! The instructors are not only good at explaining concepts but also have a lot of industry experience. I learned a lot about how to approach projects and design metrics from all the discussions I had with my instructors.
The bootcamp itself is tough and the job search after graduation is not any easier. Metis offers a lot of coaching and support to help me get to where I am today. Also, going through the job search process with my classmates provided a very crucial support network, and made it much easier to stick with it. I got multiple interviews through alumni referral and eventually landed a role through one of these referrals. The Metis community is incredible and I know it will be there to help me navigate through any challenges I have in my career.
I joined Metis after reading reviews on websites such as this (Coursereport), and after attending one of their Meetups. In spite of all the 4-5 star reviews that I had seen, Metis beat all my expectations! Let me elaborate.
I earned my PhD in Experimental Chemistry at the University of Chicago. While my research was somewhat computational in nature, I was not well-equipped to get started with a career in Data Science. If you're from academia, you have to understand that universities have no interest or incentive to prepare their cheap-labor postdocs for real jobs. In spite of having science PhDs, half of my colleagues in academia weren't able to find a single job whatsoever, and some were already past their 5th year as postdoc (we're talking Physics, Chemistry, Math PhDs). Metis was instrumental in equipping me with all of the industry-relevant skills in a very short amount of time. They were also great with demonstrating the utility of ML/AI and the potential impact that it is likely to create in the next few decades or so. While one can technically learn all of this material by oneself, it would take at least a 9 months even if you did it full-time. It would take you multiple years if you tried learning all of it while doing some job or postdoc, which is why most postdocs can't make that transition by themselves.
Alice Zhao and Roberto Reif were both great instructors with many years of industry experience. I miss the energy from our instructors and cohort. Three weeks into the bootcamp, I remember a few of us discussing about how good the bootcamp was. Fast forward 6 months, and most of us who were looking for a job have found one. Having an advanced degree helps, but not having one is completely okay. I now work for a firm called Soothsayer Analytics (with Microsoft as end-client).
My only minor complaint from the bootcamp is that it doesn't delve too much into SQL or Time-Series Analysis, and a lot of companies like Facebook or Expedia make you go through really tricky SQL questions.
TL;DR: Metis was amazing. The instructors set the tone for excitement, grit, intelligence, and success. The projects were invaluable experiences for learning so many data science skills and for using as examples of work in interviews/applications. The curriculum is as close to real-world experience as a bootcamp can get. A+ to Metis
I found Metis after several months of knowing I needed a career change and searching for the right way to jumpstart my return to tech after two years of completely unrelated work. I graduated undergrad as a mechanical engineer, worked as a research engineer, but then took a hiatus after moving to Seattle and worked as an assistant manager at a restaurant. I learned about data science through friends already working in tech in Seattle and realized I already had some of the skills from mechanical engineering and working in research, but I didn't have any recent relevant experience that would allow me to get a job anywhere. That's when I found Metis. I compared it against a couple other bootcamps, but honestly there was no comparison. Metis was an actual data science bootcamp - not just a coding bootcamp. Metis promised instructors with real-world experience and work-life balance. Metis promised a portfolio of work based on five data science projects compelted during the bootcamp. Metis was accreditted. And Metis was small, but already had excited students and a reputation for being hands-on, real-world-reflecting, and full of direct career support.
I was not disappointed. The application process was challenging, but appropriately so. My engineering background provided me with the proper math and thinking skills and I was accepted into the winter cohort of 2017. Then I had to complete the prework before starting in September; that was also very challenging, but totally necessary and I highly encourage you to do the prework if you want to do well. I had no prior programming experience other than Matlab, but I was able to teach myself enough Python in the month before starting to be completely capable during the bootcamp. That leads to another piece of advice: you get out of Metis what you put in. In other words, Metis is good for you if you're willing to put in the effort and the hours and learn a ton.
My final note, regarding my Metis experience, would be that the people made it what it is. The instructors were amazing; so intelligent, so in touch with real-world data science, so excited to be there and teach and guide, and just so overall great people. The rest of the cohort was amazing too; the application process and just the type of people Metis attracts includes only intelligent, passionate, and driven people. This was what drove the learning environment and what truly helped me get excited, challenge myself, and succeed.
For those of you interested in where you could end up: I am now a data scientist at a tech consulting company in Redmond. Because of my more recent graduation date, lack of an advanced degree, and minimal relevant paid experience, I began my role as an intern at the company. Do not let this deter you; the internship was invaluable especially after a particularly grueling application period (where I was applying to almost five companies each day for several months). I learned a ton during the internship and it was an opportunity to take risks in a lower pressure environment.
Being a math major, I wanted a deeper understanding of data science at an application based level, which I knew a masters program would not achieve. I looked across all the different bootcamps, and I found Metis as the only accredited course. The quality of instructors attracted me the most to Metis, and it is the only bootcamp that makes students complete 5 end-to-end projects. This fell in line with my urge to learn data science at an application based level. Each instructor has previous experience in the data science field from work to educational experience. These instructors made learning these complex topics easier to understand by applying basic real world analogies to them. Additionally, the instructors went around to students every day and made sure there were no lingering questions as a group and 1:1 basis.
Deciding to do Metis was a huge step for me, the financial, time, and educational undertaking were all factors in play when I decided to join Metis. Financially, Metis is the same across all bootcamps, and it is cheaper than completing a data science masters program. The time commitment is ideal as it is just 3 months; however, I would not recommend doing any job (part time included) with this course as you get more out of the course by solely focusing on the course itself. The educational undertaking seemed questionable at first but with the strong instruction from the instructors they cleared up any loose ends.
Regarding the course, I would highly encourage starting the pre-course work as soon as you receive it and make sure you understand each topic well. This will help you in every mornings pair problems and save time on your projects. One thing that helped me learn the data science algorithms better was that I would watch Andrew Ng’s videos on the topic we are learning for that day before class. This helped me absorb the information better than learning it for the first time in class. I also recommend completing the challenges that are given by the instructors. These are optional, but by completing a few of them, I was able to gain a stronger understanding of each topic.
Metis’s career support is extremely strong. The career support staff makes sure to get you connected with individuals at companies that you are interested in. The staff helps you get prepared for interviews by setting up frequent mock interviews. Joining Metis was an overall positive experience, and it changed my life for the better.
I began my search to build skills towards data science without any real knowledge of what specific subjects or topics I needed to understand in the field, other than knowing a programming language such as Python. I researched various programs that offered a variety of courses but noticed that Metis was purely dedicated to data science. I looked over the courses (and accompanied curriculum) provided by Metis and was initially stuck between taking the Introduction to Data Science, or the Beginner Python & Math courses. I reached out to the team at Metis, and had an informative and helpful conversation with Amy Ramnath, and ultimately decided that the Beginner Python & Math course best suited my needs as a beginner looking to get his feet wet.
I took flight on my journey through the 6-week online Beginner Python & Math course from May 21st to June 27th twice a week, 3-hours per lecture, with Garrett Hoffman and Roberto Reif. Both Garrett and Roberto were fantastic teachers who are passionate about the subject matter, and whom make themselves available to assist students in answering questions or providing professional suggestions and recommendations towards student goals. There is copious material to cover and review throughout the 6-week course to get you rolling in the right direction, and as with any educational course, personal time allocated to studying and practicing outside of normal lecture hours should be anticipated and expected. Although the course material was dense, I feel much more confident and comfortable knowing that I've been provided a breadth of information and knowledge to establish a foundation to build on using Python and Jupyter notebook along with NumPy, Pandas, and Matpotlib packages, and understanding what subjects and topics in math (Linear Algebra, Calculus, Probability, and Statistics) are common to data science. A nice perk of the online course is that the lecture sessions were recorded, and could be reviewed at my own convenience if I were unable to attend lecture or needed to review material again.
Overall, this was an exceptional course that has provided me with more insight into understanding what baseline subjects I need to understand to begin working towards my goal of starting a data science career.
I think I may be of a slightly different variety than many students and attendees.
I am an adult undergraduate student in my Junior year of an applied mathematics degree. I attended my course as part of an independent study contract with my university.
I attended the 6-week Math and Python for Data Science program. My instructors were Gordon Dri and Roberto Reif.
In my experience, Gordon and Roberto as well as other support staff were extremely attentive, professional, courteous, and warm. Plus, in class, there was some humor to each lesson.
My class was online via video. It is a one-way video, so only the teachers can be seen by the students. This was super convenient. We could communicate via chat (which worked very well), and I could work from anywhere at all and still receive one on one help when necessary.
The iPython notebooks that are provided by Metis can be printed to create your own personal reference manual and can be customized and expanded.
I learned quite a bit more than anticipated, though I was thoroughly familiar with all of the mathematics that were covered.
I’m grateful for the experience. This stuff is going to be invaluable for research and lab work.
I would like to have seen more/deeper mathematics covered using python, but this is a relatively short beginners course with a specific application, and I’m leaving with more than enough tools to do it myself (which is the best way for me to really learn).
Doing Metis was one of the hardest decisions I ever made. The time/financial commitment is tough to swallow for anyone! That said, it’s one of the best decisions I ever made, as Metis paid off in every way you could imagine.
Several peers have captured the benefits of Metis here, so I’ll try to avoid repeating them and focus on what stood out to me personally …
The obvious doubt about a bootcamp was, “Why pay thousands of dollars?? I can learn all this online.” But Metis provided so many intangibles - career support, alumni network, mentorship from instructors, friendship from diverse peers - that you can’t get from an online course. Not to mention improving your chances to get a job. Prior to Metis, I tried to transition from academia to industry, but with zero success; I couldn’t get a single tech company to even consider me. Within 2 months after finishing Metis, though, I received multiple job offers and accepted one with a company that I’m thrilled to be at.
The project-based curriculum, with the guidance of experienced instructors, is the ideal way to learn. Aside from giving you a strong portfolio of material to use on the job market, the projects teach you to learn and apply new material quickly. In my first job after Metis, I was much more confident learning new tools because Metis had prepared me for the fast-paced learning you need to be a data scientist.
I can’t emphasize enough how committed the Metis instructors and staff are to students. They go above and beyond to not only teach you data science but provide mentorship, career support, and emotional support. The bootcamp and subsequent job search can be an emotional roller-coaster, so their support is instrumental.
Finally, one of my favorite things about Metis is that they don’t sugarcoat the challenges of the bootcamp or make grandiose, inflated claims about what the outcome will be. Getting a job in data science is very, very hard. Metis acknowledges it’s hard, doesn’t make any wild promises, but gives you every resource to succeed and remains committed to their alumni long after you graduate. They treat you like you’re part of their family.
My biggest piece of advice - think carefully about how you need to personalize the Metis experience to suit your needs. Everybody comes in with different backgrounds and life circumstances. There’s no single formula that works for everyone; when making decisions like picking projects, deciding when to start your job search, etc., you need to consider that what’s best for you. Whatever you choose, Metis staff are going to be there to help you through it.
About a year ago, I was in your position. I was reading reviews about data science bootcamps and weighing my options. At the time, I was a full-time lawyer (no formal STEM background), watching MOOCs in the evenings and teaching myself python on the weekends. I attended Metis in Chicago during the spring of 2017. Within a month of graduating, I was recruited by a growing startup. Since then, I have been prototyping new tools, working with engineers, and enjoying the tech industry. This transition would have been impossible without Metis, its encouraging staff, its supportive instructors, and the genuine connections I made through Metis.
Having worked in my new profession for the past 6 months, I have a different perspective and new appreciation for my experience at Metis. The curriculum is rigorous, but well-structured and current. Our dedicated instructors, senior data scientists Zach & Seth, updated our lessons to tailor them to the needs of our cohort. With this foundation, I have had no problems learning other algorithms and tackling new projects at work.
Not only did Metis teach me to think like a data scientist, but more importantly, it also taught me to be a data scientist. Specifically, my transition from the bootcamp to my job was almost seamless. The bootcamp is full time 9 to 6, emulating the work day of a data scientist. We did standups, pair problems, completed projects in short deadlines, and presented our findings. Although, I wish we had dedicated more time to unit-testing and other “best practices” for collaboration with engineers, the instructors appropriately incorporated practices inspired by their own experiences and those common in the industry.
While the instructors well-prepared me for my new career, it’s the dedicated staff, program manager Nathan and career advisor Ashley, who got me this career. Metis regularly held events, hosted speakers, and organized the all-important Career Day to expose the cohort to all of Metis’ corporate and employer contacts. (I met my boss at an open house!) All the career workshops, including the essential mock interviews, were so well organized and thorough I was in fighting shape to apply, interview, and negotiate for my job.
Metis is the “marathon” of career transitions—intense, exhausting, longer than I thought I could handle, but when it’s over, so rewarding. After a quick but demanding 3 months, I left Metis with friends, a taste for junk food (so much free pizza & cake), mentors, an impressive portfolio of 5 projects, skills that I will be able to leverage for the rest of my life, and entrance into the most exciting industry.
I was part of the first cohort on the Seattle campus and came from a neuroscience background with bench science experience. To echo other reviews here, Do The Prework and do not procrastinate on that! My programming skills weren't as strong as the rest of my cohort and I likely spent more time on the prework as well as weekly HWs. The curriculum is standardized across campuses and the instructors prepare quite a bit to bring the material to life. I didn't even mind when we ran into technical difficulties when code failed or during installation days because it's a peek into real-life debugging a stack trace and troubleshooting.
With the bootcamp being project-focused, you are allowed to be as creative as you want. You will learn to fail fast and to love (or accept) the MVP. Between lectures, HW, and projects, this is a very intense, immersive program with regard to your time, and mental and emotional energy. During these 12 weeks, I'd say I spent an average of 12 hours a day on activities relating to the bootcamp, which includes attending Metis-sponsored speaker talks and regional networking events, so the access to resources is awesome. And in the end, it does make the payoff feel incredible.
My classmates were and are a valuable source of support. The staff is responsive and quite supportive and it's not that campus staff only interacts with their "home campus"; it's nice that you can learn from different career advisors and instructors if necessary. The alumni network is also a great source to bounce off ideas, learn about resources, keep up to date on job postings, and be silly!
What I also really appreciated about my Metis experience is that they help you not only learn new skills and how to apply them, but they helped me (re)discover skills that I enjoy and am good at, helping me polish those skills.
I am giving the curriculum less than 5 stars is that while there was great coverage to ML, there wasn't much time devoted to (advanced) SQL. A lot of data will already be housed in databases (at big, established companies) and relies on accessing it, rather than scraping it from outside sources. Talking to the next cohort, I think they remedied that, so 5 stars to listening to feedback!
Before: I had a bachelors in engineering and a law degree. I had worked in a number of different fields and didn't find a job that was fulfilling. I wanted to work on machine learning and NLP, and a bootcamp seemed like the fastest way to get there. From my research Metis seemed to be at exactly the right level for me. I didn't have an applied math Phd, but had a good amount of technical experience and math abilities that I wanted to take further.
Application: I had plenty of background in calculus, stats, and linear algebra, and some python experience. The application was challenging and multi-faceted, requiring math, coding skills, and some product sense/presentation skills as well. Looking back, it was important to have a competitive admissions process, as it allowed us to establish a baseline level of knowledge and hit the ground running on the first day.
Prework: Definitely do the prework. Unless you have been working as a data scientist before the bootcamp (unlikely if you are in the bootcamp), you will need to do the prework. I used it as a reference during the bootcamp as well. The more deeply you get the prework, the more time you can spend during the bootcamp on your project and more complex concepts.
Bootcamp: Full-time for three months. Days start with a coding challenge, followed by lectures, followed by project time. 5 projects in the bootcamp and a presentation to go along with each. Blogs were also encouraged, but not required for each of the projects. Classmates were diverse, thoughtful and supportive - in the 14 students we had, we had students straight out of school, and Phds who had worked in research for several years. The instructors were knowledgeable and made themselves available after class hours to answer any questions. There is definitely a lot to learn, but I found that there was a good balance between the theoretical, the practical, and practicing communicating using data with a business person/colleague/potential employer. Beyond the assigned work, nobody will be pushing you to go above and beyond, though, so you definitely get more out of it the more you put in.
Career support: Great career support that led to lots of positive effects (more interviews, more positive feedback from employers) as I am going through the job search process. The alumni network is very helpful in the job search as well.
Job: It's only been a few weeks since the bootcamp finished, but I have a good number of leads at companies that I would have been ecstatic to work at before the bootcamp started. When I interviewed, I felt technically prepared even when it was with teams staffed entirely of Phds. I'm confident I'll find a fit in a position I'll be very happy with. I don't think it's reasonable to expect any three month bootcamp to be a silver bullet that will get you a job at Google, but Metis definitely made a huge difference in focusing me towards an exciting data science career.
Before: Psychology major with eclectic work experience for the six years since I'd graduated. Decided I didn't have enough concrete skills/experience to land a job that would be challenging and interesting enough, and I didn't trust myself to learn on my own. Decided on a bootcamp because of the structure and network it would provide, and considered data science because it seemed more niche than coding.
Application: I wasn't set on attending Metis, but when the application was harder than I expected, I was determined to get in. With virtually zero coding experience, I had to learn everything on the spot, and honestly that application was the most empowering weekend of my life. And I got in. Whoo!
Prework: I worked right up until the bootcamp started, so it was challenging to finish up there while completing the prework (I enrolled three weeks before the start date). However, the application plus the prework meant that I was prepared for the bootcamp. Would have been really hard to do the bootcamp without the prework.
Bootcamp: Full-time for three months. My boyfriend was annoyed that I was always working on projects, and I probably would have done better on my projects if I'd been single, but alas. Classmates were very supportive (I didn't experience any competitiveness between people). My cohort was unusually small (19 people), which was nice, and the small size of Metis overall meant it felt very personal, but the WeWork environment provided more people to chat with, ping pong, and coffee. Class material was great, and A LOT. Every day we "learned" something that I could have spent at least a week playing around with. There's really too much to learn, and the hardest part of the bootcamp was figuring out how to prioritize between projects, challenges, reviewing lectures, doing the extra readings, and experimenting on my own. The projects were challenging but essential for learning and also provided good presentation experience. Although both of my instructors were very knowledgeable and eager to help, the quality of instruction varied; I think Metis needs to spend more time preparing instructors (because intelligent people don't always teach well, especially not on such a wide variety of tools). Daily pair coding was awesome for the learning and the social aspect, and occasional cohort+staff activities were nice too. Definitely not an easy program! But like most things, you get what you put in, and of course YMMV.
Career support: Awesome career support throughout the bootcamp and beyond. Headshots, resume help, mock technical and non-technical interviews, speakers from the field, etc. Luckily I got a job pretty quickly and didn't need that much help after the bootcamp, because I think supporting the current cohort and the one that just graduated is too much for one person and she was sometimes hard to connect with once the new cohort started.
Job: I got a job! It's a data/backend engineer for an advertising/marketing agency. I don't use any data science per se, but I LOVE my job and I definitely could not have gotten it without the coding I learned at Metis and the opportunity of Metis Career Day, where I met my current manager.
Feel free to call/text me to hear more! 917-583-5942
Before attending Metis, I was a postdoctoral researcher in chemistry who was not feeling academic research anymore. I took some online classes in data science and thought it seemed like a cool and interesting field, but online courses can only take you so far. Also, I lacked enough connections to actual data scientists to really get a job in the field. I got into and applied to several different boot camps to help me bridge this gap, but I really appreciated that Metis did not try to oversell me on my prospects afterwards. Some of the other boot camps could really learn from this no nonsense approach.
Metis helped me bridge that gap between academia and the tech industry through both the data science cirriculum and career workshops. The cirriculum starts out much more lecture focused, but quickly turns to applying that knowledge to actual projects. Lectures include both descriptions of the math behind various algorithms and programming demonstrations showing practical programming/computing skills like web scraping, SQL, and using AWS. Career-wise, weekly workshops helped you to get started on improving your resume, LinkedIn profile, and networking strategies. I felt it was extremely focused and helpful, if a bit intense! (they don't call it a boot camp for no reason.)
By the end of the program, you have a portfolio of projects covering the major areas of data science (Supervised Learning: regression and classification, Unsupervised Learning/Natural Language Processing) and your final passion project. These let you start networking with other data scientists in the field and give you something to talk about!
The other people in my cohort were also wonderful. It was a pleasure to interact with people of such diverse professional and personal backgrounds. I felt like I not only met a bunch of professional contacts but also some great new friends.
The boot camp just ended last week, but I have met so many new contacts that I don't think it will be too long to find the right position. The boot camp also offers indefnite career support and lets you return to the office as much as you need. Even a lot of the alumni who already found jobs come back on a regular basis and offer career/interview advice if you ask.
The only thing I think could be better about the curriculum is that there could have been more feedback about projects/challenges. It seemed like it came a bit late or inconsistently. Also, we went over the basics of Big Data tools like Hadoop/Spark, but it felt a little out of place since we never used it.
The one last thing I would add is that this is called a boot camp for a reason. Be sure that you are mentally and emotionally prepared, because while it was a wonderful experience, it was a very intense one. I'm glad that my cohort was such an amazing, positive, and passionate group of people to help me (and everyone else) get through this experience.
Prior to attending Metis I was a data analyst for a market research company. At the time, I was ready to leave my company for a new position, and so I conducted a search for new analyst or data scientist roles. I had trouble finding a good opportunity, and I found that I wasn't competitive for the data scientist positions to which I applied.
Metis helped me change that. After 3 months of bootcamp and a couple more of job searching, I got a GREAT data scientist position.
Metis is valuable because you learn a lot, and you apply that knowledge to concrete projects that show organizations what you can do. You should end the bootcamp with presentations, github repositories, and blog posts that form a portfolio to help you get hired as a data scientist.
The Metis network of alumni, staff, and companies that often hire Metis grads is also indispensable in hooking you into the data science scene and making opportunities known to you.
To comment on the day-to-day aspects of the bootcamp, the curriculum is full of useful material, and each morning's pair programming assignments really complement the material that we learn in the lectures. My one complaint here is that some of the algorithms and code examples were not explained very clearly in the lectures; I think specific lectures could have been a lot better. That said, lectures are where the learning starts, but then you continue by using the material and asking follow-up questions, so there's always opportunity to understand what you didn't before.
The instructors were great. They're very knowledgeable and always willing to help. During my job search, one of the instructors volunteered to help me prepare for a presentation that I had to give in an interview. Another instructor agreed to drill me on algorithm questions for an hour in preparation for another interview. I'm super grateful for their help.
Lastly, the individual career coaching was incredibly helpful. It's just great to have an experienced person giving you advice and helping you practice for interviews and whatnot. My only gripe here is that the career coach seems like she's got a few too many balls in the air at one time, so I had to be more aggressive about following up on certain things. On the upside, Metis seems to be very interested in feedback and making improvements, so I expect that issues will be ameliorated over time.
All in all, Metis was well worth the cost. I was able to make the career leap into data science that I wanted to make, as well as meet a great group of friends and colleagues!
I finished the Data Science Bootcamp in April of 2017 and it was a great experience! Making a career change is difficult and Metis did an excellent job of preparing me to enter the field. The technical skills and network I built have been invaluable and certainly worth the time and effort the program demands.
If you're interested in reading a more in-depth review, check out my blog post.
Core data science (and I mean rigorous statistical analysis and predictive modeling), felt so out of reach before Metis. There's so many resources for learning Python and SQL out there (Udacity and Mode Analytics being my favs, respectively) - that I wanted material and curriculum from a bootcamp that matched the cost of attendance. In 3 months, Metis made it worth my while - enabling my growth from pivot table repots in excel and simple sql queries all the way to Natural Language Processing on unstructured text and Deep Learning on Cloud Deployments.
The bootcamp option isn't for the faint of heart - and my personal thirst for challenge and rigor was satisfied. The immersive experience was so valuable to me for several reasons:
(a) The "guided tour" experience. There's too many resources for learning data science, and it's helpful to have experienced professionals vet the resources for you. Part of being a good data scientist is knowing what you can down prioritize for learning later. By immersing myself in the best practices and skills that Metis instructors have determined most useful on the job, I generated plenty of leads and had secured an industry role within 3 months of graduating the bootcamp.
(b) Legitimized work experience. It's credible to potential employers to see accredited Metis experience on my resume, with work references from my instructors and cohort members to further validate my experience to potential employers. Whenever a potential employer asked me to list references for someone I worked with, I had a list of capable data scientists that could vouch for me. This set me up for plenty of technical assessments and technical interviews.
(c) Connections to a learning network. Metis alumni are growing with every cohort! Data scientists are a minority compared to Software Engineers, and I was told early on that it wouldn't be unusual to work alone or on small teams on the job. That said, over a year later I'm still leaning on the community to keep my updated as data science tools and methods improve. Post-bootcamp, I learned so much more from my cohort and other alums from soliciting help and resource sharing. If I had done all the same projects by myself, then I wouldn't have had the breadth of exposure or network to be as successful a data scientist as I am.
I can't stress enough how worth it this experience was for me.
I completed my Metis bootcamp in December 2018 in Seattle. What an amazing 12 week experience. Every person in the 16 student group was smart, committed and caring as were the Metis staff as well. We were a diverse group in ages and backgrounds but everyone was there to get the most out of the 12 week experience. We were together in the classroom from 9am-5pm, 5 days a week. That made for an amazing energy.
We learned a lot but it's bootcamp and the information comes at you fast. This meant we were on a very steep learning curve which is pretty exciting. Be prepared to be all in for the 12 weeks to get the most out of your time at Metis and then spend time afterwards learning the stuff you missed along the way.
I attended Metis in the summer of 2018 and I could not be happier with my Metis experience. The instruction was state of the art and the staff was accessible, helpful, and supportive.
Probably the most important feature of a data science bootcamp is the quality of the instruction and Metis instructors are excellent. One of my instructors had relatively a stronger background in computer science and the other a relatively stronger background in math and they complemented each other perfectly. The classroom culture was collaborative and supportive. Learning from peers was highly encouraged and practiced daily. Every morning, we completed a pair programming exercise; this was an incredible opportunity to learn from peers who came from diverse backgrounds and had incredibly advanced and varied skillsets. Moreover, updates to the curriculum are continuously being implemented such that the curriculum stays on the cutting edge of data science.
I also found the way the curriculum was structured to be highly effective. The curriculum is project-based and covers topics from exploratory data analysis, to regression to classification to unsupervised learning and finally to a passion project of students’ choosing. Students complete a project for each of these units. Metis does not provide the data sources or provide students with a pre-packaged template for the projects. Students have to go out and scrape their own data or otherwise obtain data from publicly available sources and determine (with guidance from instructors of course) what direction they want to take their projects. Students’ diverse backgrounds and interests are then reflected in the choice of projects; in my cohort alone, student projects ranged from predicting the stock market (of course), to predicting good locations for cat cafes, to natural language processing of religious texts, and predicting locations and severity of wildfires. I found this project-based approach to be highly effective. In contrast to other data science programs which are centered around only one capstone project, Metis’ curriculum allowed for students to take deep dives into each of these topics.
Career support was also extremely effective. From the careers team, I learned how to update my resume and LinkedIn profile to be consistent with industry standards and even practiced in person and technical interviews. I eventually got my first job as a result of a tip from one of the career’s staff. To me, this indicated just how well the career’s staff knows their students; they knew that this opportunity would be a good fit for me and indeed it was.
Of course, Metis is not cheap but none of these bootcamps are. When I made the decision to attend Metis, I knew that I was making an investment in my future and at the time, I predicted (because hey I am a scientist and scientists like to make predictions) that I would have a strong return on that investment. In the few months that it has been since I graduated I have already had a return on that investment and I know that return will continue long into the future.
Lastly, while Metis was definitely an intense and challenging experience, it was also a fun one. It was extremely stimulating to be surrounded everyday by such knowledgeable instructors and peers with such incredibly diverse skillsets. There were late nights and work on the weekends to be sure, but I also found the work to be manageable. During Metis, I remember telling myself not to let perfect be the enemy of good and I think that advice served me well. The skills that I think are critical for students to succeed at Metis are curiosity, a willingness to try something and fail on an initial attempt, a willingness to iterate, and openness to collaboration. If these qualities resonate with you, I would strongly encourage you to check Metis out. It was an amazing experience and I am so glad that I made the choice to do it.
I can't say enough about the curriculum, staff, and community. I completed my course in Fall 2017, I got a job within 2 months. and have continued to benefit from the community as well as the course resources. My background is in psych research in academia. I got my PhD in Psych/Neuroscience and was facing a bleak job market so I made the decision to transition to data science. It was a rough transition. The bootcamp is not for the faint of heart, but my cohort was comprised of really bright and supportive students (many PhDs and graduates with genuine technical and mathematical prowess) who kept me motivated and on-track. My instructors were amazing and the administrators were incredibly hard working, organized and caring. The curriculum was comprehensive (i.e., a lot so be prepared to be overwhelmed). Regardless of how smart/hardworking you are, you will struggle to keep up. BUT if your experience is anything like mine, you'll be surrounded by people who genuinely care about your success. What's impressed me the most is that a year into finding a well-paying tech job, I'm still very much a part of- and benefit from- the Metis community. I STILL use notes from my session. and my cohort still supports each other's growth and success through referrals and knowledge-sharing. The administrators are still available a year plus into graduating for advice and help because they genuinely care about their students. If I could give this place a 6/5 I would. I really loved my time there and I have a ton of respect for the Metis family.
I two took Metis' online courses:
1. Python/Math for Data Science
2. Intro to Data Science
I thought both were very good and definitely worth the money. I was a complete programming beginner but had done some pre-work using Zed Shaw's "Learn Python the Hard Way" and I had taken one intro Python/Data Science class at Simplilearn (very good too).
Class #1 above was the first Metis class I took. Without my preparation I would have been lost, but the prep I did allowed me to keep up with the programming part of the class. The math I fortunately already knew, but it would have been confusing had I not known it. [If you don't know it, then use the class as a menu for what you need to know eventually]. Interaction with the instructor took place via chat window, but he was responsive to Q&A. The course materials (Jupyter Notebooks) were very through and detailed & I've reviewed and re-reviewed afterward and continue to get value from them.
Class #2 built on Class #1 but got more into techniques of statistical modelling. In this class, we spoke with the instructor (not just chat box), which was very very valuable given the subject matter. I thought the course material (again Jupyter Notebooks) was well structured and clearly showed how to run the models using Python code. The instructor was fantastic, even agreeing to have "office hours" before class and hanging out after class for extra Q&A. Way beyond the call of duty, but it replicated a bit the university environment.
In general, I'd highly recommend either course (and I paid myself, no corporate reimbursement). If -- like me -- you are a total beginner, then do some learning on your own before enrolling. It will be very hard to keep up if you go in cold. They cover a lot of material and go fast. But if you're willing to really try and work hard to learn, I bet you'll find good value and a lot of insight to help you on your path. I did.
* Content: It´s an injection of information straight to your brain as expected. You get to see all of the concepts on Data Science and the stats/math underneath, so be sure to make the prework, and during the bootcamp read more or look for videos on any concept you don't understand very well. There's a lot of self learning needed: instructors will be there to explain things to you, and you can ask for review sessions on some concepts. But it's practicing, researching and discussing with your peers that you'll get everything well understood
* Assignments: The most important part are the Projects (every 2/3 weeks) you'll feel you don't have time for anything, but you'll be amazed on how much you learn and get done in a little time, and that's the point. There are 5 projects: the fist of them given to you, very fast (to deliver in just a week), in group with some of your partners, to get the idea of the bootcamp methodology. The next 3 will be individually and chosen by you, each of them to practice a particular topic, methodology or algorithm (Regression, Classification, Supervised/Unsupervised learning, NLP, Neural Networks...). The last one, your "passion project", also individually and chosen by you, can be almost anything you want with what you learn. So the projects are as hard as you push youlself.
There are also other assignments: challenges (like homework that will help you with your assignments) every week, a blog and blog posts (and blog posting culture) every now and then, and two personal investigations during the bootcamp... So you´ll think its a lot but it´s all well structured: Make them all, because all assignments will help you in your projects that are the most important part
* Instructors: Since you're learning a wide range of concepts: Statistics, Math, Programming skills, methodologies, libraries... You'll also find different instructors that will give a different focus to the things you are learning. It can be frustrating sometimes not to get a straight forward answer, but at the end you'll find out that Data Science has a lot of points of view and you have to learn to find the best fit for you, so that's what the instructors are doing by helping you find an answer and not giving it straight away. One thing they'll always help you and support a lot with is the fact that no project is wrong and you can move on with any crazy idea even if you think it's impossible, and they'll show you ways of seeing things that you'll only get by practice and experience.
* Carrer Support: You'll get very useful workshops: to fix your CV, your LinkedIn profile, your networking and interviewing skills, which is great. There's also people coming over from different companies and Data Science roles to talk about their experience and you can ask them anything! After bootcamp, people from Carrer Support are still in contact with you for help, advice or any questions until you get a job (Or even after, if you need it)
In my case, that's as far as it goes: I was an international student so other support like: visit to hiring companies, salary negotiation skills or networking with hiring partners was not very useful for me (Although I heard it was very useful for my peers), neither do they offer sponsor for a working visa, that depends on the company and I was not very interested at the time. However, back home, with what I learned on the workshops, the tips that they gave me and post bootcamp advice I found a job back home in a couple of months.
* Environment: New York's campus is in a WeWork building, there a lot of areas to study in groups or concentrate individually, it's open 24/7 and there are common areas to meet the instructors or your partners regarding bootcamp topics or chat about anything else.
* Conclusion: Bootcamp experience is great. Personally I think is a little expensive, but totally worth it. You'll get the tools you need to become a Data Scientist and the culture to keep learning and keep growing. If you're an international student, don't expect to get a job to stay in the US and change your life, but you will come back home in only 3 months, having learned a lot of stuff people take maybe a year to learn and succeed.
This review is for intro-to-DS live/online course.
I recently got interested in data science (DS), and I wanted to do a career-transition through the 12-week DS bootcamp offered by METIS. In preparation for the bootcamp, I decided to take the Intro-to-DS live/online course, and I’m glad I did! This course gave me the necessary overview and hands-on coding experience with DS and machine learning (ML) fundamentals. The exploratory analysis techniques I learned in this course helped me tackle the DS challenge given during my (bootcamp) application process.
This Intro-to-DS course is fast-paced and filled with lots of content. The curriculum is structured in such a way that, we could apply the techniques we learn at each session to our project interest and continue to build upon it throughout the course. There was a total of twelve (3-hr) live sessions where we got to learn basic concepts of DS, i.e., math (LinAlg & Stats) and python, supervised- & unsupervised learning, and pretty much everything involved in a typical model-building pipeline. By the way, twelve sessions surely won’t be enough to cover all existing ML techniques today, but at least we got to see a high-level overview of most of them. Each session was held live and online via ZOOM (where the instructor and students could share each other’s screen); these sessions were also recorded so we were able to download the videos after. Course materials (e.g., lecture notes, jupyter notebooks) were distributed via git-hub, so I could keep these notes for future reference. I wished there were more homework sets given to help solidify the concepts we learned in class, but I suppose I could just google search for various datasets and explore them myself.
During the course, we were strongly encouraged to complete a mini DS project. About half the class (including myself) selected a project, worked on it throughout the course and presented it at our last session. This was a good experience for me, as I had never worked on an end-to-end DS project before. I’ve taken a couple of self-paced MOOC’s, but the live (online) classroom setting in this course was more helpful for me, as I somehow felt accountable to keep up with the materials as the course progresses.
I’d recommend taking this course, if you have some programming experience with python and are interested in getting hands-on coding experience with ML. By the way, the tuition for this course can be applied to the bootcamp, if accepted to the program. So, it’s basically a free, pre-bootcamp (warm-up) training.
I would highly recommend this course to individuals who, like myself, come from a traditional science background. I can say without doubt that this program helped me kickstart my career and provide a level of credibility--which is important when recruiters are questioning your lack of a computer science degree. I would think of Metis as an internship, dedicated learning environment for all things data related, and an opportunity to feel part of the bigger San Francisco big data community.
Please feel free to connect with me on LinkedIn if you would like a deeper dive on the program. I don't bite!
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After working as an engineer in the water industry for 14 years, Carolina Gonzalez realized her learning had plateaued and she was ready to pivot her career. She saw the potential of getting into the data science industry in Seattle, but her Python skills weren’t strong enough to get accepted into Metis, a local data science bootcamp. So Carolina chose to take the Metis part-time, live online, Beginner Python and Math for Data Science Course, to get ready for the rigorous Metis bootcamp application. Find out if Carolina got into Metis and what her plans are for the future!
Tell me about your education and career background before Metis.
My background is a blend of chemical engineering, a master's in Natural Gas Engineering and Management, an MBA and about 14 years of work experience. Most of my work experience is in the water industry at General Electric. I worked as a mechanical engineer, a process engineer, an applications engineer, and as a proposal manager.
How did you first get interested in data science?
I took some classes in business analysis at the University of Washington, and I started to get more interested in data science. I realized that there is big potential if you can analyze data and get some insights into what the data is telling you.
I was very comfortable in my job, but I felt I had plateaued in my learning. I still have 25 years or so left in my working career, so I decided to pivot. I find it very intellectually interesting working with data and turning insights from data into actionable decisions.
Since you’re based in Seattle, did you think about continuing at the University of Washington or teaching yourself data science rather than a bootcamp?
I had to figure out whether I could learn the skills without spending a long time going back to school. The classes at the University of Washington mostly taught R. I learned a lot from those classes, but I needed more project-based experience. I also felt that to make a career transition, I needed to build a portfolio of projects and things that I could talk about with employers.
Time is money, so if I was going to make a transition, I thought “the sooner that I make the transition, the more time I have to grow into this path.”
Did you consider any data science bootcamps other than Metis?
I looked into Galvanize and Metis. I had the opportunity to meet a couple of graduates from the Metis bootcamp. After talking with people from different backgrounds who went through Metis, they all convinced me that it was definitely worth it and spoke very highly of the experience and the instruction – so I started to lean more towards Metis.
I also had a couple of conversations with the Metis admissions team to talk about specifics such as the curriculum, and metrics – like how long it takes for students to get jobs.
What was the Metis application process like for you? Were you prepared for it?
I decided I wanted to go to the full-time data science bootcamp at Metis, but I realized that I needed to learn Python for the Metis admissions process. I started learning Python by myself, but decided to accelerate the process by taking the Metis Beginner Python and Math for Data Science Course.
What did you learn in the Beginner Python and Math for Data Science course?
The class is six weeks long; the first half teaches mostly Python, and the second half is math. We started with a very basic level of Python – using the Jupyter network, then looking at how to define different types of data and properties of data, along with some of the main functions and manipulations that you can do in Python. From there, we covered visualizations and different features and how to modify those for different types of charts. We also talked about Numpy, the use of arrays and Pandas.
The math portion covered linear algebra at the beginning, then very basic calculus, and probabilities and distributions. Even though I already had a math background, the Math curriculum was still a good refresher. I also did a lot of HackerRank exercises to get more familiar with the syntaxes, the code, the different errors, and to look at examples from the class.
What was the learning experience like during the Beginner course?
We had live online lectures twice a week on Mondays and Wednesdays, three hours each, from 6:30pm to 9:30pm Eastern Time. I had a chat window where we could post questions while the lecturer was teaching – that was very helpful. Another instructor monitored that chat window.
The instructors posted the Jupyter notebook for each day’s class and all the presentations on the class GitHub account. There was also a communication channel where we could connect over video and audio with instructors, other students, and see the code for each lesson.
When we learned the Pandas section, we were given a data set of NBA games; we had to use that dataset to answer a bunch of questions.
Did you think the Beginner Python and Math course was worth the time and money?
At the time, I was working full-time and I spent a significant amount of time per week on the course. But I think it was worth it. I learned Python a lot faster, and got an understanding of basic concepts a little bit easier. Having a stronger foundation made the full-time Metis bootcamp a bit easier.
If I hadn’t taken the beginner course, it would have taken me a lot longer and I probably wouldn't have been able to do the bootcamp in summer. I would’ve had to wait until fall or winter.
What were the other students like in your course?
We started with around 22 people. Some would attend live lectures, and some would watch the recordings. We would interact mostly through the class Slack channel – we could post questions, and sometimes have a conversation through that. There were students from New York, from the Midwest, Chicago, and Seattle.
Who were your instructors?
The course was designed by Metis Senior Data Scientist, Roberto Reif. There were two instructors. The main instructor was dedicated to the beginner classes – he was very good and very entertaining. The second instructor was on the chat answering questions, and would sometimes talk during a discussion about a specific topic.
What was the application and interview process like for Metis data science bootcamp? Did the Beginner course cover the questions that get asked in the application?
The beginner course materials were very helpful for the application. Part of the application is about Python and come from HackerRank challenges, and after going through the Beginner course, it was easier to answer those challenges. But the specific admissions questions per se were not covered in the class.
The first step of the application is through the Metis website. You talk about yourself and your experience. Then you do a little coding challenge on Python. If you hear back from Metis, they send you a link to complete a test within 48 hours. The first portion consists of questions related to math and Python challenges.
For the interview, I had to talk about a proposal for a project. There was also a question about probabilities and a discussion about the project’s proposed variables.
Now that you're in the full-time data science bootcamp, how prepared are you feeling compared to the other students who haven't taken the Beginner course?
Some of my classmates have a lot of their career experience in programming, which is an advantage. But there are other students who didn’t have that experience and might have benefited by learning more Python before the class. The first week or two were really difficult, but after that, I think everybody kind of got up to speed with Python.
What's your favorite project or assignment that you've worked on so far in the bootcamp?
We are in week five and working on projects. It's interesting looking at different problems that you would like to solve and figure out if you can find the data on how to structure the problem.
Hopefully, my favorite project will be the one that I'm submitting a proposal for today. As we learn more at Metis, I have more tools available for each project, so I think each project gets significantly more interesting. My proposal involves classifying startups as to whether they are likely to be acquired or go public. There are a lot of interesting problems out there, but finding the right data set is very important, and can be challenging. I think I have access to a data set now that will help me with my project, and I could complement it with other datasets and maybe web scraping.
So far, what’s been the biggest challenge in learning data science?
I think the hardest thing is dedicating the time to it. The good thing about the Metis bootcamp is that you’re putting everything else on hold and saying, "For this 12 weeks, this is what I'm going to do." But even then, there are other life responsibilities and things to balance. The more things you can take off your plate before starting the bootcamp, the more time you can spend actually working on your projects and the material.
Is there anything specific that you are doing to make sure that you have enough time to focus on the bootcamp?
I quit my job. That was one. Also, I organized my personal life differently. For example, at this time of the year, I sometimes go camping or do things like that, and I usually walk the dog in the evening. I've put off or delegated those activities to other people. I’ve shifted my activity system to make sure that I can dedicate the time to this.
What are your goals for the future, once you graduate from Metis?
Pursuing a career in data science! The main reason I'm doing this bootcamp is to work on data science projects.
I live in Seattle, so one of my priorities is to stay in Seattle – there’s a huge tech base here. I think there are a lot of interesting problems to solve beyond physical science, chemical or mechanical engineering.
What advice do you have for other people who are considering taking a data science bootcamp – should they take a prep course?
I would highly recommend taking a beginner or prep course. It’s an opportunity to not only learn the coding language that you will be working on, but also to get a taste of the material.
I also suggest talking with people who have already completed the bootcamp. On the last day of Metis, the graduating cohort gives presentations. When I was researching bootcamps, I went to those presentations and it was helpful to see the projects they could complete by the end of the program.
Over 1500 technology bootcamp graduates entered our sweepstakes competition to win a $500 Amazon Giftcard just by leaving a review for their school on Course Report. This time, our lucky winner was Susan from data science bootcamp Metis in Seattle! We caught up with her to find out a bit about her data science bootcamp experience, why she decided to attend Metis, and how she’s now working on a team of all women!Continue Reading →
When Tiffany Li heard about the power of machine learning to automate tedious processes, she turned to Metis’ data science bootcamp in Chicago to transition careers from consulting to data science. Today, Tiffany is a Data Scientist at Grubhub in Chicago, solving a variety of business problems alongside other Metis alumni. Tiffany tells us about the Metis application process, the difference between learning at Metis versus college, and the demand for data scientists in Chicago (spoiler alert: Metis grads are working at Amazon, Facebook, Airbnb, BuzzFeed, and Spotify)!
Can you tell me about your career and education background, and how that led to you to study data science at Metis?
I graduated from Northwestern University in 2015 with a double major in Economics and Math, but I didn’t take many coding classes. After graduating, I worked at Deloitte in Chicago doing consulting work with clients from different industries – I did qualitative research and some financial analysis work with Excel. That was all very interesting and I got good exposure to the business side of different industries, but I wanted to advance my technical skills.
I started researching data science and I thought it was fascinating that so many companies were using machine learning to automate processes and make better decisions. I wanted to learn to code, and to learn about machine learning so that I could get to the next level and use more advanced tools like Python in my job. That's how I discovered Metis.
Since you already had a background in math and analysis, did you consider teaching yourself?
Yeah, I actually started by taking online courses. I checked out Coursera and completed a Nanodegree on Udacity. I started learning the foundations of Python on my own, but I needed more instruction and motivation. Learning on my own was hard because every time I ran into an issue, I had to spend a lot of time researching online, which slowed me down and made me less motivated. I wanted an option that was more intense and would provide more support so I didn't have to waste so much time Googling.
Why was Metis the right bootcamp for you over college or other bootcamps?
After checking out those online courses, I did a seven-day bootcamp in Chicago called Data Science Dojo. That was helpful to learn some basic machine learning ideas, but wasn't long enough for me to get my hands dirty. I wanted a longer, more intense program.
I thought about getting a master’s degree, but I felt like the whole process of taking the GMAT, applying, and going to classes would take too long and be too expensive. The three-month bootcamp option was the perfect combination – with both the support I needed, and a short enough length that I didn't have to spend years studying to get into the data science field.
I came across Metis in a blog post about different types of data science bootcamps, and I noticed a lot of them required PhDs. Out of the ones that I was eligible for (without a PhD), Metis was one of the best, based on reviews and their alumni placement. I was originally planning on going to Metis in San Francisco or in New York, but when I heard that Metis was going to open a Chicago campus, I was really excited that I could do Metis without having to move. I applied and I enrolled in the first cohort in Chicago.
What was the application and interview process like for Metis?
For Metis, you need to have a sound background in Python, math, and a basic understanding of data science, which is great because it means the instructors don't have to spend much time teaching the foundations.
The application process is actually three stages:
- The online application where you tell them about your background and your coding and statistics experience. They also might ask short-essay style questions about statistics concepts.
- The technical assessment. There are Python questions, math questions about things like conditional probability, then you have to design your own data science project and illustrate how you would build it – from collecting data, building the model, to how to present the analysis to a stakeholder.
- A one-hour interview with a Metis senior data scientist. They ask you questions about your technical assessment: why you chose to do a type of modeling for your data science project, what kind of background you have, and how comfortable you are with machine learning and stats.
It was actually a pretty rigorous process – which is a good thing because it ensures that the student quality is high and that everyone has different skills they can bring to the class.
What was your cohort like? What kinds of backgrounds did the other students have?
My class was made up of people from backgrounds in finance, consulting (like me), engineering, and web development. There was a wide set of skills and everyone could help each other in different ways. For example, if I had a question about a front-end app, I would go to a friend who used to be a web developer because she has that expertise. My background in math, so I could help other students with probability problems.
We were lucky that our cohort was very diverse in terms of gender. We had nine students in total and four were female students. We also benefited from the $3,000 scholarship that Metis gives to women and other minority groups.
What was the learning experience like at Metis? Can give me an example of a typical day?
The three-month bootcamp was pretty intense. We had class Monday through Friday from 9am to 6pm. We started each day with pair programming. Then we would have a morning lecture covering things like supervised machine learning, unsupervised machine learning, probability, statistics, logistic regression, and neural networks. The mornings were full of new information that you had to absorb quickly.
In the afternoons, a student might present a data science-related topic that they researched, or we might get to work on our individual projects. We worked on five projects during the three months – one group project and four individual projects. We got to choose topics that we were interested in, gather data, do analysis, and present to each other. The projects were really helpful because we could immediately apply the knowledge we learned and that helped us to reinforce our learning.
How did the learning experience at Metis compare to when you were studying at college?
It’s a different learning experience. In college, the classes often focus more on theory. For example, you might learn about a great mathematician who developed a theory and how to prove it, but not about how the theory applies to real life.
Whereas Metis is very practical. They do a great job of customizing the curriculum so that whatever you learned in the bootcamp can be immediately applied to your job. Metis would change their curriculum based on students’ feedback, and invite speakers who work in data science to talk to us about what kind of tools they use. You can apply whatever you learn at Metis, not only to a job but also to job interviews.
How did Metis prepare you for those job interviews?
Each cohort has a full-time career advisor. Our career advisor in Chicago – Ashley – was really great. She did workshops about how to polish our LinkedIn profile, how to network, and how to negotiate salary. She also brought speakers from the industry, which could potentially lead to an interview. After you graduate, the career advisors help you navigate the recruitment process, help you with mock interviews, and get you connected to employers. That's really important.
At the end of the bootcamp, Metis holds a career day where students present their final projects. They invite employers who have data science job openings, to see the presentations and talk to the students afterward. A lot of times that would result in interviews immediately after the bootcamp, which was really great.
The other huge advantage of Metis is the alumni network. A lot of alumni are working at great places like Facebook, Airbnb, BuzzFeed, and Spotify. In Chicago, a lot of Metis graduates are working at GrubHub, which is how I found my current job. A Metis grad (who studied in NYC) saw my presentation and he knew that his team at Grubhub had an opening. He referred me and got me into the pipeline quickly.
Did you see a big demand for data scientists in Chicago while you were job hunting?
There is a lot of demand in Chicago. It's definitely not as huge as New York and San Francisco, but there's also a smaller supply of candidates. When Metis first opened in Chicago, there were a lot of companies very eager to hire data scientists. There are a lot of opportunities, but it can be tricky because many companies want to hire someone who has a little more experience. I was lucky to find this opening and get the job at GrubHub.
From our cohort, my classmates are now working at huge companies, but also at startups and medium-size companies. People got jobs at Amazon, at trading firms doing quantitative research, or at computer vision data science companies. Others focus more on the data visualization side of data science or are working as Cloud engineers – everyone ended up working in really good roles that they're excited about.
Can you tell us more about your role at GrubHub? What’s it like to be a data scientist??
Right now I’m on a team with two other data scientists. GrubHub doesn't have a centralized data science team so the data scientists are placed across different departments. For example, we have data scientists in customer retention, operations, and in research groups where they are focused on longer-term research projects. The good thing about that structure is that if any team has questions, they always have a data scientist on hand whom they can go to.
I work on a variety of different projects, but I spend a lot of time with our product team. If the product managers want to test out a new feature on our website or in an app, I help them figure out which metric to measure, then we monitor the results over time with A/B testing. Or a product manager might want to know why their new feature failed, so I can look into the data to help them figure out where people are dropping off, and what to do next.
The other part of my role is working on research projects. For example, figuring out the impact of weather on our business (GrubHub gets more orders when it's raining). I also look at the impact of changes to delivery fees or how we design our review system. There are tons of interesting projects and a lot of variety in my job.
Are you using the same technologies and languages that you learned at Metis?
At Metis, you learn Python and SQL – I use both in my job. Then there are other parts of the job that are more specific to the role. For example, we work with ETL (extract, transform, load), which is designed for automating the process. At Metis, you learn a little bit about Git, but I use Git a lot at GrubHub. In general, Metis covered most information, but you'll never be able to learn all the specific software that each company uses. As long as you have a good foundation of Python and SQL, you will be able to learn new technologies really quickly.
How did Grubhub support and train you when you first joined?
The training process is very hands on. I spent a lot of time working with other data scientists to understand our legacy code. We also had meetings with data scientists from other departments to know what they're working on and to see if there's any overlap in our work that we can collaborate on. GrubHub also lets us attend data science conferences and supports us in meeting people outside of the company to learn what's new in the industry.
Since you graduated from Metis and joined GrubHub, how do you feel you've grown as a data scientist?
The biggest thing I’ve gained is understanding the business. In addition to having technical skills, data scientists need to understand a business to know how to apply those skills. For example, the data might be saying one thing, but if you have some business context you’ll realize the data could be indicating a larger problem.
I also have a much better understanding of how other technology companies are utilizing data science and the different parts of data. As well as machine learning, I’m also seeing the importance of data engineering, data visualization, and how you present your analysis and convince your stakeholders. Business knowledge is super relevant for data scientists.
How useful has your background in math and working at Deloitte been in your new career as a data scientist?
There are a lot of small things I took from my past career such as how to draft an email, and effectively communicate ideas, and how to manage your time and organize all your tasks. In math, that's really important because we need to be very rigorous in our analysis. We need to be careful about the pitfalls that we might fall into if we only look at the surface. We always need to validate our ideas and make sure that we can back up our analysis.
How have you been able to stay involved with Metis?
I try to go to alumni events and reunions and Metis meetups in Chicago. I also like to attend other cohorts’ career days, not only as a grad, but sometimes as an employer. It's good to go back and check out the newest tech out there – I always learn something new from the new Metis grads.
Since I started at GrubHub, we have hired a couple more Metis grads – one is working on the research team in our Chicago office, and the other is working on my team, but from New York. We are always bringing more fresh Metis grads into the company.
For people in Chicago thinking about going to data science bootcamp, what’s your advice?
First, it's really important to research the bootcamp. After doing a lot of research, Metis came across as the best one because they care a lot about the students and they put in a lot of effort and support. I recommend going to a website like Course Report to read students reviews.
Go to bootcamp info sessions to get an overview of what the bootcamp teaches. Sometimes bootcamps will host events like "One Day at Bootcamp" where you can get a feel for whether you’re interested in data science, and if you can handle the intensity of the bootcamp. So go to a lot of meetups, talk to instructors, alumni, and current students. That's important before committing yourself to a three-month bootcamp – it's not for everyone.
Is there anything else that you want to add about Metis?
The Metis community is really great. The staff and instructors are always supportive. You have two full-time instructors, one full-time career adviser, and one full-time program manager. So you get a lot of undivided attention from the staff.
Another great thing about Metis is they're very open to feedback. For example, they invite alumni back to hear about what we're working on and they ask us if there are any technologies Metis should add to the curriculum. Right after we make suggestions, they take action and change the curriculum accordingly. Seeing feedback get implemented is really great.
What should you expect in a data science job interview and how can you prepare for one? We asked a Senior Career Advisor at Metis Data Science Bootcamp, Andrew Savage, to give us some insight into what candidates will experience when they apply to data science jobs. Find out examples of interview questions you might get asked (and what you should do if you don’t know the answer), the soft skills interviewers are looking for, and see Andrew’s favorite online resources to help you ace the data science job interview. Watch the video or read the blog post!Continue Reading →
Jamie Fradkin has worked at BuzzFeed as a data scientist for over a year and a half after attending Metis data science bootcamp in New York City. As a former biomedical engineer, she wanted to switch careers so that she could make use of her math and statistics skills in a booming new industry. Learn about the Metis application and learning process, Jamie’s experience getting hired and changing roles at BuzzFeed, and her tips on building confidence in your own skills.
What's your career and education background? How did your path lead to a data science bootcamp?
I went to Johns Hopkins and studied biomedical engineering and applied math and statistics. My first job out of college was at a medical device company where I was working in research & development making surgical tools, doing lots of mechanical work and quality testing. I was really passionate about that industry and felt it was something that would be a great fit for me, but I wasn't making use of the statistical or math foundation that I had built up in college.
I heard about data science being a booming new career that had more demand for my skills and interests in a variety of industries and locations. After about a year working in the medical device industry, I decided to make the change so that I could best leverage my education, and have more opportunities and options for my career.
I felt a bootcamp was a super smart and efficient way to go about leveling up my data science education as opposed to going back to school for a formal master's degree. In my research, I found that Metis had a really comprehensive curriculum with experienced instructors so I thought that would be a good fit for me.
Did you consider going to a bootcamp for any other digital skills? What attracted you to learn data science?
In a lot of technical fields, you can feel a bit out of touch with the actual strategy behind decision making. You provide reports and are the go-to person for technical questions but you aren't really in the loop on how that's affecting business operations. Data science is a great bridge between those hard quantitative skills and soft skills. It allows you to work with a lot of different kinds of people – you’re able to communicate your ideas to a non-technical audience for them to gain the most insights possible from their data. Data science is a really cool bridge between the two worlds – it’s a unique job.
How did you find out about Metis? What stood out about the bootcamp and did you consider any others?
My dad sent me an article about how college graduates with humanities degrees were going to coding bootcamps in order to develop technical skills that would make them more appealing in the job market. Even though I had two varying technical degrees, I didn't know anything about machine learning or visualization. I learned about Metis from my dad and it came at a great time.
I applied to Metis and Galvanize, but it came down to location because I wanted to be in New York. I read Course Report quite a bit before I started and it seemed like a lot of people had phenomenal experiences so I was sold.
How was the Metis application and interview process for you?
Metis changed the admissions process a bit, but when I did it it was really open-ended. We had to craft a data science project and articulate to the interviewer how we would execute it. And of course there was a Python and SQL test. I liked how they tried to determine your curiosity, creativity, and grit, which is Metis' tagline. The process tested what kind of data scientist you would be if admitted. I'm not sure exactly how competitive it was, but I thought the application was pretty fair.
How was your Metis learning experience? Did the teaching style match your learning style?
I went to Metis in January of 2016 and I started my new position at BuzzFeed in May of 2016. Metis is 12 weeks so literally, every day counts. You can't slack off and you can't miss a beat. The structure of every day was perfect for what I had envisioned – you have a morning lecture and then the entire afternoon is time to work on assignments, projects, and ask your teachers questions.
Every morning there was a lot to digest, but you could always ask your peers and instructors for help. It wasn't a typical college setting where you get a lecture and then they hope you understand it by Googling it. I felt there were endless opportunities to make sure we fully understood the topics we covered.
I loved how collaborative the experience was. In a bootcamp, you really bond with your peers. Everyone is having a tough time because everyone comes in with different knowledge. It certainly wasn't competitive during the course because we all wanted to make sure everyone was up to speed. I had a great time.
How did Metis help prepare you for the job search?
I think similar to what I described about the actual curriculum at Metis, the career support unit provided endless opportunities to ask questions. So while there's no system where a career counselor is going to literally match you with a job and get you hired, I felt I had all the resources I could ever need to accomplish that on my own. We could get advice on what to wear to an interview, how to write an email to the hiring manager, or see if the Metis alumni network had any connections in a specific company. The career counselors were your friends. Metis gave a lot of general career education to bootcamp students as a whole, but they also catered to you on an individual basis to make sure you found the best fit for you.
Do you have any advice for bootcampers who are going through the current job search?
The people who have the most success looking for jobs are the ones who have a personal reason as to why they want to work at a certain company. You can spread your resume to as many places as possible and hope something sticks, but when I made it pretty far in the interview process or even got offers, it was really because I genuinely had an interest in the company's mission, and I really felt my skills would be a good fit for them.
It's important to take time to think about the kinds of companies you want to work for and hopefully, your interest and your skills will shine through.
You’ve been with BuzzFeed for about a year and a half. How did you find the position and how was that interview process?
I found it by cold messaging a recruiter on LinkedIn, which I think I had done quite a bit back then. It actually turned out that another Metis graduate was already working at BuzzFeed – she was a phenomenal contributor to the company and had really excelled since starting there. So I think that actually helped my case quite a bit.
One or two years ago, bootcamps weren't really a well-known concept yet. The recruiter connected me to the Metis grad and there was a phone screen. I also had an in-person technical interview, another virtual technical interview, and then there was a take-home assignment, which is where I was provided a data set of lots of stats around how users engage with BuzzFeed quizzes. It was very open-ended where they only said, "tell us some insights about this data." I made a presentation that was designed for a non-technical audience, which might mean people who write quizzes or people who work on distributing it across all social media platforms. My task was to essentially take a massive data set about how users interact with quizzes and give them some strategic recommendations based on that data.
The interview process at BuzzFeed was long and challenging – around six weeks. But like I said before, I passionately loved BuzzFeed so before I started there they were able to see that. The take-home assignment was enjoyable for me.
Describe why BuzzFeed needs a data analyst or a data scientist. Could you tell us about the day-to-day of your role?
I’ve had two separate roles since working at BuzzFeed. One was more on the data analytics side, which is necessary at BuzzFeed because we have a huge team of content creators – people who write posts and make videos and quizzes etc. It's really important for there to be a feedback loop between our audience and our content creators so that we know what's truly working and connecting with our audience.
So in that respect, a data scientist is really helpful to sift through all the page views, shares, comments and other engagement benchmarks to figure out what's working for us as a brand. Another side of BuzzFeed is we have a lot of what we call owned and operated properties like a website and app. I'm now working on the BuzzFeed app as a data scientist where I provide a lot of reporting and metrics on how users are engaging with content to help inform new designs. We are content agnostic because it's not about trying to decide what content to feature in the app since that's all done automatically. In the app we can control the design, the layout, and other features that we want to have. One main area of BuzzFeed data science is content and one area is products such as the site and app. Another is distribution, which is thinking about what we put on Facebook, Twitter, Instagram, Snapchat, etc.
Are you using the same languages and tools that you learned at Metis? Did you have to learn any new technologies to work at BuzzFeed?
Language-wise it's always Python and SQL, and we use Jupyter Notebooks just like at Metis. BuzzFeed does have a couple of in-house business intelligence tools that we're allowed to use. All in all, there's a ton of overlap with Metis. The only thing different at a larger company is that you're exposed to more production-level tools like Spark and Hadoop. When you need something to be fast and efficient for millions of reviews it's got to be production-level machine learning, whereas at Metis it was only small data sets.
What was the learning curve like for you when you first started at BuzzFeed? A year and a half later, how do you feel you've grown as a data scientist?
Part of what happens when you first start at a company is needing to get that domain knowledge. BuzzFeed is part of the media news and entertainment industries, so I had to learn what metrics matter to people and how we define success here. I had to learn what information to provide to our different stakeholders to help them best do their job. I've had a lot of great opportunities to extend my technical skills, take classes, and collaborate with other fellow data scientists.
I think I've grown in the way that I communicate this information to other BuzzFeed employees. I’ve become familiar with the data that we have and how I can best serve everyone’s needs.
Does BuzzFeed do a good job of onboarding their new hires and making sure that their data scientists are continuing to learn and grow?
Around the time I joined BuzzFeed they were really interested in shifting the concept of a data hire from an analyst to a data scientist. We wanted to set that expectation of what kind of levels someone will be delivering at. It's really about providing results based analyses. The data science department has definitely grown quite a bit since I've started. We’ve hired between five or 10 new people in the past year.
Another thing that I love about data science at BuzzFeed is the opportunity to have side projects. There isn't too much restriction on what you're allowed to work on because the leadership here realizes that data science is actually a creative pursuit. So if you have a curiosity about something that wasn't directly assigned to you, there's so much room for experimentation without negative consequences. We all learn from each other and share findings all the time. I think mentorship can come in a lot of different forms and it doesn't necessarily have to be someone who has a higher education level than you or even higher experience. It's just learning from someone who has different skill sets.
In terms of your background in biomedical engineering, has any of that experience been useful in your current position as a data scientist at BuzzFeed?
Knowledge-wise there probably was not much overlap. There is a fearlessness that I associate with that time in my life – not getting knocked down by any seemingly impossible task that gets put in front of you. I learned to have persistence and essentially be able to start a problem without conceptualizing what the solution would be. That's where I'd say the two fields overlap.
What role do you think Metis has played in your success? If you didn’t attend a bootcamp like Metis would you be where you are today?
It's hard to say what would’ve happened had I not gone to Metis, but I think it really helped to have an accredited program on my application. I technically could’ve just googled and looked up all the topics that Metis covered and maybe taught myself, but they had a really phenomenal reputation. Coming from that program certainly helped my case quite a bit.
And for some reason, Metis instilled a lot of confidence in me even after a really short time studying in this field. The curriculum was so perfect because you did homework assignments and projects presentations that were similar to what you would really do in a data science role. I had the confidence to know that I could do this, I just had to find a company where I would love to do data science.
Are you still involved with Metis alumni and instructors?
Yeah. The first year or so after I graduated I went to career days and I was an alumni interviewer. I went to a lot of open house events and things like that. Every time they ask me to participate in something, be a speaker or connect with one of their students, I always say yes because I think it's really important to maintain the alumni network in this community. So I'm still relatively involved. I really feel that I owe them a lot for getting me to the level where I could have a job that I love.
What advice do you have for people thinking about making a career change and attending a data science or coding bootcamp?
I get this question a lot and it's hard because data science and data scientists are still terms that mean so many different things in different settings. My initial advice – don’t choose this career path because you think you should. Choose it because you want to and you think it would be a good fit for yourself. I'm assuming that whoever is going through this process has done enough research and knows that it's a career path that would be suitable for them.
The other component of that is to not be intimidated by data science because there are a lot of scary graphs and terms. But I really feel that in a bootcamp setting, as long as you have the willingness to work hard and to learn, you can succeed with the support that a bootcamp provides. Essentially, if you're considering applying and you think you'll enjoy it, don't be swayed and don't be intimidated – have confidence.
Need a rundown of everything that happened in the coding bootcamp industry this September? You’re in luck! We’ve collected all the most important news in this blog post and podcast. This month, we kept up with the status of the bootcamp industry, learned about how bootcamps are thriving in smaller markets, and explored different ways to pay for bootcamp. Plus, we added 7 new schools from around the world to the Course Report school directory! Read below or listen to our latest Coding Bootcamp News Roundup Podcast.Continue Reading →
Can anyone learn data science? What do you need to know before you go to data science bootcamp? Do you need a quantitative degree? We asked Metis’s Chief Data Scientist, Deborah Berebichez, how to know if you’re ready for a data science bootcamp, and how to set yourself up for success before the first day of class. Watch the video or read the summary.Continue Reading →
Need a summary of news about coding bootcamps from July 2017? Course Report has just what you need! We’ve put together the most important news and developments in this blog post and podcast. In July, we read about the closure of two major coding bootcamps, we dived into a number of new industry reports, we heard some student success stories, we read about new investments in bootcamps, and we were excited to hear about more diversity initiatives. Plus we round up all the new campuses and new coding bootcamps around the world.Continue Reading →
Haven’t had time to keep up with all the coding bootcamp news this March? Not to worry– we’ve compiled it for you in a handy blog post and podcast. This month, we read a lot about CIRR and student outcomes reporting, we heard from reporters and coding bootcamp students about getting hired after coding bootcamp, a number of schools announced exciting diversity initiatives, and we added a handful of new schools to the Course Report school directory! Read below or listen to our latest Coding Bootcamp News Roundup Podcast.Continue Reading →
Unlike many web development bootcamps, data science programs often require some background knowledge in statistics and coding. So we’re diving into the admissions process at data science bootcamp Metis, and getting insights from alumni Deepak and Emily, who explain their journeys from the Metis interview to their new jobs at Facebook and Etsy.
Amy, tell us about your role as the director of admissions at Metis.
Amy: As Director of Admissions, I'm usually the first interaction students have with Metis. I walk students through the different stages of the admissions funnel and work one-on-one to help them determine what course will be a great fit for them. Sometimes it's the immersive bootcamp; other times it's our professional development courses.
In some cases, when they're not yet ready for the immersive bootcamp, I provide resources to help them build up their backgrounds. My goal is to help students create a career path of success and help them move along the application process as seamlessly as possible.
Can you walk us through the steps of the Metis admissions process?
Amy: There are three steps of the application process.
- Submit a written application. There are open-ended questions and Python programming exercises. We ask students to have some programming experience and a statistical thinking background, and we’re able to see that in the first round of the application.
- Coding Challenge. If they move on to the next round, they are given 48 hours to complete a coding challenge. The challenge involves a technical assessment, an exploratory data analysis, and a data science project.
- Interview. Once we have those materials, we’ll set up a 30-minute interview with one of our data scientists to get to know the student more, help us determine if they are a good fit for Metis, and see if we are the right fit for the student.
It takes two to three weeks from the time you submit an application to receiving a decision.
What level of programming or statistics do you want to see in an ideal applicant?
Amy: We've intentionally left this pretty open ended. Although we don't require students to have a background in Python specifically, we want to see how you tackle something you might not be familiar with. We do expect that students come in with some programming experience and some background in statistics, and we've designed the admissions process to delve deeper into those areas. You don’t need to have worked as a web developer, but if you don’t like programming, then this is not the best profession for you. We can test these skills in the admissions process based on the questions that we ask.
What sort of backgrounds and experience in coding do Metis students have?
Amy: Students come to Metis with various backgrounds. Some have the usual computer science, economics, math, and physics backgrounds, but then we also see students with non-traditional paths to data science: finance, history, English, or psychology.
One thing our students have in common is their interest in technical skills and their statistical thinking. You may not have taken a formal statistics class, but this is something that you're very curious about, and you're able to show that your path has lead you towards this career in Data Science, even if your degree doesn't show that.
We have about 60 hours of prework to help students develop a stronger foundation in areas that they might be weaker in. This includes Python, linear algebra, and statistics.
Are presentation skills and communication skills important to being a data scientist?
Amy: Data scientists, in general, need to be strong communicators both visually and verbally, because data scientists are really the translators within their company. They're taking the insights that they find in the data and communicating them to a wide audience of folks who may not be familiar with a lot of the technical aspects but need the insights from that data to make decisions and drive value.
During the bootcamp, we focus on five different projects where students actually present their findings, because we want them to be comfortable communicating their findings in front of people. During the interview process, we have a project challenge that they actually present to our instructor.
Emily, I'm curious what you were up to before you went to Metis?
Emily: I came straight to Metis after getting a Master’s in Organizational Behavior, and before that, I got an undergraduate degree in Decision Sciences. I did have a lot of statistics experience, because I was a Statistics minor in college and I took econometrics courses in grad school. Conducting psychology research also requires a lot of statistics.
I definitely didn't have a traditional computer science background. I had been using R, which is a language many data scientists use. I had taken one class in Python, and that was four years ago.
What I really liked about Metis was that it filled in those gaps I had in Python and machine learning. I hadn’t done those more advanced techniques beyond regression.
Did you do your coding challenge in Python or in R?
Emily: I did do it in Python. I had a little bit of exposure to Python, and it is somewhat similar to R. I definitely needed some online references, but just to look up how to do an IF ELSE statement with Python syntax. I also knew that the Metis bootcamp was going to be in Python, so if I wasn't comfortable completing this coding challenge in Python, it probably wouldn't have been a good fit.
Was the 60 hours of Metis prework helpful to you?
Emily: Definitely. I was strong on statistics and weak on computer science. There were some students with opposite backgrounds. So the prework was a good refresher, but I really needed the Python, computer science, and machine learning focus.
Deepak, how about you? You had actually worked as a web developer before but you more recently working in education. How did you decide to make the switch?
Deepak: My background is in math as an undergrad, and then I started working with databases. I was working on end-to-end projects, analyzing and visualizing data, and presenting that to an audience. During that phase, I had to transition to a web developer on my own, but throughout that time I was working with a lot of data.
Even during grad school in economics, I had a lot of exposure to economic data. So I've been playing with data for a long time, and that's what motivated me to come to Metis. Although I had a lot of data analysis skills, I didn't have a lot of computer science, machine learning, or predictive modeling skills.
Could you get through the Metis take-home challenge with your Python skills?
Deepak: To be honest, I didn’t have any Python skills before I started the take-home challenge. When I spoke with Amy, she said, "You can code the take-home challenge in any language you want, but we recommend Python." So I started looking at Python. It was a steep learning curve for me, but I did my take home challenge in Python. Because I had knowledge in other programming languages, I was learning the syntax.
What did the Metis application tell you about the Metis bootcamp experience?
Deepak: The intensity of the application gives you an idea about how rigorous the bootcamp will be. I had looked into other data science bootcamps as well, and some of them seemed like they would take anyone. At Metis, the take-home challenge and the interview process was challenging. You really had to put in effort to get through it, and when I did get admitted, I felt like I deserved to be in the program.
Emily: I had a very similar feeling. Also, my brother is a data scientist; he'd spoken at Metis before and been to their career day, and had great things to say about Metis. I trusted his judgment. He told me that after 12 weeks, Metis students were giving really good, really strong presentations. That gave me a great feeling about Metis.
What stood out to you about Metis when you were looking for data Science bootcamp? Did you look at other data science bootcamps?
Emily: Looking at the landscape, there aren’t as many data science bootcamps as there are web development bootcamps, so I didn’t have to compare 20 different schools. I knew I wanted to work in New York, so that limited it further. I looked briefly at other ones, but I knew Metis was competitive. I looked at the syllabus, and Metis taught exactly what I wanted to learn: machine learning and Python. I looked at NYC Data Science Academy, but they covered some material that I already knew (they teach R as well as Python).
Deepak: In my research, start date was important, and I looked at where alumni landed jobs; it looked really strong. The key factor for me was talking with the Metis data scientists during the interview. I had a chance to ask them honest questions, and they were really honest and open in their answers.
Did either of you want to work in a specific role or at a specific company when you graduated from Metis? What was your goal?
Deepak: I was not looking at a specific role or a specific company. My goal was to gather strong data science skills, and gradually put myself into the field.
Emily: I wanted a Data Analyst or Data Scientist position. I didn't have a specific company in mind, but I had looked at previous job descriptions at a few companies I was familiar with. I found more job descriptions that required Python than R, so that pushed me towards Metis.
Once you started at Metis, what were your classmates like? Was everyone on the same level as you?
Deepak: For my class, I would say yes. Some people were stronger in programming, while other people were stronger in statistics, but overall I think we were on the same page when we started the program.
Emily: It was quite a diverse group. I had classmates who were my age, a couple of years out of college. We also had people with PhDs in statistics, someone with 20 years of marketing experience, and people with undergrad degrees in computer science. That diversity is one thing that’s really appealing to me about Metis– all these people came to Metis because they were still missing some components.
Emily, could you tell us about your favorite project that you built while you were at Metis?
Emily: My final project was my favorite project. It was sort of “meta,” because I actually did a project about data science freelancers. There's a website called Upwork.com that has lots of freelancing jobs (including data science). I used the Upwork API to gather all the data scientists’ profiles and all of the current jobs posted on the website. My final project was to make a tool that actually matches these data science freelancers to jobs, tailored to their skill set.
That project definitely evolved over time. When I started gathering this data using the API,, I definitely didn't know what the final project would be. As I iterated through it with my instructors and other students, I was trying to figure out "how can I make this as useful and impactful as possible, rather than just give summaries of this data?"
I used Python for most of my project, but I also ended up using R to make the final web application. I used a library called Shiny, which makes it easy to build an interactive dashboard.
Did you find that your final project was important during jobs interviews?
Emily: Definitely. The other great thing about Metis is you also have to write a blog, which I had been meaning to do. When I went to job interviews, I often ended up talking about my project and the challenges. I would give people the link to my application and other projects.
Deepak what was your favorite project?
Deepak: My favorite project was also my final project, because I had a lot of time to work on it, and the topic was something that I was really interested in: soccer statistics. The biggest challenge was getting the proper data and the proper amount of data. I had to reach out to different data scientists across the globe to get the data I wanted, which was interesting.
After talking with the instructors, we ended up expanding the project and creating a soccer betting application. Based on past soccer statistics, users bet on upcoming games and see over time, whether your betting strategy will make you money in the future. I absolutely loved it. The one-on-ones with instructors really helped me understand everything in more detail.
Also, we did presentation practice, which was really helpful and you could see a drastic amount of improvement. And it was all due to the comments and tips from the instructors at Metis, which are the skills that I'm going to carry forward into my job in the future.
What are you up to after graduating, Deepak?
Deepak: I am currently a Business Intelligence Engineer at Facebook. Most of my work is analyzing data, creating pipelines, getting data from different data sources, and visualizing that for our stakeholders. There's just a little bit of data science involved in my job now, but this was a great opportunity for me when it came. I couldn't refuse. I can always build on my data science skills in the future.
Deepak, do you feel like you get to use your skills learned at Metis?
Deepak: I haven't used my skills yet because I'm very new to my job, but there are other people in my team who went to data science bootcamps as well. They definitely say that they use their data science skills, so I'm pretty sure I'm going to use them in future.
Emily, tell us what you are working on and where you're working now.
Emily: I'm on the Analytics team at Etsy, working with about 20 analysts right now, and we essentially embed with other teams. I'm working with our Search team, which is really exciting because search is a huge part of the Etsy experience. Unlike most companies, Etsy’s data scientists are almost all PhDs in machine learning and computer science.
As Analysts, we design and analyze experiments and serve as the “quantitative voice” for our partner teams. Two things I’ve worked on is experimenting with Etsy’s search ranking system and work on opportunity sizing. I hadn't really had experience with big data before, so the learning curve was steep at first. The primary table I use has six billion rows. I've also started using Scala to write Hadoop jobs to get the data that's not stored in a SQL database. That's really exciting.
Amy, are you looking for applicants you think are going to able to land certain types of jobs when they graduate?
Amy: Metis is a vocational training program. Our job is to help students gain the skills they need in 12 weeks to become a data scientist. Most of those positions are entry level data scientists or a data analyst.
We’re definitely trying to identify students who will be successful in the job search after 12 weeks at Metis. 12 weeks is not a lot of time, but the whole idea is that you can adapt quickly when you get into the workforce. We’re looking for passion, communications skills, technical abilities, curiosity, and grit. All these things easily translate into success after the bootcamp.
For data science beginners, what's your advice to prepare for the Metis application?
Deepak: First, understand what data science is. Don't go into the field just because people say it's classy or you make a lot of money. Do your homework, make sure you're passionate about analyzing data and telling stories with data. If you have that passion, and you have basic computer science and statistics skills, I would definitely tell anyone to join the field. There's going to be a huge demand for data scientists in the future.
Emily: There are two sides to this answer. I could recommend specific Coursera courses or books. For example, the John Hopkins Coursera course is great.
The other important prep is to do projects. Don't just read about data science; find a question you're interested in answering with data. There are a lot of data sets that you can basically download. Start playing around with them, as that will force you to learn these skills, and hopefully you’ll enjoy it. If you don’t enjoy it, then this might not be the best path for you. Try to figure out what questions are interesting for you and practice ways of investigating – that would also be great to show in an admissions process. There was a great Quora answer about this subject.
I would be impressed with someone who says, "I don't have a formal background, but I got really interested in data so I developed these predictive modeling skills because I wanted to be able to predict this specific thing that I'm really interested in."
At the same time, don’t be intimidated by data science job descriptions that say you need a PhD in machine learning and have a ton of packages on GitHub. That's not the case. Metis is not meant to take you from 0 to 60, but if you have the curiosity and the grit, don't be afraid to try. Don't feel that just because you don't have a traditional background, you can't make it in this field.
Deepak: Metis will definitely provide you with the tools and skills required for your first data science job. Like Emily said, don't get intimidated. As long as you have those basic skills that we've mentioned and a very keen interest, Metis will provide you with all the skills required.
Amy: I definitely agree with both Deepak and Emily. Do your research, reach out to people on LinkedIn, attend meetups. If this is something you're interested in, then you need to immerse yourself in the world of data science and and really understand the different paths that are out there.
I love when students reach out to me. I'm always willing to have one-on-one conversations to talk about how to move from step A to step B and help them improve in their careers.
Secondly, just do as much coding as possible. Get familiar with Python, do data science projects. Once you get into Metis, you don't want to spend time troubleshooting code. You want to really focus on learning the data science concepts.
It’s that time again! A time to reflect on the year that is coming to an end, and a time to plan for what the New Year has in store. While it may be easy to beat yourself up about certain unmet goals, one thing is for sure: you made it through another year! And we bet you accomplished more than you think. Maybe you finished your first Codecademy class, made a 30-day Github commit streak, or maybe you even took a bootcamp prep course – so let’s cheers to that! But if learning to code is still at the top of your Resolutions List, then taking the plunge into a coding bootcamp may be the best way to officially cross it off. We’ve compiled a list of stellar schools offering full-time, part-time, and online courses with start dates at the top of the year. Five of these bootcamps even have scholarship money ready to dish out to aspiring coders like you.Continue Reading →
In our recent Student Outcomes survey, alumni reported that they were working in over 650 different companies! Of course, you may have read recent press citing companies like Google who apparently aren’t willing to invest in junior technical talent from coding bootcamps (we happen to know that coding bootcamp grads have been hired at Google and Salesforce, but that’s not the point)... Here we’re highlighting 8 forward-thinking companies who are psyched about the bootcamp alumni on their engineering teams. Each of these employers have hired multiple developers, and are seeing their investment pay off.Continue Reading →
Many competitive coding bootcamps want you to have some programming knowledge in order to be accepted into their programs – whether they’re looking for past experience on your resume or require that you pass a coding challenge. For a beginner, it can be tough to get the experience that a selective bootcamp looks for in the application process. There are many ways to learn basic coding (including teaching yourself) but if you want to make sure you’re covering the right material and quickly, then a bootcamp prep program may be for you.Continue Reading →
Welcome to the July 2016 Course Report monthly coding bootcamp news roundup! Each month we look at all the happenings from the coding bootcamp world from new bootcamps to big fundraising announcements, to interesting trends. This month the biggest trends this month are initiatives to increase the diversity in tech, some huge investments in various bootcamps, and more tech giants launching their own coding classes. Read below or listen to our latest Coding Bootcamp News Roundup Podcast!Continue Reading →
You’ve all heard about judging coding bootcamps and data science bootcamps based on their outcomes (aka does a bootcamp get students jobs), but we rarely get the perspectives of both the bootcamps and the employers at the same time. So we were really excited to have our guests, Megan Ayraud of Metis and Brennan Biddle of Capital One Labs, join us for a video interview.
Meet Your Panel:
Megan is the head of careers at Metis, a Data Science bootcamp in New York, San Francisco and now in Chicago. Megan helped develop the 12-week Careers Curriculum, which is concurrent with the bootcamp curriculum at Metis, and provides a lot of post-graduation support for Metis students.
Brennan is a Data Science and Data Engineering Recruiter at Capital One Labs in New York, which is an employer partner of Metis.
It can be really difficult to sort through job placement rates and stories about bootcamp grads who've gotten jobs after graduating. In this live Q&A, Megan and Brennan answered all of our questions about how Metis prepares future data scientists for their first jobs, and how an employer like Brennan can hire effectively from a coding bootcamp like Metis.
Megan and Brennan, can you first tell us a little bit about your roles. Megan, what does it mean to be the Head of Careers at Metis?
Megan: As the Head of Careers at Metis, I work with all of our students and graduates on helping them through the job search within the current data science market. As you mentioned, I've built a curriculum that goes alongside our data science curriculum, and it's all about developing them in their career and getting them prepared for the job search post-Metis.
There are workshops built into the 12-week bootcamp, one-on-one meetings, mock interviewing, and we bring speakers in who are data scientists in the community to help shine a light on what data science is like in their companies. I organize a career day at the end of every bootcamp cohort and we invite employers to meet our students and network. I also work with all of our students individually, along with my career advisors. Shout out to Metis’s amazing career advisors, who help find our grads amazing jobs.
Likewise, Brennan, what does it mean to be a Data Science & Data Engineering Recruiter right now?
Brennan: The fact is that the term data science means a million different things to a million different people. Even internally within Capital One, we're a big machine. Different groups in different corners of our business define data scientists differently. The hardest part is finding the right candidates that match up with specific needs of each individual group within our organization and finding where those technical strengths lie.
Brennan, are there baseline requirements needed to be a data scientist at Capital One? Do candidates need a certain type of degree?
Brennan: Really good question. I can't speak to everywhere, but here at Capital One, we want our data scientists to essentially wear a research and development hat coming into the door. Our Data Science organization is the most random, diverse, crazy, eccentric group of people you'll ever work with. Just sitting in my little pod alone, we have a Neuroscience Ph.D., a Math Ph.D., and a former high school math teacher. There's not one specific type of a person or background, either personal or professional, that we're looking for. I usually tell candidates the weirder background you come from, usually the more successful you'll be as a data scientist here at Capital One.
That actually aligns really well with bootcamps because everyone who graduates from a bootcamp has a past life or career.
Megan, how do you coach students on incorporating their past lives with their future lives as data scientists in just 12 weeks?
That imposter syndrome is a common problem and we work with our students a lot, not only in groups and workshops, but also a lot of one-on-one mock interviewing. When we’re working on the technical interview, we actually bring data scientists in and have them actually go through a real live mock interview as students can expect in the real world.
That helps build a lot of confidence in soft skills, and how to present your background and tie in your past life to your new life as a data scientist. It's amazing to see that transformation. A lot of people say, "I was a math teacher in my former life, how could I possibly tie that into data science?" As career advisors, we see that connection so clearly, but it's our job to help them develop that narrative, practice it, build confidence, and then be able to tell that story to employers.
Brennan, have you hired Metis graduates?
Brennan: We have hired a few!
What types of roles are Metis grads going into at Capital One Labs? Does Capital One have a Junior Scientist role?
Brennan: Yeah, we do. Even for a “junior” data scientist, we're looking for a really strong foundational data science background. This for us is someone that is really good a Python, has at least a little bit of exposure to working in a Hadoop framework, and someone that is interested in functional languages.
We do have junior level roles (although they don't pay like junior level roles). We anticipate hiring someone that we can grow, mold and develop. And we find that places like Metis do a really good job of building that foundation that we can build upon.
How did you meet the students that you've hired from Metis? At a career day or did you guest-lecture at Metis?
Brennan: The answer is both. Metis has had a few cohorts and we've been working with them throughout. Our team goes to the Metis classroom, introduces ourselves to the cohorts, tells them what we do, and learn about the projects they're working on. The last cohort came in for a career day to see our office space and interview and they asked a lot of questions of different data scientists on different teams. Metis has a career night that we attend every single cohort as well. At this point, we have a pretty good relationship with them at different stages in their process.
We work a lot on building brand within the bootcamp as well. Now that we're hiring, we're starting to build a bit of a reputation and by bringing people in to see our space, telling them about the projects that we do, I think that we're starting to generate a lot of interest.
Megan and Brennan, I want to talk about what goes into a technical interview for data scientists. What would I expect as a data science candidate?
Megan: I see employers do things really differently. There is a lot of overlap from coding interviews for software engineer roles to data science. I see most of our grads having to do some level of whiteboarding. They may not necessarily be coding on the whiteboard but instead walking through a solution, how you would approach it, and how you would get to an answer. At Metis, students do whiteboarding practice almost every day. They’re pair programming all the time at Metis, so they feel comfortable doing that with an employer on an interview.
We also see companies incorporating case studies into the interview process. Case studies are just a huge part of the data science world. Sometimes those are in the office, sometimes they’re take-home projects that the candidate has to be ready to present in front of an audience at the interview. Communication is such a huge part of the data science role, so candidates have to show communication skills, logic and technical skills, and also portfolio review. At Metis, students build five projects along the way so that they have this robust portfolio to show employers. I see a mix of those three things happening in data science interviews.
Brennan, of those three things: communication skills, technical skills, and portfolio, which stood out the most about the Metis students you have hired?
Brennan: I hate to give a floppy answer but it really is a mix of all of three. Our interview is a four step program. I warn our candidates that during our interview process, they may be asking themselves, "Am I interviewing for a software engineering job or for a data science role?” We really do test those Python and Scala skills in our candidates, but our interview process is pretty detail-oriented and by the end of it we have a good feel for their analytical abilities, their mathematical abilities and their culture fit as well. And we try to paint a nice, complete picture when making our final decision on our candidates.
Do any of the Metis students you’ve hired have PhD’s?
Brennan: Megan, correct me if I’m wrong, but I don't believe they do. The people that we've hired don’t come from traditional computer science background.
Megan: One person has a Masters degree in Math, which is relevant, but not computer science. Another person had a bachelor's degree.
Has it ever been a concern for the Capital One Labs team that those students didn’t have PhDs?
Brennan: Generally speaking, in our data science community here at Capital One, not many of them come from a traditional computer science type of background. Of our Ph.D.s, we legitimately have a Ph.D. in theology all the way through to astrophysics and everything in between. A few of those are computer science backgrounds but not all of them.
Brennan, how do you ensure that new hires at Capital One Labs are supported as they continue to learn after they had graduated from Metis?
Brennan: Absolutely- I think that's the hardest thing for me to portray to candidates as a recruiter. At Capital One Labs, we have a large community of data scientists all over the country- San Francisco, Chicago, New York, DC, Dallas- and that community of data scientists is really supportive. We know most of one another on a first name basis. We have our own internal Big Data Academy as well. We have meet ups internally where we all meet in one city and catch up with one another. Capital One is a great place to work as a data scientist for several reasons, but the one that's hardest to portray is the community within the Capital One culture.
Capital One is a really big company but as a data scientist it feels more like you're working at a big start up with around 100 to 200 people as opposed to a huge bank with 50,000 employees. They all know each other very well, have lunch together every single day, and travel to see one another all the time. It's a really tight-knit community of people.
Megan, as you build Metis's employer network, how important is it that your employer partners provide mentorship for your graduates?
Megan: That's something we definitely look out for, and we hope to partner with those types of companies. We see our students and our grads being selective for those types of opportunities as well. Unless they're coming to Metis with a lot of professional experience already under their belt – and we definitely do have that profile of a candidate – then they may be okay in a less mentorship-driven company. But to find an employer like Capital One Labs is just so awesome for our students.
We've also started working with employers on building out apprenticeship programs and that has been very successful. And really the model works. We have tried 8 to 12 week maybe even 16-week apprenticeships where they can come in, work on real projects for the team, and there's a heavy mentor component to it. They can get up to speed in more of a comfortable but accelerated pace, and when they come on full time they're ready to go from Day 1.
Do you see most Metis graduates going into straightforward Data Scientist roles or are there other types of roles that students can get when they graduate from Metis?
Megan: The title that we most often see is Data Scientist. We also see Data Analysts or some analyst position. Data Engineer is usually the third title we see.
What have you seen your most successful Metis graduates do differently that really sets them apart as candidates for data scientist roles?
Megan: First, how do you define “successful?” In my eyes it's a student who lands a competitive data scientist position at a company that they're super passionate about within the first month or two of graduating from Metis. When I see people following that path, they've been incredibly engaged during the bootcamp and also they're all-in with careers. They're just soaking up the advice and all the tidbits of information that we that we put out there.
The most successful Metis grads are juggling and balancing a lot of or spinning multiple plates at once. They're building their projects, they're learning, they're involved with the career team, they're getting their resumes together, they're interviewing. Juggling everything is a lot to ask, but they're able to handle it gracefully and confidently.
After Metis, they have a plan. They're not taking a few months to just relax, they're aggressively attacking their search and taking all the advice that we've given them and totally utilizing their network that we helped them build and just going after it. I see those people with the most offers, having the most options, and feeling most successful out of Metis.
Do you recommend that students start applying for jobs the second they graduate from Metis or should they wait a couple of months after they graduate to improve their portfolio?
Megan: I think personal comfort level factors in, but we see a lot of people starting to get a little antsy about their search around Week 9 of the bootcamp. They’re starting to think of their list of target companies to start applying to and researching. Around that time, they may start putting out some applications and start getting some coffee meetings with hiring managers. By career day, they're starting to more actively apply. I think that's a pretty good timeline to start things off.
Do Metis and Capital One Labs have a nice feedback loop? If you notice a new hire is lacking in one subject, are you able to give that feedback to Metis?
Brennan: Yeah, fortunately we haven't seen any gaps to relay back to Metis. Capital One has a relationship with Metis on several different levels. We partner with them in all kinds of different ways. So there’s a nice give and take between our relationship as far as helping them help us and vice versa.
Megan: We have a close partnership and it's been great to work with them. They've definitely given us some awesome feedback on career day and that has certainly evolved drastically over the couple of years that we've been running bootcamps. It's just awesome to get that advice because they're the ones that we're doing this for and their feedback is so important to us.
Brennan, will you hire from Metis in the future?
Brennan: That's the plan!
Megan, there’s a lot of talk about how to report outcomes and the methodology behind those statistics. Is Metis planning on publishing an audited report anytime soon?
Megan: We're definitely thinking about that very seriously. We are actually accredited by a company called ACCET and we have to go through an audit process frequently. We have to submit verified employment data once a year. Our numbers are very much accurate and if they weren't, they would shut us down. We are under very strict guidelines on how we report that data.
Brennan, last question. What is your advice to other employers who are thinking about hiring from a bootcamp or from Metis in particular? Have you found the secret sauce to navigating through the bootcamp world?
Brennan: The hardest part about hiring from bootcamps is that they essentially open the floodgates and all of us potential employers are all trying to find the top talent from the bootcamp all at once. The hardest part for us is having an interview process that selects the top talent and making sure that they are the right fit for our role, but then also being fast enough and agile to work with Metis grads as well because there's so much competition for this talent. My advice is to start early and build a strong relationship with the bootcamp and then build out a process that can effectively and quickly hire the right people.
Is there anything that we totally skipped over that either you Brennan or Megan want to make sure our Course Report readers know about?
Megan: It’s been great working with Brennan and the Capital One Labs team and we look forward to more partnerships and continuing the relationship and hopefully getting even more Metis grads onto their team in the future.
If you're a college student, an incoming freshman, or a teacher with a summer break, you have tons of summer coding bootcamp options, as well as several code schools that continue their normal offerings in the summer months.Continue Reading →
Since launching in 2013, Metis has always partnered with leading tech companies to design their bootcamps- they teamed up with world-renowned Rails shop Thoughtbot to develop their Web Development bootcamp, and partnered with Datascope Analytics to design their current Data Science bootcamp. While we’ve chatted with instructors and curriculum designers at Metis, this is Course Report’s first time talking with Metis students! In this Alumni Q&A, Emily and Itelina tell us what they were up to before Metis, their data science bootcamp experience and their lives post-graduation.
Liz: What were you up to before you started at Metis? Tell us about your education background, your last job, things like that.
Emily: Before Metis, I was working for a small branding firm where I was doing qualitative research and visual strategy/brand strategy, and I really liked doing research. I have a Bachelors of Fine Arts, which isn’t something you necessarily think of with data science. I got really interested in data visualization and wanted to learn more quantitative skills. I started taking courses in the evening in statistics, and that snowballed until I found out about Metis and this data science course and I thought, “Okay, I want to learn all of those things that they’re teaching.”
Itelina: I graduated with a degree in mathematical economics from Princeton University four years ago. In the last four years, I’ve been working as a management consultant at Price Waterhouse Coopers. I work mostly with healthcare clients such as hospitals and insurance and pharmaceutical companies.
I did a lot of projects that involved solving problems with data, so I was often the lead on the analytics work stream. I worked on analyzing data from hospitals and insurance companies and seeing different patterns in cost of care and quality of care. That got me really interested in solving problems in data, and I also did some of that in my undergrad degree.
I really wanted to find a program where I could the latest techniques, technology and tools, and Metis was the perfect program.
Liz: Itelina, what clicked and made you think, “I want to be a data scientist, go to a bootcamp and get that data science job after I graduate”?
Itelina: When I was an undergraduate doing economics research, I used statistical software to analyze data. When I was consulting, analyzing data wasn’t always the primary focus because I was part of a group working on strategy.
But we started working with clients who had increasingly larger data sets. Very early on I realized that some of the traditional tools used in data analysis, such as Excel, were obviously not powerful enough to do the kinds of analysis we needed with larger quantities of data.
So I started taking courses online forum Coursera. I took the data science specialization track and I learned a R and things like that. That made me realize that there are a lot of technologies and tools out there and that I wouldn’t be able to focus on learning all of those technologies while working at my job. That made me realize that I wanted to attend a bootcamp program.
Liz: Emily, you mentioned doing a night statistics class, right?
Emily: I did some online courses as well. Not through Coursera, but I did one that was through EdX.
Liz: Did you ever go to meetups or workshops before you thought about plunging into a full-time bootcamp?
Emily: I hadn't been to any meetups before. I did go to the open house that Metis had just to check them out to see if this was really something that I was interested in, but I wasn’t really going to meetups.
I did some in person courses as well as the course that I did through EdX. I was sold on the idea after I went to the Metis open house. After that, I researched the other boot camps in New York, and I felt like Metis really stood out to me.
Liz: Itelina, did you also look at data science bootcamps in New York?
Itelina: Yeah, I did. Honestly, I considered graduate school programs vs. bootcamps. I figured that doing the bootcamp would be good for me because it would be a shorter program, more practical. I looked at some of the other bootcamps in New York City. There’s a bootcamp called Data Incubator; they’re geared more towards students with PhDs.
There were a couple of other programs, but I just thought that Metis was great for what I was looking for because of its instructors, full day program and the curriculum, so that was why I chose it.
Liz: Emily, what were the other boot camps that you looked at?
Emily: I can’t remember the other ones that I looked at but I looked to see which ones were being offered in New York. The main reason I chose Metis is because they had their curriculum published. I think there was another bootcamp in New York that was teaching mainly R, and I was more interested in Python because I felt like it’s a language that’s very versatile and can be used for so many different things.
They touch on so many different languages like D3, which I wanted to get better at. We did a little bit of SQL, MongoDB and Hadoop. I wanted the full breadth of languages.
Liz: Tell us what the application was like? Did you have to do a coding challenge in a certain language?
Itelina: For the application process at Metis, you do a take-home challenge. You’re required to do it in Python but you can do it at your own pace at home. There’s a set of problems for you to solve with descriptive results and answers.
There was a video interview with one of the instructors. They do test your background but it wasn’t intense or difficult.
Itelina: I knew how to do data analysis in R but when I got the challenge from Metis, I said, “I need to learn how to use Python,” so I looked through a book and did the challenge.
Liz: How many people were in your cohort?
Itelina: I think about 20-25 people.
Emily: I think it was 21.
Liz: Did you find it to be a diverse class in terms of age, gender and race?
Itelina: In terms of gender, definitely. I think half of our class were girls and half were guys. Age, definitely; we had people who recently graduated from college and people who were trying to make a mid-career change. We had people who were born outside of the U.S., so definitely a lot of diversity in backgrounds.
Liz: I’m not super familiar with the world of data science but is it fairly male-dominated?
Emily: I’m not sure because I feel like it’s such an emerging field. That’s one of the things that I considered when I was looking for a job after graduation. There are many people trying to hire data scientists right now. I’ve met people who ask what I do and I say I’m a data scientist and they say, “My company’s been trying to hire a data scientist for months and months and they can’t find anyone.” That could be because there are more positions to fill, but it could also be that different companies are looking for someone very specific to fulfill their needs. I don’t know yet how if I feel that it’s a male dominated field; possibly.
Liz: Who were your instructors and was it important to you to research the instructors?
Emily: It was important to me. I was actually a bit disappointed because when I first went to the open house Irmak and Bo were instructors at the time. I met them and thought they both seemed really nice. I was excited for them to be my instructors then they weren’t. But, the instructors for our cohort were also pretty amazing.
Before it started, I looked at their LinkedIn profiles and Googled them to see what their backgrounds were. They were definitely very qualified.
Liz: Who were your instructors?
Emily: Erin Schumacher and Jon Hanke.
Liz: What was the teaching style like?
Itelina: The general format for the bootcamp is lectures for a couple of hours in the morning and some pair programming problems to improve our coding style. In the afternoon it’s mostly individual work on projects. The instructors are available and you can ask them any questions or ask you can ask your classmates. It’s half structured, half individual work.
Liz: You’ve both done a traditional 4-year degree. What did you think about the teaching style compared to a traditional university classroom?
Itelina: I thought the format was very innovative. It’s project based so every two to three weeks we do a project and make presentations. It’s very practical and simulates a real work environment where you’re delivering results in short time periods.
I thought it was a very innovative learning model because you’re learning and doing. At the same time, we have good coverage of the theory behind the different models and the technology tools.
Emily: I thought so too. Having the unstructured time in the afternoon — it was sometimes hard to stay focused, but it did replicate a real work environment in that you don’t always have someone looking over your shoulder and telling you “You need to do X, Y and Z.” You sometimes have to figure that out for yourself and manage your own time.
Liz: Were there also teaching assistants or former students that were around to help?
Itelina: We had one teaching assistant. She was around to help everybody with the problems sets as well as the questions so we had very good support.
Liz: Was there a good feedback loop with Metis?
Emily: We had one-on-one meetings with staff members including one of the co-founders. They like to do that a few weeks in to see how it’s going for you and to see what’s working for you and what isn’t, so that they can make real-time adjustments to the course. They encouraged us regularly. Occasionally they asked us to fill out anonymous surveys.
Itelina: I definitely agree. They were very active in getting feedback and making adjustments on the spot. If there was a particular topic that we wanted to cover more, they would schedule an ad hoc session in the afternoon during our free time. It was a very good process.
Liz: Did everybody that you started with finish with you?
Emily: I think we had one person who started who left after the first week. The other 21 people made it all the way through to the end.
Liz: Did you have tests or assessments throughout or was it mostly project based?
Itelina: We didn’t have tests; mostly project based assessments.
Liz: What technologies did you learn at Metis?
Emily: Python was the main language that we used. For different projects we were encouraged to use different technologies. SQL, Mongo DB, Hadoop, D3, Hive, Spark —we covered those and you could use them or not, depending on whether they were necessary for your project. Other people with more advanced knowledge coming into the course got into Neural Nets, but that wasn’t explicitly taught.
Liz: Emily, you mentioned your background and wanting to get into data visualization; were you able to do that at Metis?
Emily: You could go as far into D3 as you wanted to, but there was a lot of emphasis on communication. The three main things that they talk about are programming, statistics and communication. At the end of a project when you’ve done all the complicated statistical models, you need to explain it to your client or the stakeholders in the project. A lot of the communication comes from data visualization. That was discussed and we were encouraged to explore that.
Liz: Itelina, can you tell us about your favorite projects?
Itelina: For one of my projects I worked on some data that was published in a Kaggle competition. Kaggle is a company that hosts a lot of predictive modeling competitions. Sometimes a company will say, “We have this data set and we’re looking for someone to develop a predictive model on this outcome.” Kaggle will host this challenge as a competition and there is money involved, so people compete for these prizes. There’s a leaderboard where people see their scores. It’s a great resource for keeping up with the latest techniques that people are using as well as data sources.
I used Kaggle to get a set of data. That data set contained information on patients and their medical records. The topic was predicting a person’s risk for developing type 2 diabetes based on their past medical record. For that project I worked on developing a predictive model for a person's risk for diabetes based on these other factors. That was definitely an interesting project.
Liz: I love that because it’s clearly your former career influencing the way you use data science skills to solve problems that you couldn’t have solved before. Did you work on it alone or with a group?
Itelina: For that project I worked with a couple classmates who also picked healthcare related topics. Another classmate focused on the wellness aspect. She was analyzing diet and blood pressure, and how that correlated to their risk for certain diseases. Somebody else did something similar with heart disease. I worked with a group collaborating on similar topics but on different angles.
Liz: Emily, what was your favorite project?
Emily: It was probably my final project where I did some analysis of UN data. It was for a data visualization challenge that the UN was hosting on the Millennial Development Goals. It was a 15 year project in which the UN focused on specific goals.
One goal concerned curbing the spread of different diseases. I focused on HIV data, and found some interesting things going on with the spread of new cases of HIV in Sub-Saharan Africa. I looked at which countries were able to stop the spread of HIV.
It was hard to determine causation in the data, but I managed to come up with some interesting visualizations. I was a finalist in the competition. Also, the UN took notice of me and offered me a job!
Liz: What are you up to now?
Emily: I’m on a short term consulting contract with the UN right now. I’m working on a dashboard exploring some data related to climate change.
Liz: What was that process like?
Emily: I sent it to them because I had to make the entry into the data visualization challenge. Also, my school had a contact at the UN because they were hiring. I sent in an application and got an interview because they saw the work that I did for my final project and thought it was good.
Liz: I’m assuming you’re using data science skills for this project.
Emily: At first when they were telling me about the project I was a little bit scared. I knew it was only a three-month contract and I thought, “You want me to do all that analysis in three months? You want me to solve climate change in three months?” But they have a lot of analysts who’ve been working with this data for a couple of years. I am working with a team of people who are figuring out how to present it and build the dashboard.
Liz: What is it like transitioning from a bootcamp into a real data science position?
Emily: At first it was difficult because I’ve worked on so many of my projects individually. I had to figure out how to setup a workflow in terms of code.
On the other and, I really feel like I need to shake my imposter syndrome because I feel like I have so many skills. I’m working with people who are also very skilled, but I don’t feel like my skills are sub-par.
Liz: Itelina, what are you up to now since you’ve graduated?
Itelina: I’ve been doing a lot of job searching. I would say the process has been a lot better than I would have expected if I was just searching for jobs on my own because Metis provides a great network. We have great career services and great support.
In fact, before I graduated from Metis, I actually had a job offer. I think Metis really does a great job of making the right introductions and connecting you to the right companies that are a good fit for your skills.
I’m looking for a job where I can grow my career, so I want to take a little more time to talk to different companies and see what’s out there.
Liz: What’s your dream position?
Itelina: I definitely want to work for a company that has a really good vision, so a company that will make an impact on our society. I’m looking at opportunities in mid-stage startup companies, although not exclusively. I’m looking primarily for opportunities in healthcare, but I’m looking at opportunities in other industries as well. Definitely a data scientist/analyst type of position.
Liz: What is Metis’ approach to job preparation? Did you do mock interviews, resume building?
Emily: We did all of the above. They did a really great job. We had workshops on writing resumes and cover letters. We also did a mock interview where we were actually whiteboarding. We had the opportunity to do interviews for soft skills and one-on-one meetings with career staff.
Sometimes it felt like it was a lot to take in while learning so many things, but it was really supportive. I still communicate with the career staff. They still respond if I want them to look over a cover or some changes I’ve made to my resume.
Liz: Itelina, you’ve gone through a number of data science interviews with different companies. Have those been set up through Metis or through your own network?
Itelina: Partly through Metis and partly through my personal network. Generally Metis makes an introduction or tells us about a certain opportunity. We take it from there and see if there’s a fit.
Liz: What is a data science interview like?
Itelina: It’s actually very different from company to company. I’ve interviewed with companies where all that’s required is a take-home challenge then going to the company and meeting people and seeing if there’s the culture fit.
I’ve had other companies ask me to solve math problems. They would ask me to do that for an in person interview as well, and I would have to make a presentation to an entire group on a past project and defend my work.
I’ve had interviews where people asked me questions related to SQL. I think it’s different depending on what each position is asking for.
Liz: What has been the reception of you being a bootcamp graduate?
Itelina: People are generally interested. At almost every interview people ask me to describe what the program is like and the things that we do and how they teach. People are definitely interested in that kind of background. They’re interested in how well it prepares you for the kinds of problems you have to solve in a real job.
Liz: My last question that I ask everyone is was it worth the money and would you recommend it? Do you think that you could have learned everything you learned on your own?
Emily: I would say yes, absolutely. I think it was worth it. It was a challenge. It was really intense but I don’t think that I would’ve gotten all of this knowledge on my own, especially in this short amount of time.
I’m continuing to reinforce that knowledge and add to it with projects that I am working on. I really don’t feel like I could’ve downloaded all of that knowledge into myself in any other way.
Itelina: I absolutely agree with everything Emily said. I would definitely do it again in a heartbeat. It was definitely worth the money. The learning environment, having the type of classmates that you can learn from, the instructors — you definitely couldn’t have gotten it just by learning on your own.
Again the Metis hiring network is also really great, so I would definitely recommend it to anyone. I wish the program was longer so that I could stay there and learn more!
Liz: Anything you want to make sure people know about Metis, bootcamps or data science in general?
Emily: There is a lot of talk about data science because it is one of these fields where there are more open positions than there are people to fill them. I would say to do one of these courses, a masters or a bootcamp, you really have to love working with data, love a challenge and love problem-solving. You have to love working hard. I don’t think it’s something you can do just because you want a guaranteed position and a decent salary. You have to love this kind of work.
Itelina: I would say anybody who’s looking to get their foot in the door in data science or looking for a change should check out bootcamps. This type of program is very innovative. I benefitted greatly from the program so I think it’s great for somebody who is trying to start a new career.
Did you miss the last Metis Data Science Open House? Catch the replay, along with some of our favorite moments from the panel Q&A, here!
You don’t have to be a data scientist to read into these statistics: A McKinsey Global Institute report estimates that by 2018 the US could be facing a shortage of more than 140,000 data scientists. The field of data science is growing, and with it so does the demand for qualified data scientists. Sounds like a good time to pursue data science, right? No kidding! Data scientists make an average national salary of $118,000. If you’re looking to break into data science, or just trying to refresh and hone the skills you already have, Course Report has you covered. Check out this comprehensive list of the best data science bootcamps and programs in the U.S. and Europe for technologies like Hadoop, R, and Python.
Welcome to the January News Roundup, your monthly news digest full of the most interesting articles and announcements in the bootcamp space. Want your bootcamp's news to be included in the next News Roundup? Submit announcements of new courses, scholarships, or open jobs at your school!Continue Reading →
Welcome to the August News Roundup, your monthly news digest full of the most interesting articles and announcements in the bootcamp space. Want your bootcamp's news to be included in the next News Roundup? Submit announcements of new courses, scholarships, or open jobs at your school!Continue Reading →
What does it take to become a UX Designer?Continue Reading →
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We learned so much in our webinar with Laurie & Irmak of Datascope Analytics and Metis, including:
As promised, you can watch and share the whole webinar on demand by following this link.Continue Reading →
Join Liz of Course Report and Laurie & Irmak of Metis on Tuesday, July 22 for a free webinar. You'll also have a chance to ask any questions you have about the Data Science course at Metis!Continue Reading →
Date: Tuesday, July 22
Time: 6:30pm EST
Since launching their Ruby on Rails bootcamp in Boston, Metis has expanded to new cities and added courses in new fields. Their upcoming Product Design course in New York aims to prepare students for a job in the growing field of UX and Product Design. We talk with Alex Baldwin, co-creator of Metis Product Design Curriculum, about the differences between UX Design and Product Design, why the bootcamp model is great for this subject, and the types of students who will excel at Metis.
What is a product designer and how is it different from a UX Designer?
Never have I met another industry that cares more about what they call themselves. Are you a UI or a UX? Usually, you’re doing a lot of everything. So I see product designers as people who are not afraid to just jump into something. Some days, I’ll be doing database stuff. I might not be the one to write the queries but I’m the one that’s going to bring attention to that problem and make sure that resources are allocated to fixing it. There’s definitely been a push in the last couple of years; designers are becoming more of marketers and using growth hacks. It’s definitely bringing design and marketing together.
It’s really fascinating to see something like the Dropbox case study of how their referral program allowed them to be enormous. Our clients at thoughtbot request types of features like that, and being able to attach a growth loop onto something that people really desire is a fantastic way to make sure that you stay interested in your product and people are using it and recommending it once it’s good.
When it comes to products, you have to look at flows, user interfaces, functionality, measurement and research and there are all these other aspects to consider.
Why do you think that the boot camp model will work for product design?
I think our boot camp model is wonderful because the last month is all about passion projects. We’ve identified that there’s definitely this need when people join bootcamps, specifically those who want to do product design, that they have something in their portfolio that they can show. They care most about getting feedback on their work and getting those as close to done as possible.
Doing this learning in the classroom setting is way easier; students sit down, map out where they need to go, and the instructors can go through and evaluate how to get them there most efficiently.
I think that in-person feedback is really important. So, having the dedicated 3-month period of time where this is the only thing they’re focused on is what will really be successful.
What sort of student are you expecting? Are you expecting somebody who has to have a passion project in mind? Do they need experience in coding or web design?
First and foremost, they should have some sort of background in graphic design. I think if you already call yourself a designer and you have a portfolio, it doesn’t really matter what’s in that portfolio. It could be illustration, marketing pages, it could be anything about design, whether it’s UX or not.
There are no “last job” requirements. What we really care about and what we’ll be interviewing for is people that have been satisfied with their work but they crave more. They want more of a connection with the people that they’re making these projects for and they want to make sure that the projects work.
Will you all have a technical challenge or will it be more of a culture interview? Have you thought about what the interview will look like?
Yeah; the course does include basic HTML and CSS. The reasoning for that is it’s hard to get things to completion when you don’t control that stack. If you’re waiting on someone else to finish part of your work, you’ll just have to sit there and wait. So I think we’re going to spend a lot of time building those skills.
There’s not necessarily a technical challenge, although we are going to have people do a graphical challenge. We want somebody to produce something… Even if it’s really bad, that’s fine; we just want to see where people’s graphical skills are. But I can’t tell you exactly what our challenge is!
Will students have pre-work on HTML/CSS?
Yep, we have two weeks’ worth of pre-work, and all our pre-work consists of custom tutorials that we’ve written ourselves. We just want to make sure that they’re versed in the software flow and are able to scale up those skills pretty easily from the beginning.
What has it been like to design a curriculum for a fairly new subject matter?
This has actually been super fun because there’s nothing else to go off of. We took the core cases of interaction design and made those cases stick together really well. We looked at the key abilities and how we could increase people’s yield in those activities.
We’re doing these weekly exercises and they’re really meant to push you into getting to being in a production role and thinking about the whole loop of products.
It’ll be really cool to see how many different variations of the same assignment turn up.
We are all really excited.
So will students do those 7 projects over the first 2 months and then they work on their passion project?
The structure is 7 weeks of lectures and exercises- there are no abstract lectures. One day we’ll talk about data analytics. At the end of that lecture, they’ll translate that concept into an app.
The way the lectures work is that the co-instructors will trade off. You’ll have one person taking one day and the other will be walking around and helping students while the lectures are going on and answering questions.
Then we also have a team project that we’re going to put together. We want to group some of the designers in teams of 4 or 5 and have them work on a behavior builder. What I like about the behavior builder is that it’s really easy to see how people interact with this in real life and measure it’s success, as opposed to hey, let’s release a music app and maybe it’ll do something or it won’t. It’s very clear whether this action is happening or not.
In talking with students, I hear a lot of feedback saying that they want to work as a group and do team projects so they’re prepared for that in the real world.
It‘ll be super fascinating because these students aren’t all going to be from similar backgrounds. They’ll realize that one person is clearly better than them at graphic design, so they should be the visual designer of this project. One person has done research before, so they should fulfill that role. And just seeing how those roles specialize and how they work together will be neat.
Will the Metis Product Design bootcamp have “personal investment days” like the Ruby on Rails bootcamp does?
I think with the Rails bootcamp, they have Knowledge Bomb Fridays with friends of thoughtbot and employees. They also have Fireside Chat Tuesdays that are employer-focused, where they have people speak from hiring companies and they’d bring in a Head of Engineering to talk about something that’s specific to that company.
We plan on doing similar things with guest speakers from the community.
Have you had to think about working the outcomes into the curriculum like doing job placement, interview practice and stuff like that or are you letting Metis handle the brunt of that?
Metis is handling most of that. They have a really awesome talent placement manager that we’ll be working with and they’ve done great work. I think the hiring percentage out of the first bootcamp has been strong, and it’s only been a few weeks.
For me, I’m thinking how can we get someone in 3 months to be hirable at thoughtbot? I know that there’s other companies that hire similarly to us but usually their bar is not quite as high. They might want more visual design but generally, the bar at thoughtbot all around is high. If I needed someone to be hirable here which we’ve done in the past through our friendship program – I think we’ll be doing a really awesome job and unleashing some seriously terrible monsters in the design field on the city of New York.
Does that mean that thoughtbot will be hiring from this program?
We are a hiring partner and we do have an office in New York. If anyone’s a fit, we’ll see. I still don’t have any students placed so as soon as that happens, we’ll have a better gauge.
[As of December 8, 2017, Dev Bootcamp will no longer be operating.] The sale of Dev Bootcamp, the first coding bootcamp of its kind in a rapidly expanding space, to Kaplan marked a significant landmark in the coding school world. Kaplan, the test prep powerhouse, lends legitimacy to the bootcamp model as a viable form of education.
Other coding schools’ recent fundraising only confirms that the model will continue to grow and perhaps even expand to subject matter beyond coding, like sales, user experience design, and digital marketing. General Assembly raised an impressive Series C round of $35MM in March 2014, and Flatiron School, based in New York, recently pulled in $5.5MM in new funding in April.Continue Reading →
Irmak Sirer and Laurie Skelly work as data scientists (Irmak is also a partner) at Datascope Analytics, a data-driven consulting and design firm in Chicago. When they met the folks at Metis, who have already proven their propensity for great partnerships with their thoughtbot collaboration, it was clear that a Data Science program was in the cards. Now, Irmak and Laurie are designing the curriculum for the upcoming Metis Data Science course in New York, which is one of the only programs that teaches relative beginners (students don't need PhDs to apply) to be employable data scientists.
Tell us about Datascope Analytics and how you got involved with Metis.
Laurie: Datascope is a data science consulting firm. We work with a wide range of clients from regional not-for-profits all the way to Fortune 500 and national corporations. People usually ask what kind of data we work with. We’re the kind of firm that uses any data source to help people solve their problems. We’re a very broad, general-purpose firm.
Most of us have some kind of academic background and we have continued to come back to the idea of doing some sort of teaching or training. We’re small in size, so finding the bandwidth to run classes and develop new material was really going to be difficult and probably a really long-term idea.
We ran into Jason and Bernardo from Metis at the Strata conference, which is a data science conference; I used to work for Kaplan part-time in graduate school. They were looking for data scientists, people with domain knowledge to help develop the curriculum, and we were really excited to find a way to do some teaching and training without having to take care of all the logistic aspects ourselves.
Irmak: Laurie first met Jason and Bernardo and the entire Datascope team was really excited about it and I also jumped in on the opportunity. Now Laurie and I are designing the course and we will be the first instructors.
We have seen the boot camp model expand to data science and product design, etc. Why do you think that the bootcamp model can work for data science?
Laurie: I think that there are a few reasons why it’s a really great option for a lot of people. Usually, people who would consider the bootcamp are comparing it against a master’s program or self-teaching.
A couple years ago, there were really no options for Data Science masters degrees, and this year I think there are 40 new ones that are starting. But those will take 1-2 years and they’re usually upwards of $50,000.
On the other side, there’s self-teaching. There’s so much available on the internet but it’s really difficult, even for disciplined people to stick with it for the amount of time it takes to learn and be employable.
The Metis program is a very happy medium of a 3-month time window where you can take a leave of absence or say, “I’m gonna make the jump and figure this out.” I’m sure that some people will also be sent by their employers.
Irmak: I think with something like a master's program, you absorb a lot of theoretical knowledge. Then you start working as a data scientist and it also takes a long time to get familiar with the real life practical applications of that theoretical knowledge and relearning all you were taught. We want to throw you right into the thick of real problem cases and supply the information you need to get through. This gives you valuable experience.
Do you expect that students will graduate ready to be junior data scientists at a company?
Laurie: Absolutely. There are a ton of people out there who have all or nearly all these skills; they’re just filling in a couple of cracks.
I had the experience of knowing a lot of what I needed to know and not understanding what it was called. We come from different academic traditions and a lot of times, when discussing machine learning and regression techniques and modelling, people will be talking about the same space and really have a lot more knowledge in common than they realize. They just need to have that reconciliation. There are also people in the academic world who need to adjust to how different things are in the world of business.
I don’t want to hit this point too hard because I think it’s part of the secret sauce of why data science is so valuable right now, but we don’t know everything. Being on a job, we don’t know everything all the time. Data scientists, even the best ones in practice do a lot of Googling. It’s about having that skill and that confidence to apply the solution to the problem even if it’s a problem that you’ve never encountered before.
That’s the sort of thing that we can definitely teach in 12 weeks, especially the way that we’ve designed it- the process of fearlessly tackling very technically challenging things, knowing where to look, and knowing how these pieces fit together.
This will be your inaugural cohort- do you expect people to have experience with coding or analytics or statistics, or could somebody theoretically be a beginner?
Irmak: What we expect is some exposure and experience in statistics and programming but not more than that. We’re actually expecting different people with very different skill sets.
I think that there will be people that have a lot of programming experience and have seen a little bit of statistics, maybe in school, but they don’t really do focus on analysis. There could be a student that worked in the sciences, as another example, where they used statistics a little bit and they coded a little bit but they haven’t done anything like data science directly.We would also consider people that do more traditional types of statistics. They’ve done some coding but they’re not that experienced in programming.
The idea here is, not everybody is going to gain the exact same skills going through the boot camp. Wherever you are weaker in the big picture of Data Science, you will learn and strengthen that part.
And we think that curiosity and creativity, those kinds of personal attributes are also very important; because as Laurie said, a lot of data science is having confidence in your ability to learn something you don’t know, which means that you should be curious and creative with the tools themselves.
Laurie: If your question is- are we going to be able to take beginners and make them data scientists in 12 weeks? I would say the answer is “not yet.” Because this is our first class, we don’t know how inexperienced someone could be and still bring them up to speed.
Fortunately, I think that since we’re so early in the data science bootcamp game, we’re going to have a pretty competitive applicant pool. Maybe in the future we’ll be able to assess people and say, “You’re really great on your programming but you’re missing some skills in experimental design; go bone up on that and come back and we’ll be ready to take you.”
Our biggest goal is to be telling the truth when we say in 12 weeks you’ll be able to get a job and not drown in that job. We don’t have any interest in putting people in situations they’re not ready for.
Have you thought about what the interview is going to look like?
Laurie: Yes! Based on people’s expectations, it’s way more of a culture interview – but there is a technical challenge as well. People think about data scientists and they think about the toolbox and how intimidating it is. But like we said, we really need curious, clever people more than someone with a lot of technical experience. I’d rather have someone who I can tell is going to pick something up quickly or is asking clever follow up questions and is just really listening closely. We’ll probably learn more as we go but for this round, we’re going to be setting ourselves up for a better experience if we find a lot of really good personality matches. These will be our pioneers.
What is the technology stack that students learn in a data science boot camp?
Irmak: We think of it not only as a technology stack; we think of it as data science dimensions- the domains and the data, algorithms, tools and the visualization/communication part of a project. Basically, we want the graduates to be equipped in all of these dimensions.
In terms of data, we’ll be teaching SQL and non-SQL databases. We’ll be teaching about APIs and web scraping, where you could get data from different sources and how to clean that data.
In terms of algorithms, we will go over machine-learning algorithms; we will go over regressions, supervised learning and non-supervised learning. The tools you will learn is how to apply those algorithms.We will teach the course using Python as a programming language. There are a whole bunch of packages in Python where you can apply these types of algorithms.
In terms of visualization and communication, we think that the communication skills and the presentation and the relationship with the client is very important. We will teach these skills alongside visualization tools such as d3.
Laurie: In every project, you have to decide what kind of data you’re using, how do you get it, how do you store it, what are you doing with is as far as algorithms, what are you using to implement those algorithms and when you are done, how are you going to show those results to somebody else?
So for each area, or dimension, of data science, we will provide broad exposure to what’s out there so the students have a good sense of the ‘lay of the land’. In each one we’ll also be working from a kind of ‘home base’ - for a programming language, it will be python; for visualization, it will be d3; for databases, we’ll use mongoDB and MySQL. For each of these they’ll have some repetitive use and training, so they will build up a toolkit that they are familiar with, but also understand what alternatives are out there for each piece and be ready to make some changes in the lineup if a job or project calls for it.
Of our 12 weeks, we use one of them to spend a lot of focus on d3 because everybody wants to be able to make cool visualizations in d3 and we think it’s worth it.
How will your project-based curriculum look?
Students will be working on 4 or 5 projects and for each one, there’ll be some kind of output. There are many ways where they might be doing similar algorithms but different people will be picking different data sets so they’ll have different outcomes and it’ll be really interesting for people to express themselves as they’re going.
Irmak: I think it’s a great way for them to show how they can tackle a lot of problems for potential employers, instead of just one big project, you get to show completely separate, unique problems that have nothing to do with each other that show all your skills.
Has it been a challenge to create a bootcamp curriculum for data science?
Laurie: Metis really picked the right data scientists in partnering with Datascope; we’re obsessed with design. When we were at Strata, we were doing a workshop called “Design Thinking for Dummies (Data Scientists).” So this is the kind of stuff we love to chew on anyway.
What will a typical day look like at Metis?
Irmak: First of all, generally, you will be working on a project. The first project will be just a week, another one 3 weeks long, and so on.
You will have questions about how to make progress with your current project, you need to know about the tools, algorithms, approaches. During the day we will give some lectures where you are getting some of that knowledge and then most of the day, you are actually working on that project, applying that knowledge and also, creating new questions.
Will students have pre-work once they get admitted and how many hours are you expecting that to take?
Laurie: They have online pre-work; we have aimed for a maximum of 30 hours so that people could do it in about 2 hours per day at home, basically 2 weeks.
Pre-work focuses on using the command line, Python and some statistics background. They’ll work through some examples from some books that we love and there’ll be some example problems from those books. If they can submit the answer correctly, they’ll pass pre-work.
Are you going to have the “personal investment days” that the Ruby on Rails boot camp has at Metis, where they give people free Fridays?
Laurie: Yeah; we’re going to have guest speakers come in; we’re going to have more like culture days. There are going to be some cool conferences going on while we’re in New York. That and the fact that we’re enforcing that the day ends at 6 and that people continue being reasonable whole people and will take breaks. It will be important to try to keep people from burning out too hard.
Will Datascope Analytics be hiring from the Metis graduate pool?
Laurie: We will be a hiring partner, absolutely. We’re really excited to have first look at the fresh recruits. As a goal, it’s absolutely at the forefront of my mind to create a course that can help students get awesome jobs because that’s why people want to be data scientists. In addition to Datascope, we are sharing information about the type of companies Metis should target to become hiring partners.
I’m sure you all have amazing insight into who they should be reaching out to.
Irmak: Metis is in charge of a lot of the organization but we’re in contact with companies that we worked with, companies that we know and have relationships with and we know are in need of data scientists.
Laurie: I don’t know how many data scientists you know but I’ll just get emails from people at random companies asking if I have recommendations. And it actually feels like a big relief to say, “Let me put you in contact with our hiring person at Metis.”
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Derek Kaknes was sold on Metis because of their association with thoughtbot. A recent graduate, Derek tells us about what drove him to learn to code, the teaching styles that worked best, and how Metis taught him to write code he's proud of.
What were you up to before deciding to go to Metis?
Before Metis I was working with a small team at a company that I helped co-found. At the company, we had gone through several false starts trying to hire the right software engineer, so I viewed Metis as an opportunity to gain the skills necessary to fill that gap.
Did you apply to other bootcamps? Why did you ultimately decide on Metis?
I looked at several other bootcamps, including Launch Academy, Flatiron School and DevBootcamp, but ultimately decided on Metis because of their connection with thoughbot. The opportunity to learn thoughtbot best practices from thoughtbot developers themselves was a huge selling point.
Which instructors/mentors were especially helpful to you? Did you feel like the teaching methods worked with your learning style?
Both of our primary instructors, “Goose” (Matt) and “Steiner” (Josh), were fantastic; but we also had the benefit of other thoughtbot employees dropping in to help whenever they were around. The ability to ask questions and get answers from developers who are in the industry using best practices was enormously valuable.
Can you talk about a time when you got stuck in the class and how you pushed through?
We were taught repeatedly that we should never be “stuck” on a problem in isolation for more than 30 minutes. Whenever I was stuck for more than a few minutes, I would first ask a classmate for help and, if we couldn’t resolve the issue, elevate it to one of the instructors. As a result, I don’t have a great example of being “stuck” in class, but if I had to choose a frustrating experience it would be debugging AJAX requests made to an external API.
Tell us about your final project- what technologies did you use, how long did it take, what does it do?
My final project was called “BTC Shop” and is an ecommerce shopping application designed specifically for bitcoin payment. The main technologies I used were the Coinbase payment API, which is a pre-built “plug-and-play” option allowing applications to receive btc payments, and the Blockchain API, which is a free third-party API that allows applications to send, receive and track btc payments. Using the Blockchain API, I was able to recreate the functionality of the Coinbase product in my own implementation, including producing a QR Code for user’s to scan and send payments. The whole development cycle took three weeks, including roughly one week for initial research and design.
Would you have been able to learn to code and get a job without Metis?
No way. I had tried previously to teach myself, but consistently got caught on some early hurdles and never made much progress. Metis provides an awesome platform to accelerate your initial learning curve and, through the association with thoughtbot, enforces current industry best-practices to every line of code that you write. Could I have learned to write code that worked without Metis? Maybe, but not code that I would be proudly confident to show a potential employer.
Adam Patterson knew he wanted to make a career change, and turned to Metis, a 12-week Ruby on Rails bootcamp in Boston (and New York), to learn the skills to make the switch. In this Student Spotlight, Adam shares his experience, including what made the instructors effective, how he got support from classmates, and the value of an in-person bootcamp.
What were you up to before deciding to go to Metis?
Before joining Metis I was working in a business development role at an education company. I had a very small amount of programming experience, but enough to know that this was a career change I needed to make. After three years of frustration over not being able to change or improve systems I relied heavily upon, I decided to gain the technical skills to be a true problem solver in today's world.
Did you apply to other bootcamps? Why did you ultimately decide on Metis?
I did not apply to any other bootcamps. I did look into other programs, however already being located in the Boston area limited my reasonable choices. I primarily chose Metis because it was being taught by thoughtbot developers and it was exciting to be part of something new.
Which instructors/mentors were especially helpful to you? Did you feel like the teaching methods worked with your learning style?
Both of my instructors were especially helpful. One of the best parts of the experience was that my classmates and I each built a strong relationship with each instructor, allowing us to learn from both of them in a special way. We even had a third instructor join the group toward the end, and he quickly became just as important to us as our main instructors. It's a real testament to the teaching style and approachability of the people at thoughtbot. The instructors did a terrific job of adjusting to our content delivery suggestions on the fly. With over a dozen students at different levels and with unique learning styles, it is difficult to make it work for everyone; I felt as though they did as good of a job as could be expected.
Can you talk about a time when you got stuck in the class and how you pushed through?
Just one time? During the beginning stage of Fail Forward (my final project), I was having a lot of trouble implementing the LinkedIn API and Oauth gem. This was especially problematic because I wanted to get that sorted out before moving on to the rest of my app. I was fortunate enough to have a classmate who used a similar method for his user authentication, and he helped guide me through the implementation. The environment created by the instructors was one of collaboration, and this was an outstanding example of how supportive the group was of one another.
Tell us about your final project- what technologies did you use, how long did it take, what does it do?
My final project was called Fail Forward. It is a place for professionals to share stories of failure, receive feedback from peers in their industry, accumulate knowledge, and become better through failing. I was inspired by a social experiment from high school and the programming mantra of "fail fast, fail often". I used the LinkedIn API and Oauth gem for user login in order to link it to each user's LinkedIn profile. Users could then post their failures within their industry, update their posts with new knowledge, comment on other user's fails, and follow specific fails to find out when they are updated. It is mainly Ruby on Rails, with some JQuery, CSS, and HTML. It took me about 2.5 weeks to get the app where it is today.
Would you have been able to learn to code and get a job without Metis?
I think almost anyone could learn to code without a bootcamp. The value of a bootcamp is, of course, learning the essential skills to be job-ready in a much shorter period of time, learning best practices (very important in Ruby), and receiving immediate hiring support. Again, learning from thoughtbot devs was one of the best parts about Metis; I may not have had much prior coding experience, but I have learned only good coding habits and did not have any bad ones to break.
What do you do once you've established yourself as a top-notch web consultancy in Rails apps? If you're thoughtbot, you collaborate with Kaplan to create Metis, a 12-week Ruby on Rails course, to fuel the next generation of developers.
We caught up with Josh Steiner (of thoughtbot) and Jason Moss (of Kaplan) to find out more about the first Metis cohort, how the program helps graduates find jobs in tech, and what students can expect to learn throughout the course.
What is your background and how did you end up in the Code Bootcamp space?
Josh: I’ve been working at thoughtbot for over a year now, and before that I was a part of thoughtbot’s apprenticeship program. Prior to my apprenticeship, I was a student at Rensselaer Polytechnic Institute, and while there, I made Rails applications for Major League Gaming.
How did you get started with Metis?
Josh: Matt (the other instructor) and I are both involved in Learn, which is thoughtbot’s educational venture (we have workshops, forums, and mentoring), and we saw a bootcamp as an evolution of that idea. thoughtbot is known around the world as being an expert in Rails, so this seemed like a great next step. We pitched it to our CEO, Chad, and he actually told us that Kaplan had approached thoughtbot with the same idea. So thoughtbot and Kaplan came together at the same time, wanting to do the same thing. Matt and I were put on the project to design the curriculum and teach these first classes.
Tell us about Metis and the relationship that thoughtbot has with Kaplan.
Josh: It’s been a wonderful partnership- very collaborative. In some aspects, such as the curriculum, thoughtbot takes the lead, and Kaplan provides expertise (e.g., on learning science and assessment). In other aspects, such as hiring/placement, Kaplan has taken the lead and thoughtbot is assisting by leveraging our client network to identify hiring partners. They’re a great group to work with, and you can tell they are very passionate about what they are doing.
When is your first cohort?
Our first class is Feb 24, and you can apply for the June cohorts in Boston and New York on our website. There will be more to come after that, but so far, those are unannounced.
How many people are you seeing apply for the first cohort?
Josh: We’re at a number we’re happy with for the first class, especially with only two months to get the word out and launching over the holidays. We wanted a slightly smaller class for the first time.
We have seen a ton of interest, however. People see thoughtbot on the ticket and the class appeals to them because of that. We would like to get people to the level of an apprenticeship at thoughtbot, but unfortunately we can’t hire everyone. Hopefully, a few of them will end up working at thoughtbot.
What are you looking for in potential students? Do they need programming experience?
Josh: People need to be comfortable with the computer, but they don’t need to have done any programming in the past. We’re mainly looking for three traits: drive, grit, and communication. We think that these are the most important qualities of a good programmer, and are particularly important for somebody who is going to learn to program in such a short amount of time.
What can a potential student expect to see in the admissions process?
Josh: We have an application on our website, and then filter students for an interview to better understand their motivation, goals, and familiarity with Metis. We also ask some technical questions to understand how they approach and solve problems.
Describe the partnership Metis has with Upstart.
Jason: We have partnered with Upstart, a firm that provides individuals with access to funding for career advancement in exchange for a percentage of their future earnings. As part of the agreement, participants in Metis will have the option of guaranteed tuition financing (up to $15,000) through Upstart in exchange for a small and pre-determined fraction of their future earned income. For more information, visit: www.upstart.com/metis
Can you give us a quick run-down of the curriculum? What’s your teaching style?
Josh: The curriculum is broken into three parts- the first 7 weeks will be teaching the fundamentals of programming. We’ve designed an application, and through the application, we’ll exercise all of the things that we think good web developers need to know. Each day, Matt and I will present a problem to the class (eg. “how can we get users to log into our site”) and then teach them what they need to know to solve the problem. Then we’ll all program it together. By doing it this way, they get to see how experts approach problems. By hearing why we make decisions, instead of us just telling them “this is the way to do it”, they will be able to apply that knowledge in other scenarios, as well.
The next two weeks will be a team programming project- we’ll emulate a client project, and they will see how we work at thoughtbot every day. Matt and I will come to them with a few ideas, the team will pick and plan out the project, and program that collaboratively for the next two weeks.
The last three weeks will be their capstone project. This is a solo project, and it’s something they will be passionate about, because they picked the project themselves. Up to that point, they’ll get small homework assignments (wireframes, database diagrams etc), so that on Day 1 of their capstone project, they can just sit down and start programming.
Also, during the first 7 weeks, we’ll have an “Investment Day,” which is modeled after something we have at thoughtbot. On Fridays, students can work on anything they want. Some students may want to reinforce subjects that they have struggled with. Others may take the opportunity to work on open source, pair program, or build a small app on their own.
How does Metis help your graduates find jobs in tech once they've completed the program?
Jason: Metis has a full-time Employee Placement Specialist on our team, whose primary responsibility is to help our graduates find jobs. This person is a resource, who can help with everything from resume-writing and interviewing to career coaching and company targeting. Everyone at Metis will also participate in a Hiring Day at the end of the 12 weeks. The Hiring Day is an opportunity to meet directly with employers who are interesting in hiring entry-level Ruby on Rails developers, and specifically, Metis graduates. Also, once they've graduated and while they're looking for jobs, we provide them with 3 months of online professional development through thoughtbot's Learn Prime so that they can keep sharpening their skills.
Can you explain the relationships that Metis has with partner companies? Who are some of your partner companies?
Jason: Some of our Hiring Partners include: CoachUp, Constant Contact, Iora Health, LevelUp, WegoWise, 3PlayMedia, Wistia, and of course, thoughtbot and Kaplan. We'll be adding many others in the near future. Hiring Partners are not required to hire our graduates, but they do agree to attend and interview graduates from at least one Hiring Day and then they give us feedback on the graduates. We give the hiring partners our students’ resumes and code samples, and we work to understand their needs and optimize our curriculum accordingly.
Do you get a recruiting fee for placing a student with a partner company? Does the student get a tuition refund?
Jason: Yes, we get a 20% placement fee, which is paid within 30 days of the student’s start date. A student who is placed and stays at a Hiring Partner gets a $2000 tuition refund.
Tell us about the Boston tech scene- why does Boston need a bootcamp right now?
Josh: The main reason this is our first location is because thoughtbot is headquartered in Boston, and we’re close to New York, where Kaplan is located. In addition to that, Boston has a great tech scene. We have an awesome Ruby group in Boston and we have two Ruby events each month, which are great opportunities for students to network. There are usually announcements with multiple companies hiring at both of these events.
Course Report is featured on the Kapor Center blog!
Coding bootcamps are producing graduates that enter the workforce almost immediately, so their approach to recruiting and retaining students from underrepresented backgrounds may quickly start to define, and potentially diversify, the landscape of the tech industry. One way to reach out to potential students is through scholarship programs. Check out the full article on the Kapor Center blog to see a full list of coding boot camps currently offering scholarships specifically to underrepresented minorities.Continue Reading →