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
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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
- March 11, 2019
- Class size
- Minimum Skill Level
- Absolute Beginner
- Placement Test
More Start DatesMarch 11, 2019 - OnlineApply by March 11, 2019
- Data Science, Git, Python, SQL, Hadoop, Data Visualization, Data Analytics
In PersonFull Time40 Hours/week12 Weeks
- Start Date
- February 25, 2019
- Class size
- New York City, San Francisco, 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
More Start DatesFebruary 25, 2019 - New York CityApply by February 25, 2019February 25, 2019 - San FranciscoApply by February 25, 2019February 25, 2019 - ChicagoApply by February 25, 2019February 25, 2019 - SeattleApply by February 25, 2019
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
- New York City, San Francisco, Chicago, Seattle, Online
- 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 am an architect and a Transportation Planner turned Data Scientist(may be junior) through Metis. That statement itself should say a lot about the bootcamp, but let me go into details. Although I had worked on statistics and modeling techniques for analyzing travel patterns in cities, I was not equipped with the tools and techniques needed in the industry of Data Science and I was very nascent with the coding skills. I was very excited about learning all these, given the kind of change Data Science was bringing to the Transportation industry (basically, I knew the application of the skills that I would acquire). Just now, I work as a Senior Data Analyst(using technologies like Pyspark and Hive) at Apple Maps through Wipro.
Metis has been instrumental in providing me with Data-Science related concepts as well as hands on experience on those skills in quite a short amount of time. The mix nature of every cohort with some similarly passionate, hard working and fun people also helps a lot with growing and learning. I got admitted into other Data Science bootcamps also, but the curriculum of 5 hands on projects really made me more interested in Metis. Although, I had my doubts of not being able to learn so much just in 3 months, Metis has actually shown me that it's possible ! I was learning coding as well as data-science courses online but it would have taken me a lot more time to be able to reach where I am, without Metis. Being gone through those projects in Metis, sometimes I still go through my projects/lectures to help myself in the work.
Paul and Joe as instructors were technically equipped to answer all my questions and help me out through the challenges that I went through in my projects. The way they used to engage with us on Fridays, with all the games and fun made the bootcamp kind of a course easier to go through. One more interesting part, is the alumni network offered by Metis. It is and will always be useful to be connected with Metis people.
Metis could have concentrated more on the time-series analysis and A/B testing part as that is a major requirement in interviews of Data Analysts atleast. Also, I feel that Metis can be more involved even after the bootcamp for may be 3 months to make students have a more guided interview process.
Where one can end up after Metis: 6 months after the bootcamp, my whole cohort was placed pretty well in good companies. I would say, with passion, a lot of hard work, little patience (because getting into the feel of data science interviews to cracking it, takes time and all of this will take time but it is all worth it if you are going to enjoy the work you are going to do), Metis will definitely be able to help you reach where you want to. I won't at all be wrong in saying that it has changed my life in a positive way.
I've always been a math geek, which is what drew me towards the data science space. Having already had some exposure to coding and statistical modeling as an EE major, I realized that self-study wasn't enough to fill in the gaps, and a graduate program in data science would have been excessive.
I spent a lot of time researching different programs, and very few compared in terms of community and level of attention as Metis. Not only did the program do well to fill in the gaps and help to build a well-rounded portfolio, but it introduced me to a huge community of people who are all as eager as I am to learn.
Inevitably there were gaps in the curriculum (it's impossible to cover everything in 12 weeks), but the program and instructors did well to establish a foundation and point me in the right direction to dig deeper. Even after finishing the program I was still learning and working on independent projects with their support.
The career staff bent over backwards to make sure I had all the resources I needed as I went through the uphill battle that is the interview process as a newly-minted data science graduate.
In short, it was a difficult decision for me to go through with the program, but I don't regret it.
I graduated from the Seattle Fall cohort (the second cohort in Seattle) in December. I come from a pure math background, and I'm a recent Ph.D. grad who was pretty nervous about going into data science.
Metis is everything I wanted out of a bootcamp. The project based curriculum works really well, especially since the topics of projects 2-5 are entirely self directed. It's easy to get excited about learning this stuff when you're applying it to a topic you really care about. That also makes it easier to pick yourself up when you fall -- and you'll fall a lot.
The lectures are an excellent introduction to the material, too. I had the opportunity of both learning from the Fall 17 instructors and TAing for the Winter 18 instructors, and all four of them were awesome.
Some quick warnings. Definitely do the prework. Definitely work ahead as much as possible -- everything sneaks up on you, from project MVPs to student investigations. This bootcamp is extremely intense -- I had to defend my Ph.D. in the middle of it, and that was the next thing to impossible. Don't do that. :P
Career resources are incredible, too. The job hunt is a long, demoralizing, frustrating process, and our career advisor made everything go so much more smoothly. I had no idea how much I was doing wrong, or making things harder for myself, when I was trying to figure out everything on my own.
The community of alumni is a great resource. My cohort actually had Thanksgiving together -- not every one will be that closely knit, but you'll definitely make friends and business contacts, and you have the whole network of Metis alums to draw on.
One small gap in the curriculum -- at least for me -- was some fundamental statistics stuff, especially related to experimental design and a/b testing. But the curriculum is evolving, and Seattle is still a young campus. Be forewarned, though, if your basic statistics is a little shaky like mine was, you might need to do some self-study in some areas.
If you can afford it (both in terms of time and money) then Metis will give back so much more than you put in. Highly recommended.
I attended the Fall 2017 bootcamp in Chicago. From the beginning, Metis was very helpful to me. I live in the DC area and the NYC cohort was full, but they helped me come out to Chicago so that I could still participate. They were very helpful (Nathan Vermeiren) with my housing search and all of my other out-of-town needs. I ended up staying right next to the Metis office which worked out great. It was actually helpful for me to be away from home because I didn't have any of my usual distractions/duties to do, so I recommend going out of town. The only downside to going out of town was that Metis was not as connected in the DC area. Metis did continue to support me fully in the job search until I obtained employment and I had no trouble contacting the DC Metis alums who were quite helpful.
The curriculum is great. They spend a small amount of time on many methods and tools used in data science. They are not simply choosing the tools that are 'always done'. I know our teachers went out of their way to have us use the most current tool for the job (like spark vs. hadoop) and/or the best tool for the job. Often they would show us several ways to do something and then we would choose the one that we liked best or were familiar with. One of the many things that make this bootcamp more valuable than sitting in your PJs on coursera, is the ability to ask them questions (more on this below). Another is that you will learn the method/tool and then USE it in a major project, not just a homework. This project will be something that you are proud of and can put on your resume. These projects are the reason I got job interviews. Not my PhD, or my peer reviewed publications, but my ML projects from Metis. The interviewers would ask far more questions about my metis projects and I was able to speak about the methods with confidence and authority because the project required me to know what I was doing.
The timing of the teaching is just right, in that they teach it to you the day before you need to start using it in your project. The best way to learn is to use it right away, and you will. Cloud computing/storage (AWS, Spark,hadoop) and databases (SQL, MongoDB) are other topics where it is valuable to have an in-person teacher. These are things that are difficult to get right when reading a forum because some things will be specific to your hardware or router. It would have taken me MUCH longer to figure out how to use AWS by myself.
Teachers are what makes the Metis experience. Our teachers were Zach Miller and David Ziganto. These 2 are truly great instructors and also great mentors. They are incredibly knowledgeable about all things Machine Learning and Python, and are so patient when you have questions. They also have many informative stories about their experiences when working in data science or interviewing data scientists for jobs. They taught us about what you do as a data scientist, and pitfalls to watch out for. One of the things that they manage to do is push you really hard and enforce difficult deadlines, but at the same time support you. They aren't going to give you answers (like any good teacher) but they will help you get out of being stuck on one thing for too long. They come and sit with us ALL day, every day and were never dismissive or impatient.
I have never had someone be so hard on me about presentations. They have incredibly high standards and it has taught me so much about something I thought I was already good at. I didn't fully understand why they wanted these 5-6 min presentations until I had my first interview and realized that a quick presentation is what you are doing every time you get an interview. It's literally interview prep, w/o labeling it that way. The career advisor Ashley Purdy was a big part of the presentation brigade and she helped me communicate complex things in a less technical way (which is very important for a data scientist). These three never let up on me for a single second and it was perfect.
I paid for bootcamp so that I could get better and that means I need to be pushed into a place(s) where I am not comfortable. I can do comfortable projects on my own. Full disclosure: I did not always succeed when they encouraged me to do things that were harder and that was ok. They were there for me when I failed and helpful about what I could do to deal with it. I know that's not always the case (it's completely possible to fail bootcamp if you are underperforming consistently), but because I choose something that was less 'safe' for me, they took this into consideration, and made sure that I was mentally/emotionally ok.
Ashley Purdy (career advisor) pushed me to do things that I never would have done in my search for jobs and online presence. Metis required me to write a blog, which has earned me more than one interview, and I now enjoy writing posts. Our teachers have even promoted my posts via linked in and data science weekly. Ashley also showed me how to cold call people on linked in and just talk to them about their job (informational interview). This was very scary and really paid off, as one of the people I spoke with got my resume in front of the right people which led to me obtaining the job I wanted! She is there when I have questions about salary negotiation or whether a recruiter is just spamming me. There were also some great presenters that came to talk about what they do at company X as a data scientist. The Metis alumni is a great community and will really help to build your data science contacts.
Metis’ career advisor (Ashley in Chicago) is one of the main reasons I chose Metis over some other bootcamps. Metis is very invested in whether you are able to obtain a job that you love. They don’t just present you to a few employers and say ‘bye’; they continue to make sure that you are structuring your days and applying to places that will be a good fit for you. It was always very clear that they were not just trying to get me to take any job so that they could check a box. In fact, they encouraged me to hold out for what I want rather than take the first thing I was offered.
Zach, David, Ashley and Nathan are also just really great people. They have a great sense of humor and are fun to be around. We had plenty of good laughs together (students+staff) and I know I had a great time while also being stressed out. This is why I always say, it's like grad school (in fast-forward) w/o the emotional abuse. lol
Students were another important part of the bootcamp. Every student was highly motivated and smart. We hall had various areas of expertise and I liked learning from them. It's very much a feeling of 'we're all in this together' and even when I wanted to slack off, I was inspired by them to continue to bring my 'A game' because they operated at such a high level. They were an important part of the high standards that Metis and our teachers set.
This bootcamp is intense. If you have been through a PhD program, it's kinda like the night before you have to send your abstract/poster to the conference committee and you all stay up all night together working on it. Except that's every day for 12 weeks. You will want to spend your weekends on your projects, and come home pretty late (not everyone does this, but I did). So, be prepared for that level of work. It's completely worth it, but not possible for everyone. There's a reason they call it a bootcamp...
In sum, I highly recommend the Metis bootcamp to people who can devote the time. I obtained 2 very good job offers, in writing, within 2 months of the graduation date. I now have a job at Booz Allen Hamilton as a data scientist that is exciting, and I am ready to connect you to my network once you graduate from Metis!
The Metis data science bootcamp is three months of intense learning. The curriculum is a data science survey course covering a wide range of data science topics from linear regression to natural language processing. Each topic is divided into approximately two week segments where you learn about the math and details behind the topic and at the same time, work on a project implementing what you learned.
I attended Metis to make a career change. I was formerly a trader for 11 years. For me, the lessons and coursework at Metis were extremely challenging. I spent every day and night working on projects, debugging code, and reviewing material and still felt over my head for much of the bootcamp. I was putting in 12+ hour days, seven days a week.
Metis especially shines in three aspects. One is the quality of the students. My cohort had 11 people in it. There was a large range of math and programming skills in the cohort. The students with more advanced technical skills were able to take advantage of material and produce more advanced projects than others. Everyone completed a portfolio of projects. Given this differing skill level, the learning atmosphere was collaborative. Every student was stressed and overworked. We used this common bond and help and support each other.
Another strength of Metis is the quality of the instructors. The instructors for my cohort were tough but fair. Their data science knowledge was impressive. They held our cohort to a very high bar. I both succeeded and failed at times, but they were there to guide me on how to improve and make progress.
Finally, Metis focuses on getting its graduates employed. Throughout the entire bootcamp, there is an overarching theme of getting employed. Each campus has a full time career advisor who knows the ins and outs of navigating the data science job market. Upon completing the program, you will have a portfolio of five projects to show employers that you are capable of performing as a data scientist. The instructors gave us insight into what employers are looking for in terms of the interviewing process and job performance. This combination of projects, instructors, and career advisors is how I managed to land job interviews and eventually get hired.
One thing I wish I knew before Metis:
I would have gotten more out of Metis if I had more of an introduction to the machine learning algorithms. Understanding the machine learning algorithms was the most challenging part of the course. There were times where we spent two hours on an algorithm then moved on to the next topic. It was impossible for me to understand this complex new material in that short amount of time.
Before Metis, I was not competitive for any data science positions. I wasn't even competitive to receive an interview.
After completing the 3 month program at Metis, I was competitive for entry level data analyst and data science positions. I received an offer 2 months after the bootcamp ended.
- Metis gives you all the tools for success
- They spend time helping you develop the soft skills necessary to excel in your career (project presentations, public speaking, working in a team, communication)
- They touch base on industry skills that aren't always used in an academic setting (git, open-source programming languages)
- They scratch the surface of many topics -- some might see this as a flaw and make someone a 'jack of all trades, master of none' but Metis provides you all the tools to get started in a specific topic domain -- it's up to the student to really exploit the resources that they provide.
- Job support and alumni network is incredible. After graduating, all alumni from all cohorts from the other campuses are connected and this provides a great network of people to reach out to for advice or help on a specific problem or job opportunities, et cetera. The careers department helps you with everything from resume to salary negotiation to just plain advice on what to do next when you're talking to a potential employer.
- Instructors are some of the coolest and smartest people I've ever met. They're incredibly intelligent and focused on their but so down-to-earth and unpretentious that you would have no idea that they went to MIT or Cornell.
This isn't even a 'CON'. I just wish I knew this beforehand.....
- In my opinion, Metis is BEST for those that are making a career CHANGE. In other words, someone who has already has an established career and looking to get into the data science realm. With data science being such a hot industry, most employers are looking for people with job experience (regardless of the domain). So for people (like myself) who don't have work experience and chose Metis as a substitute for graduate school --- expect to have to work a little harder to land that first job :)
I hope this review helps other people in a similar situation as mine. I came to the 12-week bootcamp with little to no relevant professional experience (I was in software sales beforehand), but willing to do whatever it took to break into a career in Data Science.
If you want to transition career paths like I did, Metis is absolutely the way to do it. I had several interviews with interested employers within days of graduating, with one interview coming just minutes after I presented my final project at career day -- and this all during the holidays, when recruiting is generally slower.
Metis provides the tools and support that are impossible to get for a career-switcher like myself. Yes, you can crack a book on statistics and do some Hackerrank coding -- and you should do those things anyway! -- but that is nothing compared with the legitimacy that I've gained in employers' eyes by investing time and money in the program. Moreover, the career support is excellent and doesn't expire; the Career Services staff have done an outstanding job of building a hiring network that you as a Metis student have full access to, and they give top-notch advice on the minutiae of resume writing, LinkedIn profiles, and so on.
In my opinion, a further vastly underrated aspect of Metis is the strength of the alumni network. By graduating from the program, I instantly have connections to alums that have ended up at major companies in many different industries. Browse the LinkedIn profiles of Metis alumns and you can see for yourself.
I will give two pieces of advice: one for those making a decision about Metis, and one for those who have decided to attend the bootcamp.
My advice in making a decision whether to attend is to contact as many alums as you can via LinkedIn or through their blogs, and ask them about their lives pre-, post-, and during Metis. You will learn a lot about what Metis students are like, and whether you can see yourself as one of them.
My advice for you, if you will be attending, is to take as much initiative as you can to learn what you can beforehand. I personally read and noted "Intro to Statistical Learning," a Machine Learning textbook, and I found that I got much more out of the bootcamp because of that. You may decide that Andrew Ng's Coursera course is more your speed, or maybe something else -- but putting in the work regardless will pay off. The bootcamp is only 12 weeks long, and you can't possibly learn 100% of everything that's thrown at you. Putting in the sweat equity ahead of time will pay enormous dividends if you're not seeing most of the machine learning concepts for the first time.
I financed the bootcamp myself with savings. It is obviously not cheap to attend, and I thought long and hard before finally submitting my payment. But in end the desire to change careers won out, and I have not looked back since. The bootcamp was worth every penny that I spent, even if it was nerve-wracking to commit to at first.
In sum, I would wholeheartedly recomend Metis to anybody that is truly motivated to change careers, even if it is a significant change like it was for me.
I took the d3.js/data science course at Metis with Mollie Petit.
Mollie was really helpful and positive throughout the course. She provided a ton of resources for us to learn throughout the classes as well as after. I learned a lot both about data science as well as the practical application of d3.js.
The only thing I wish was that we had a bit more time and/or the class was more spread out so as to allow for a slower learning pace.
The course overall was great. Mollie was an enthusiastic, positive teacher who was always available for help. I would highly recommend the class for anyone interested in data science or d3.js specifically.
Prior to enrolling in Metis I was a PhD candidate in a subject matter that is generally not seen as related to data science. While I was confident in my statistics background I had only coded sparingly and was completely new to machine learning concepts. On a whim I decided it was time to leave academia and stumbled upon data science as a career option. After debating whether or not to enroll in a bootcamp or teach myself the curriciulum I quickly found Metis and decided to apply, was accepted, and enrolled soon thereafter.
Having received a PhD I was no stranger to learning and was happy to discover that the Metis program design and curriculum was exactly what I was looking for. It was the perfect combination of fun, knowledge, and rigor. Given that the course is only 12 weeks the pace can be relatively quick at times; that said, I felt confident in the material I was learning and truly engaged in the projects I was delivering. At times I felt stressed, afraid, and overwhelmed that the work I was doing wasn't up to par but was continually validated and supported by my classmates, instructors, program manager, and career staff. And for my final project I was able to create an app that combined my PhD expertise with data science and was elated to find merging the two fields was a relatively easy task.
Within about a month after I completed the bootcamp, with the help of the career team, I had multiple job interviews under my belt and received a job offer as a Senior Data Scientist. I can't say enough good things about the careers staff both on site and around the country. In my experience, they were constantly on my side on my job hunt, during negotiations, and even after I accepted the offer.
Without a doubt I am confident that Metis was the right choice for me. While I'm certain I could teach myself to code and some overarching machine learning concepts, I found the classroom experience to be invaluable for me. The opportunity to engage with peers, ask questions, and of course the career support could not be self-taught. If you're on the fence about whether or not Metis is the right choice for you I strongly suggest that you take the leap!
Metis is an absolute gem. The bootcamp was an amazing experience and extremely worthwhile. I felt like I got more out of it than any other educational program I've been in.
What made Metis stand out for me was the support that students received at every step of the way. From the coursework, to the projects, to career help- Metis truly has every student's back. It feels amazing to know that you have someone to turn to who will actually help (or try very hard even if they don't know how). The focus on employability is also very refreshing. It seemed at times that Metis was more urgently trying to get us employed than we were- which I loved! Like a pushing parent- it was what we all needed.
My thoughts in summary: the coursework leaves your thoroughly prepared for a position in data science, the speakers introduce you to an amazing array of applications and directions to head in after the course, and the career help is extremely thorough and personal.
I'm a fulltime software engineering manager with 3 kids. It's challenging to find time at home to level up on a new language so every couple of years I look for a short-term night class to force it. I maintain a metrics pipeline at work and I chose D3 because I was looking to take my visualizations to the next level.
My first impression is that Metis has a very high bar for instructors. This course was taught by Michael Freeman who is a University of Washington professor and a widely recognized expert in the data visualization community.
His creds: https://ischool.uw.edu/people/faculty/mikefree
Michael is someone who can write 300 lines of D3 from memory in 10 to 15 minutes while verbally explaining every detail to the class along the way. By the end of the 15 minute session he's manipuated a huge dataset into a beautiful visualization, answered all of your questions along the way, and taught it in a way where *everyone* in the class could start working on in it in their next lab. For me the teacher makes the class. By the end of the course I felt like it was money well spent.
I am happy to say that I got a lot out of this course -- not only in knowledge and practical application but in inspiration and critical thought. In my experience this class was successful in what it set out to accomplish and I would very much recommend it to a friend or colleague.
It is important to note that the origin of my praise is the teacher himself, Kevin Quealy. His approach to teaching was interactive, casual and paced to maintain the inclusion of those who quickly understood as well as those who needed a bit more time to comprehend. I found myself on both sides of the spectrum throughout the course and felt confident that my questions would be addressed.
I recently finished the Metis Introduction to Data Science class in NYC and I have nothing but good things to say about it. I have been interested in Data Science for a while so I decided to go to a Metis meetup where they went over the curriculum of the class. I found it very interesting but what sold me was the teacher, Sergey Folgelson. He is an excellent and energetic teacher.
The class is intense and moves quickly. At the time of this review it runs every Tuesday and Thursday from 6:30pm to 9:30pm. You may be tired after a long day of work but Sergey will wake you up. There will be exercises thrown in at different portions of the lecture. You will learn a lot if you put in the work. I found that reviewing the notebooks after class on my own and completing the exercises really helped me retain the information. If you expect to just go to class and not do any homework then you might not get much out of it. As with most things you learn by doing.
For me it was a perfect introduction to the field and just made me want to learn more. I loved it so much I applied to the 3 month intensive.If you work with data and are curious about what all the fuss is about Data Science then I cannot recommend this class enough. Best investment I have made in a long time.
I completed the Metis Data Science Bootcamp in SF. There are a lot of skills taught at Metis, but my main take way skills were python programming, building machine learning models, and using AWS (EC2, S3). Post bootcamp career help (mainly via slack) was extremely useful. After 6 grueling months job hunting, I was able to land a job as a Data Scientist at Roche. I still talk to Metis alumni via slack to ask questions and/or answer questions. I definitely recommend checking Metis out!
I completed the data science bootcamp at Metis 2 weeks ago, and I am already missing the rigor and flow that I had gotten into. It is no doubt an intensive program, but the way Metis has structured it, you kind of start loving and enjoying the hard work. These are some of the highlights from my experience:
- The pair programming challenge at the start of each day pumps up your brain in the morning. This also gives you a chance to work with different smart people from the class every day.
- The lectures after that helps you learn a new topic.
- The longer lunch time (1.5 hrs) helps you catch up on some work, or just chat and interact with your peers.
- The investigative presentations that students give after lunch are also very interesting. Knowing your turn will come forces you to think about new and interesting topics - research them and present when its your turn.
- The practice time in the afternoon gives you lot of time to work on your projects and interact with the instructors and TA's one on one.
- The weekly guest lectures give you an insight into the real world. You get to meet real data scientists from different industries, know their stories, how they work and and build networks.
- The project presentations at the end of the weeks/end of every other week were the most interesting part. While you get to experience presenting and sharing your projects with the rest of the class and get feedback from the instructors, you also get to enjoy the projects built by others and overall it becomes an enriching experience.
I just graduated from Metis on April 7, 2017. Today is April 12th and I have already had a total of 5 interviews with 3 different companies - I have 3 more interviews with 3 more companies scheduled in the next two days. If it sounds too good to be true, you might be right. There was a lot of hard work getting to this point. But there is no doubt in my mind that I couldn't have gotten here without the help of everyone at Metis. Let me break down the what I think are the most important reasons to attend Metis:
What you will learn
- Data science is massive. Machine learning is massive. You will actually spend most of your 12 weeks learning about how little you actually know. It's not until the last few weeks that you're able to look back and realize how much you learned. More importantly, you will know the things that you haven't learned yet. Metis sets you up to continue learning and growing for years to come. Before I attended Metis, I had an inkling of what Machine Learning is. Now, I am a full blown addict and couldn't be happier. I didn't even know 100% that it was "what I wanted to do", but now I can't imagine it being any other way.
Who will instruct you
- First off, Metis hires kick ass Senior Data Scientists to be your instructors. Every single one of them has an amazing background and a ton to offer you. The diversity of their team is really astounding and the domain expertise that instructors have, whether its from consulting, nuclear physics, banking, etc, all weaves its way into data science. My instructors really showed me what a wide open playing field Data Science is. Now that I've graduated, I couldn't be happier to have great mentors, contacts, and friends.
Who you will be in the trenches with
- Everyone who is accepted to Metis is up to snuff. Your first week will inevitably be trying to gauge where you are at with other students and battling Impostor Sydrome, but the bottom line is that everyone there is there because they chose to do something different. I don't want to overtalk it, but attending Metis is a life-altering decision and you will always share that special bond with the people in your cohort. Not only will you spend hours and hours and hours together working on challenges and projects, but on a deeper level you know you all wanted something different and better for yourselves. That's a solid bedrock to build lasting relationships.
So what did I leave out? Amazing career workshops, guest data scientist speakers almost every week, building a rock star portfolio of real-world problems, networking events, career day, food and drinks (sometimes but not always), and on and on and on. I didn't want to focus on the career aspect because honestly, if you do the work, if you attend the workshops, if you listen to your career advisor (shout out to Ashley!!), it will happen for you. Are there any downsides? Yes. Did I mention that there is a lot of hard work involved? Metis is a full-time commitment. And when I say that, I mean it. You will eat, sleep, and breath data science. You will bleed algorithms and you will sweat data and you will love it.
I just graduated from the 1st cohort of the Chicago Metis Data Science Bootcamp last week, and I am already getting interviews for these great Data Science positions that I would not be qualified for without the going through Metis at all! The 12 weeks were intense and sometimes even brutal, but I honestly could not have done this much on my own. We learned to code in Python, statistics related to Data Science, supervised and unsupervised machine learning algorithms, and even did 5 projects from collecting data, building models, to presenting the analysis in front of a large group of audiences in just a short period of time. The instructors cared very much about each student's success so we got a lot of support. The Metis staff, especially our career advisor Ashley, were also super helpful to ensure that we would land our dream job successfully after the Bootcamp. We would spend the time to polish our resume, practice interviews, network with companies so that we are ready for the real challenge after the Bootcamp. Overall, I highly recommend Metis to anyone interested in switching career path to Data Science!!!
Overall, I really enjoyed the Metis Data Science Bootcamp experience! I came away from the program with a more well-rounded skillset that gives me a starting point for solving data science problems and additional reach-back to a network of other data scientists. I highly recommend checking out Metis if you’re interested in data science!
If you're interested in more of my detailed thoughts on the Metis experience, including the people, curriculum, location, and resources, check out this post on my blog.
I loved it.
The instructors were really really good, and I had a lot of fun learning all that material. The projects are well structured, and the syllabus is a very good overview of what happens in many (well, at least mine) data science companies. The coursework was a good mix of learning new topics, i.e. lectures, and hands-on work on programming challenges, projects, etc.
I had full career support throughout my job search, even after I graduated from the bootcamp, and I definitely couldn't have found my awesome job without taking this course.
I was part of the spring cohort at Metis Data Science Bootcamp and absolutely loved it. Everyday at Metis followed a similar schedule. We did pair programming in the mornings, followed by lectures, followed by coding. Over the 12 weeks we went through the basics of statistics, data science and data engineering, good programming practices, interview prep and much more. The instructors Bo Peng and Aaron Schumacher were both very experienced in the data science field and that wealth of knowledge definitely shown through in their lectures and their ability to field pretty much any question we could think of between the two of them. I also really appreciated all the assistance we got in the job hunt both in the application process. Lastly, I really loved my cohort. They came from such diverse backgrounds that everyone really added and brought value to the whole experience.
Thanks to the bootcamp, I was able to find work within 2 weeks and am now working as a Data Scientist making a 6-figure salary doing what I love!
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As you’re researching technology bootcamps, we’re willing to bet you’ll come across SQL. We see SQL in the curricula of so many different types of bootcamps, from Web Development to Data Science. With a wide variety of uses, and not many alternatives, SQL is a technology that you’ll arguably need to use no matter what job you get in tech. Jonathan Balaban, a senior instructor from Metis, joined us to answer all your SQL questions: what is SQL used for, which companies use SQL, and how you can learn SQL as a beginner!Continue Reading →
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.
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We learned so much in our webinar with Laurie & Irmak of Datascope Analytics and Metis, including:
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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 →