Metis offers data science training via 12-week full-time immersive bootcamps, part-time online bootcamp prep courses, and corporate training programs, with campuses in Chicago, New York, San Francisco, and Seattle. The Metis Data Science Bootcamp is designed and taught by industry practitioners and covers Python, Bash, algorithms, linear regression, machine learning, NLP, databases, and interactive data visualization. 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 Hadoop, Hive, and Spark. The data science curriculum is delivered through project-based, hands-on, collaborative learning and Metis provides students with on-site instruction, and access to speakers, mentors, events, and job support.
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 a free, self-paced Admissions Prep course for those who need to brush up on their linear algebra, calculus, probability, statistics, 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. Metis is authorized to enroll international students with M-1 visas across all U.S. campuses, which allow non-U.S. students to attend technical and vocational programs. International students who are already in the U.S. on an F-1 visa may also transfer to Metis. Veterans may apply to use the Post-9/11 GI Bill® to develop their data science skills at the New York City bootcamp.
Graduates leave fully qualified for data scientist jobs, with placement programs available. Students receive mock interview training, visit company offices, present their projects to employers at Career Day, and have access to an extensive network of speakers, mentors, events, and ongoing career coaching and job placement support.
Recent Metis Reviews: Rating 4.88
Recent Metis News
- July 2019 Coding Bootcamp News Roundup
- Compare Data Science Bootcamp Costs
- How to Land a Data Science Job in Seattle
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
- September 23, 2019
- Class size
- Minimum Skill Level
- Absolute Beginner
- Placement Test
More Start DatesSeptember 23, 2019 - OnlineApply by September 23, 2019
- Data Science, Git, Python, SQL, Hadoop, Machine Learning, Algorithms, Data Visualization, Data Analytics , Artificial Intelligence
In PersonFull Time40 Hours/week12 Weeks
- Start Date
- September 23, 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
- Some experience with programming and statistics.
- 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 DatesSeptember 23, 2019 - SeattleApply by August 19, 2019January 6, 2020 - SeattleApply by December 2, 2019September 23, 2019 - New York CityApply by August 19, 2019January 6, 2020 - New York CityApply by December 2, 2019September 23, 2019 - San FranciscoApply by August 19, 2019January 6, 2020 - San FranciscoApply by December 2, 2019September 23, 2019 - ChicagoApply by August 19, 2019January 6, 2020 - ChicagoApply by December 2, 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
- September 9, 2019
- Class size
- 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
More Start DatesSeptember 9, 2019 - OnlineApply by September 9, 2019
83 reviews sorted by:
- Only Applicants, Students, and Graduates are permitted to leave reviews on Course Report.
- Post clear, valuable, and honest information that will be useful and informative to future coding bootcampers. Think about what your bootcamp excelled at and what might have been better.
- Be nice to others; don't attack others.
- Use good grammar and check your spelling.
- Don't post reviews on behalf of other students or impersonate any person, or falsely state or otherwise misrepresent your affiliation with a person or entity.
- Don't spam or post fake reviews intended to boost or lower ratings.
- Don't post or link to content that is sexually explicit.
- Don't post or link to content that is abusive or hateful or threatens or harasses others.
- Please do not submit duplicate or multiple reviews. These will be deleted. Email moderators to revise a review or click the link in the email you receive when submitting a review.
- Please note that we reserve the right to review and remove commentary that violates our policies.
Click here to log in or sign up and continue.
About a year ago, I was in your position. I was reading reviews about data science bootcamps and weighing my options. At the time, I was a full-time lawyer (no formal STEM background), watching MOOCs in the evenings and teaching myself python on the weekends. I attended Metis in Chicago during the spring of 2017. Within a month of graduating, I was recruited by a growing startup. Since then, I have been prototyping new tools, working with engineers, and enjoying the tech industry. This transition would have been impossible without Metis, its encouraging staff, its supportive instructors, and the genuine connections I made through Metis.
Having worked in my new profession for the past 6 months, I have a different perspective and new appreciation for my experience at Metis. The curriculum is rigorous, but well-structured and current. Our dedicated instructors, senior data scientists Zach & Seth, updated our lessons to tailor them to the needs of our cohort. With this foundation, I have had no problems learning other algorithms and tackling new projects at work.
Not only did Metis teach me to think like a data scientist, but more importantly, it also taught me to be a data scientist. Specifically, my transition from the bootcamp to my job was almost seamless. The bootcamp is full time 9 to 6, emulating the work day of a data scientist. We did standups, pair problems, completed projects in short deadlines, and presented our findings. Although, I wish we had dedicated more time to unit-testing and other “best practices” for collaboration with engineers, the instructors appropriately incorporated practices inspired by their own experiences and those common in the industry.
While the instructors well-prepared me for my new career, it’s the dedicated staff, program manager Nathan and career advisor Ashley, who got me this career. Metis regularly held events, hosted speakers, and organized the all-important Career Day to expose the cohort to all of Metis’ corporate and employer contacts. (I met my boss at an open house!) All the career workshops, including the essential mock interviews, were so well organized and thorough I was in fighting shape to apply, interview, and negotiate for my job.
Metis is the “marathon” of career transitions—intense, exhausting, longer than I thought I could handle, but when it’s over, so rewarding. After a quick but demanding 3 months, I left Metis with friends, a taste for junk food (so much free pizza & cake), mentors, an impressive portfolio of 5 projects, skills that I will be able to leverage for the rest of my life, and entrance into the most exciting industry.
I was part of the first cohort on the Seattle campus and came from a neuroscience background with bench science experience. To echo other reviews here, Do The Prework and do not procrastinate on that! My programming skills weren't as strong as the rest of my cohort and I likely spent more time on the prework as well as weekly HWs. The curriculum is standardized across campuses and the instructors prepare quite a bit to bring the material to life. I didn't even mind when we ran into technical difficulties when code failed or during installation days because it's a peek into real-life debugging a stack trace and troubleshooting.
With the bootcamp being project-focused, you are allowed to be as creative as you want. You will learn to fail fast and to love (or accept) the MVP. Between lectures, HW, and projects, this is a very intense, immersive program with regard to your time, and mental and emotional energy. During these 12 weeks, I'd say I spent an average of 12 hours a day on activities relating to the bootcamp, which includes attending Metis-sponsored speaker talks and regional networking events, so the access to resources is awesome. And in the end, it does make the payoff feel incredible.
My classmates were and are a valuable source of support. The staff is responsive and quite supportive and it's not that campus staff only interacts with their "home campus"; it's nice that you can learn from different career advisors and instructors if necessary. The alumni network is also a great source to bounce off ideas, learn about resources, keep up to date on job postings, and be silly!
What I also really appreciated about my Metis experience is that they help you not only learn new skills and how to apply them, but they helped me (re)discover skills that I enjoy and am good at, helping me polish those skills.
I am giving the curriculum less than 5 stars is that while there was great coverage to ML, there wasn't much time devoted to (advanced) SQL. A lot of data will already be housed in databases (at big, established companies) and relies on accessing it, rather than scraping it from outside sources. Talking to the next cohort, I think they remedied that, so 5 stars to listening to feedback!
Before: I had a bachelors in engineering and a law degree. I had worked in a number of different fields and didn't find a job that was fulfilling. I wanted to work on machine learning and NLP, and a bootcamp seemed like the fastest way to get there. From my research Metis seemed to be at exactly the right level for me. I didn't have an applied math Phd, but had a good amount of technical experience and math abilities that I wanted to take further.
Application: I had plenty of background in calculus, stats, and linear algebra, and some python experience. The application was challenging and multi-faceted, requiring math, coding skills, and some product sense/presentation skills as well. Looking back, it was important to have a competitive admissions process, as it allowed us to establish a baseline level of knowledge and hit the ground running on the first day.
Prework: Definitely do the prework. Unless you have been working as a data scientist before the bootcamp (unlikely if you are in the bootcamp), you will need to do the prework. I used it as a reference during the bootcamp as well. The more deeply you get the prework, the more time you can spend during the bootcamp on your project and more complex concepts.
Bootcamp: Full-time for three months. Days start with a coding challenge, followed by lectures, followed by project time. 5 projects in the bootcamp and a presentation to go along with each. Blogs were also encouraged, but not required for each of the projects. Classmates were diverse, thoughtful and supportive - in the 14 students we had, we had students straight out of school, and Phds who had worked in research for several years. The instructors were knowledgeable and made themselves available after class hours to answer any questions. There is definitely a lot to learn, but I found that there was a good balance between the theoretical, the practical, and practicing communicating using data with a business person/colleague/potential employer. Beyond the assigned work, nobody will be pushing you to go above and beyond, though, so you definitely get more out of it the more you put in.
Career support: Great career support that led to lots of positive effects (more interviews, more positive feedback from employers) as I am going through the job search process. The alumni network is very helpful in the job search as well.
Job: It's only been a few weeks since the bootcamp finished, but I have a good number of leads at companies that I would have been ecstatic to work at before the bootcamp started. When I interviewed, I felt technically prepared even when it was with teams staffed entirely of Phds. I'm confident I'll find a fit in a position I'll be very happy with. I don't think it's reasonable to expect any three month bootcamp to be a silver bullet that will get you a job at Google, but Metis definitely made a huge difference in focusing me towards an exciting data science career.
Before: Psychology major with eclectic work experience for the six years since I'd graduated. Decided I didn't have enough concrete skills/experience to land a job that would be challenging and interesting enough, and I didn't trust myself to learn on my own. Decided on a bootcamp because of the structure and network it would provide, and considered data science because it seemed more niche than coding.
Application: I wasn't set on attending Metis, but when the application was harder than I expected, I was determined to get in. With virtually zero coding experience, I had to learn everything on the spot, and honestly that application was the most empowering weekend of my life. And I got in. Whoo!
Prework: I worked right up until the bootcamp started, so it was challenging to finish up there while completing the prework (I enrolled three weeks before the start date). However, the application plus the prework meant that I was prepared for the bootcamp. Would have been really hard to do the bootcamp without the prework.
Bootcamp: Full-time for three months. My boyfriend was annoyed that I was always working on projects, and I probably would have done better on my projects if I'd been single, but alas. Classmates were very supportive (I didn't experience any competitiveness between people). My cohort was unusually small (19 people), which was nice, and the small size of Metis overall meant it felt very personal, but the WeWork environment provided more people to chat with, ping pong, and coffee. Class material was great, and A LOT. Every day we "learned" something that I could have spent at least a week playing around with. There's really too much to learn, and the hardest part of the bootcamp was figuring out how to prioritize between projects, challenges, reviewing lectures, doing the extra readings, and experimenting on my own. The projects were challenging but essential for learning and also provided good presentation experience. Although both of my instructors were very knowledgeable and eager to help, the quality of instruction varied; I think Metis needs to spend more time preparing instructors (because intelligent people don't always teach well, especially not on such a wide variety of tools). Daily pair coding was awesome for the learning and the social aspect, and occasional cohort+staff activities were nice too. Definitely not an easy program! But like most things, you get what you put in, and of course YMMV.
Career support: Awesome career support throughout the bootcamp and beyond. Headshots, resume help, mock technical and non-technical interviews, speakers from the field, etc. Luckily I got a job pretty quickly and didn't need that much help after the bootcamp, because I think supporting the current cohort and the one that just graduated is too much for one person and she was sometimes hard to connect with once the new cohort started.
Job: I got a job! It's a data/backend engineer for an advertising/marketing agency. I don't use any data science per se, but I LOVE my job and I definitely could not have gotten it without the coding I learned at Metis and the opportunity of Metis Career Day, where I met my current manager.
Feel free to call/text me to hear more! 917-583-5942
Before attending Metis, I was a postdoctoral researcher in chemistry who was not feeling academic research anymore. I took some online classes in data science and thought it seemed like a cool and interesting field, but online courses can only take you so far. Also, I lacked enough connections to actual data scientists to really get a job in the field. I got into and applied to several different boot camps to help me bridge this gap, but I really appreciated that Metis did not try to oversell me on my prospects afterwards. Some of the other boot camps could really learn from this no nonsense approach.
Metis helped me bridge that gap between academia and the tech industry through both the data science cirriculum and career workshops. The cirriculum starts out much more lecture focused, but quickly turns to applying that knowledge to actual projects. Lectures include both descriptions of the math behind various algorithms and programming demonstrations showing practical programming/computing skills like web scraping, SQL, and using AWS. Career-wise, weekly workshops helped you to get started on improving your resume, LinkedIn profile, and networking strategies. I felt it was extremely focused and helpful, if a bit intense! (they don't call it a boot camp for no reason.)
By the end of the program, you have a portfolio of projects covering the major areas of data science (Supervised Learning: regression and classification, Unsupervised Learning/Natural Language Processing) and your final passion project. These let you start networking with other data scientists in the field and give you something to talk about!
The other people in my cohort were also wonderful. It was a pleasure to interact with people of such diverse professional and personal backgrounds. I felt like I not only met a bunch of professional contacts but also some great new friends.
The boot camp just ended last week, but I have met so many new contacts that I don't think it will be too long to find the right position. The boot camp also offers indefnite career support and lets you return to the office as much as you need. Even a lot of the alumni who already found jobs come back on a regular basis and offer career/interview advice if you ask.
The only thing I think could be better about the curriculum is that there could have been more feedback about projects/challenges. It seemed like it came a bit late or inconsistently. Also, we went over the basics of Big Data tools like Hadoop/Spark, but it felt a little out of place since we never used it.
The one last thing I would add is that this is called a boot camp for a reason. Be sure that you are mentally and emotionally prepared, because while it was a wonderful experience, it was a very intense one. I'm glad that my cohort was such an amazing, positive, and passionate group of people to help me (and everyone else) get through this experience.
Prior to attending Metis I was a data analyst for a market research company. At the time, I was ready to leave my company for a new position, and so I conducted a search for new analyst or data scientist roles. I had trouble finding a good opportunity, and I found that I wasn't competitive for the data scientist positions to which I applied.
Metis helped me change that. After 3 months of bootcamp and a couple more of job searching, I got a GREAT data scientist position.
Metis is valuable because you learn a lot, and you apply that knowledge to concrete projects that show organizations what you can do. You should end the bootcamp with presentations, github repositories, and blog posts that form a portfolio to help you get hired as a data scientist.
The Metis network of alumni, staff, and companies that often hire Metis grads is also indispensable in hooking you into the data science scene and making opportunities known to you.
To comment on the day-to-day aspects of the bootcamp, the curriculum is full of useful material, and each morning's pair programming assignments really complement the material that we learn in the lectures. My one complaint here is that some of the algorithms and code examples were not explained very clearly in the lectures; I think specific lectures could have been a lot better. That said, lectures are where the learning starts, but then you continue by using the material and asking follow-up questions, so there's always opportunity to understand what you didn't before.
The instructors were great. They're very knowledgeable and always willing to help. During my job search, one of the instructors volunteered to help me prepare for a presentation that I had to give in an interview. Another instructor agreed to drill me on algorithm questions for an hour in preparation for another interview. I'm super grateful for their help.
Lastly, the individual career coaching was incredibly helpful. It's just great to have an experienced person giving you advice and helping you practice for interviews and whatnot. My only gripe here is that the career coach seems like she's got a few too many balls in the air at one time, so I had to be more aggressive about following up on certain things. On the upside, Metis seems to be very interested in feedback and making improvements, so I expect that issues will be ameliorated over time.
All in all, Metis was well worth the cost. I was able to make the career leap into data science that I wanted to make, as well as meet a great group of friends and colleagues!
I finished the Data Science Bootcamp in April of 2017 and it was a great experience! Making a career change is difficult and Metis did an excellent job of preparing me to enter the field. The technical skills and network I built have been invaluable and certainly worth the time and effort the program demands.
If you're interested in reading a more in-depth review, check out my blog post.
I completed Metis' data science bootcamp in April of 2017. Overall, I had a fantastic experience and learned a ton. Don't be fooled by the "bootcamp" style of learning. The curriculum is rigorous and the coursework moves quickly. You'll do well if you come prepared to put the work in. I wrote a full review of my experience at Metis on my blog: https://wellandwired.com/blog/metis-data-science-bootcamp-review
After completing the program I started a job at Amazon and have had the opportunity to be a part of some amazing and impactful projects!
- The bootcamp provides sufficient (elementary) overview of fundamental machine learning algorithms, at least enough for entry-level data jobs. However, I’d advise future students to also spend some time learning from external resources (books, blogs, etc.) to get a deeper understanding of each concept.
- If you intend to join the bootcamp, look up the curriculum ahead of time and think about what kinds of projects you might want to tackle based on your interests.
- Unless you already have previous programming experience, it would be good to learn python programming and other CS fundamentals yourself.
- Just keep in mind that career transitioning is not as easy as you might imagine. If you are trying/planning to do it, I think your quickest way to getting a DS role is to leverage the domain knowledge you have from your previous career and then implement DS/ML into your field. Also, I learned that becoming a data scientist is more like a marathon, rather than a sprint.
Metis is an exceptionally well-run program whose staff is stocked with talented, dedicated professionals. The program is fast-paced and intense, covering both breadth and depth across the data science topic spectrum. The quality of students is also exceptional, which provides an amazing secondary learning opportunity.
I was impressed by how well the course was designed for people at many levels. Since it is a project-based curriculum, you were free to bite off whatever you felt like you could chew. For example, those who were CS majors could dive a little more deeply into new packages/techniques since the coding wasn't as much of a hurdle, whereas those with less coding experience could stick with what we discussed in lecture. I loved the balance of project time and lecture time and felt like it was the best way I've ever been able to learn.
It is fast paced, and to grasp everything that is discussed would be a challenge for almost anyone. However, you get a great understanding of the topics/techniques that you choose to use in your projects. Interviewers know you wont know everything, and simply want to see that you have a deep knowledge of techniques that you actually implemented.
I learned a lot from my classmates. Everyone was always willing to help others if they felt like they had more experiences with a topic. I think the admissions process selects for people they think will be successful working data scientists, which includes traits of personability and teamwork.
The careers team was so knowledgeable and supportive. The instructors were so smart and invested in your learning. I learned more in these 3 months than I did in any year of college (technically and about networking/job search/careers) and I enjoyed it much more.
I wouldn't have been able to revamp my career without Metis. They were able to teach alot of different concepts fairly in depth (within reason for a 12 week program) and their focus on 5 projects I believe is the difference maker for Metis. It allows you to show employees you are relatively ready to perform well for their company. The extra job assistance post Metis was better than what my University had to offer and I ended up with a solid job after Metis. I highly recommend this program to anyone who is trying to break into the Data Science industry.
Overall, I'm glad I did Metis. I would not have gotten the job I did without it.
Going in to the bootcamp I was unsure of my decision to enroll. A cursory Google search of coding bootcamps yields a mixed bag when it comes to tech professionals' views on them. A lot of people see bootcamps as a cash grab that churn out low-quality candidates. I was worried about being stigmatized for having a bootcamp on my resume.
For the most part, hiring managers are open to bootcamp graduates. This isn't universally true, but it wasn't a huge hurdle when it came to the job search. The biggest hurdle is the fact that you presumably won't have relevant experience on your resume. In several cases, I felt like I had met the technical bar for a position but was passed over for someone with more experience. This is likely to be a reality in your job search, but it doesn't make getting a job impossible.
That being said, I do believe there is a soft ceiling for jobs you'll be competitive for based on your background. It's not easy to get the Data Scientist title without a graduate degree. Even the more advanced Data Analyst positions seem to be pretty consistently filled by people with an M.S. in a STEM field. The point I’m trying to make is this: if you’re coming in with very little technical experience and no graduate degree, you’re realistically going to be shooting for a Data Analyst role, not a Data Scientist one.
There’s nothing wrong with that though! Getting a Data Analyst role then moving to a Data Scientist role in 2-3 years is a tried and true path in to data science.
Anyways, that’s enough about the reality of the job search. The bootcamp itself was actually a very fun experience. The project-based curriculum is effective in both its delivery of the content and in setting you up to get a job. Being forced to come up with a solution to every problem you encounter is the best way to learn. Prior to the bootcamp I found myself giving up when I encountered something really hard. Having a deliverable and a deadline keeps you a lot more accountable than self-study.
I was expecting to be miserable for the three months of the bootcamp, but it really wasn’t that bad. I’d expect to be putting in 50-60 hours a week, but your days are quite varied. Your mornings are spent doing pair problems and lectures, and your afternoons are spent working. You have 4-5 hours to work on projects every day when you’re on-site – you would be surprised what you can accomplish over a few weeks when you’re throwing that much time in to projects.
The career support was wonderful. Marybeth was super helpful and supportive for all my job needs, but you need to meet her halfway – the career services are not your concierge job lead service.
I’ve come so far in the 6 months since I started the bootcamp. If you enroll, expect lots of personal and professional/technical growth. To be honest, I don’t think I was capable of being as serious as I needed to be about moving in to a more technical job. Metis gave me the structure and help I needed.
One final word of advice: you really do get out of Metis what you put in. This doesn’t mean that you have to go super far out of your comfort zone in terms of the effort you’re putting in, but it does mean you have to be serious about getting in to Data Science. When you encounter problems, you need to be willing to troubleshoot them yourself rather than immediately ask for help. You need to be willing to hold yourself accountable for applying to jobs. The job search can be really tough and downright demoralizing, but if you have persistence you will eventually get where you want to be. Overall, I highly recommend Metis.
Core data science (and I mean rigorous statistical analysis and predictive modeling), felt so out of reach before Metis. There's so many resources for learning Python and SQL out there (Udacity and Mode Analytics being my favs, respectively) - that I wanted material and curriculum from a bootcamp that matched the cost of attendance. In 3 months, Metis made it worth my while - enabling my growth from pivot table repots in excel and simple sql queries all the way to Natural Language Processing on unstructured text and Deep Learning on Cloud Deployments.
The bootcamp option isn't for the faint of heart - and my personal thirst for challenge and rigor was satisfied. The immersive experience was so valuable to me for several reasons:
(a) The "guided tour" experience. There's too many resources for learning data science, and it's helpful to have experienced professionals vet the resources for you. Part of being a good data scientist is knowing what you can down prioritize for learning later. By immersing myself in the best practices and skills that Metis instructors have determined most useful on the job, I generated plenty of leads and had secured an industry role within 3 months of graduating the bootcamp.
(b) Legitimized work experience. It's credible to potential employers to see accredited Metis experience on my resume, with work references from my instructors and cohort members to further validate my experience to potential employers. Whenever a potential employer asked me to list references for someone I worked with, I had a list of capable data scientists that could vouch for me. This set me up for plenty of technical assessments and technical interviews.
(c) Connections to a learning network. Metis alumni are growing with every cohort! Data scientists are a minority compared to Software Engineers, and I was told early on that it wouldn't be unusual to work alone or on small teams on the job. That said, over a year later I'm still leaning on the community to keep my updated as data science tools and methods improve. Post-bootcamp, I learned so much more from my cohort and other alums from soliciting help and resource sharing. If I had done all the same projects by myself, then I wouldn't have had the breadth of exposure or network to be as successful a data scientist as I am.
I can't stress enough how worth it this experience was for me.
I completed my Metis bootcamp in December 2018 in Seattle. What an amazing 12 week experience. Every person in the 16 student group was smart, committed and caring as were the Metis staff as well. We were a diverse group in ages and backgrounds but everyone was there to get the most out of the 12 week experience. We were together in the classroom from 9am-5pm, 5 days a week. That made for an amazing energy.
We learned a lot but it's bootcamp and the information comes at you fast. This meant we were on a very steep learning curve which is pretty exciting. Be prepared to be all in for the 12 weeks to get the most out of your time at Metis and then spend time afterwards learning the stuff you missed along the way.
I attended Metis in the summer of 2018 and I could not be happier with my Metis experience. The instruction was state of the art and the staff was accessible, helpful, and supportive.
Probably the most important feature of a data science bootcamp is the quality of the instruction and Metis instructors are excellent. One of my instructors had relatively a stronger background in computer science and the other a relatively stronger background in math and they complemented each other perfectly. The classroom culture was collaborative and supportive. Learning from peers was highly encouraged and practiced daily. Every morning, we completed a pair programming exercise; this was an incredible opportunity to learn from peers who came from diverse backgrounds and had incredibly advanced and varied skillsets. Moreover, updates to the curriculum are continuously being implemented such that the curriculum stays on the cutting edge of data science.
I also found the way the curriculum was structured to be highly effective. The curriculum is project-based and covers topics from exploratory data analysis, to regression to classification to unsupervised learning and finally to a passion project of students’ choosing. Students complete a project for each of these units. Metis does not provide the data sources or provide students with a pre-packaged template for the projects. Students have to go out and scrape their own data or otherwise obtain data from publicly available sources and determine (with guidance from instructors of course) what direction they want to take their projects. Students’ diverse backgrounds and interests are then reflected in the choice of projects; in my cohort alone, student projects ranged from predicting the stock market (of course), to predicting good locations for cat cafes, to natural language processing of religious texts, and predicting locations and severity of wildfires. I found this project-based approach to be highly effective. In contrast to other data science programs which are centered around only one capstone project, Metis’ curriculum allowed for students to take deep dives into each of these topics.
Career support was also extremely effective. From the careers team, I learned how to update my resume and LinkedIn profile to be consistent with industry standards and even practiced in person and technical interviews. I eventually got my first job as a result of a tip from one of the career’s staff. To me, this indicated just how well the career’s staff knows their students; they knew that this opportunity would be a good fit for me and indeed it was.
Of course, Metis is not cheap but none of these bootcamps are. When I made the decision to attend Metis, I knew that I was making an investment in my future and at the time, I predicted (because hey I am a scientist and scientists like to make predictions) that I would have a strong return on that investment. In the few months that it has been since I graduated I have already had a return on that investment and I know that return will continue long into the future.
Lastly, while Metis was definitely an intense and challenging experience, it was also a fun one. It was extremely stimulating to be surrounded everyday by such knowledgeable instructors and peers with such incredibly diverse skillsets. There were late nights and work on the weekends to be sure, but I also found the work to be manageable. During Metis, I remember telling myself not to let perfect be the enemy of good and I think that advice served me well. The skills that I think are critical for students to succeed at Metis are curiosity, a willingness to try something and fail on an initial attempt, a willingness to iterate, and openness to collaboration. If these qualities resonate with you, I would strongly encourage you to check Metis out. It was an amazing experience and I am so glad that I made the choice to do it.
I can't say enough about the curriculum, staff, and community. I completed my course in Fall 2017, I got a job within 2 months. and have continued to benefit from the community as well as the course resources. My background is in psych research in academia. I got my PhD in Psych/Neuroscience and was facing a bleak job market so I made the decision to transition to data science. It was a rough transition. The bootcamp is not for the faint of heart, but my cohort was comprised of really bright and supportive students (many PhDs and graduates with genuine technical and mathematical prowess) who kept me motivated and on-track. My instructors were amazing and the administrators were incredibly hard working, organized and caring. The curriculum was comprehensive (i.e., a lot so be prepared to be overwhelmed). Regardless of how smart/hardworking you are, you will struggle to keep up. BUT if your experience is anything like mine, you'll be surrounded by people who genuinely care about your success. What's impressed me the most is that a year into finding a well-paying tech job, I'm still very much a part of- and benefit from- the Metis community. I STILL use notes from my session. and my cohort still supports each other's growth and success through referrals and knowledge-sharing. The administrators are still available a year plus into graduating for advice and help because they genuinely care about their students. If I could give this place a 6/5 I would. I really loved my time there and I have a ton of respect for the Metis family.
I two took Metis' online courses:
1. Python/Math for Data Science
2. Intro to Data Science
I thought both were very good and definitely worth the money. I was a complete programming beginner but had done some pre-work using Zed Shaw's "Learn Python the Hard Way" and I had taken one intro Python/Data Science class at Simplilearn (very good too).
Class #1 above was the first Metis class I took. Without my preparation I would have been lost, but the prep I did allowed me to keep up with the programming part of the class. The math I fortunately already knew, but it would have been confusing had I not known it. [If you don't know it, then use the class as a menu for what you need to know eventually]. Interaction with the instructor took place via chat window, but he was responsive to Q&A. The course materials (Jupyter Notebooks) were very through and detailed & I've reviewed and re-reviewed afterward and continue to get value from them.
Class #2 built on Class #1 but got more into techniques of statistical modelling. In this class, we spoke with the instructor (not just chat box), which was very very valuable given the subject matter. I thought the course material (again Jupyter Notebooks) was well structured and clearly showed how to run the models using Python code. The instructor was fantastic, even agreeing to have "office hours" before class and hanging out after class for extra Q&A. Way beyond the call of duty, but it replicated a bit the university environment.
In general, I'd highly recommend either course (and I paid myself, no corporate reimbursement). If -- like me -- you are a total beginner, then do some learning on your own before enrolling. It will be very hard to keep up if you go in cold. They cover a lot of material and go fast. But if you're willing to really try and work hard to learn, I bet you'll find good value and a lot of insight to help you on your path. I did.
* Content: It´s an injection of information straight to your brain as expected. You get to see all of the concepts on Data Science and the stats/math underneath, so be sure to make the prework, and during the bootcamp read more or look for videos on any concept you don't understand very well. There's a lot of self learning needed: instructors will be there to explain things to you, and you can ask for review sessions on some concepts. But it's practicing, researching and discussing with your peers that you'll get everything well understood
* Assignments: The most important part are the Projects (every 2/3 weeks) you'll feel you don't have time for anything, but you'll be amazed on how much you learn and get done in a little time, and that's the point. There are 5 projects: the fist of them given to you, very fast (to deliver in just a week), in group with some of your partners, to get the idea of the bootcamp methodology. The next 3 will be individually and chosen by you, each of them to practice a particular topic, methodology or algorithm (Regression, Classification, Supervised/Unsupervised learning, NLP, Neural Networks...). The last one, your "passion project", also individually and chosen by you, can be almost anything you want with what you learn. So the projects are as hard as you push youlself.
There are also other assignments: challenges (like homework that will help you with your assignments) every week, a blog and blog posts (and blog posting culture) every now and then, and two personal investigations during the bootcamp... So you´ll think its a lot but it´s all well structured: Make them all, because all assignments will help you in your projects that are the most important part
* Instructors: Since you're learning a wide range of concepts: Statistics, Math, Programming skills, methodologies, libraries... You'll also find different instructors that will give a different focus to the things you are learning. It can be frustrating sometimes not to get a straight forward answer, but at the end you'll find out that Data Science has a lot of points of view and you have to learn to find the best fit for you, so that's what the instructors are doing by helping you find an answer and not giving it straight away. One thing they'll always help you and support a lot with is the fact that no project is wrong and you can move on with any crazy idea even if you think it's impossible, and they'll show you ways of seeing things that you'll only get by practice and experience.
* Carrer Support: You'll get very useful workshops: to fix your CV, your LinkedIn profile, your networking and interviewing skills, which is great. There's also people coming over from different companies and Data Science roles to talk about their experience and you can ask them anything! After bootcamp, people from Carrer Support are still in contact with you for help, advice or any questions until you get a job (Or even after, if you need it)
In my case, that's as far as it goes: I was an international student so other support like: visit to hiring companies, salary negotiation skills or networking with hiring partners was not very useful for me (Although I heard it was very useful for my peers), neither do they offer sponsor for a working visa, that depends on the company and I was not very interested at the time. However, back home, with what I learned on the workshops, the tips that they gave me and post bootcamp advice I found a job back home in a couple of months.
* Environment: New York's campus is in a WeWork building, there a lot of areas to study in groups or concentrate individually, it's open 24/7 and there are common areas to meet the instructors or your partners regarding bootcamp topics or chat about anything else.
* Conclusion: Bootcamp experience is great. Personally I think is a little expensive, but totally worth it. You'll get the tools you need to become a Data Scientist and the culture to keep learning and keep growing. If you're an international student, don't expect to get a job to stay in the US and change your life, but you will come back home in only 3 months, having learned a lot of stuff people take maybe a year to learn and succeed.
This review is for intro-to-DS live/online course.
I recently got interested in data science (DS), and I wanted to do a career-transition through the 12-week DS bootcamp offered by METIS. In preparation for the bootcamp, I decided to take the Intro-to-DS live/online course, and I’m glad I did! This course gave me the necessary overview and hands-on coding experience with DS and machine learning (ML) fundamentals. The exploratory analysis techniques I learned in this course helped me tackle the DS challenge given during my (bootcamp) application process.
This Intro-to-DS course is fast-paced and filled with lots of content. The curriculum is structured in such a way that, we could apply the techniques we learn at each session to our project interest and continue to build upon it throughout the course. There was a total of twelve (3-hr) live sessions where we got to learn basic concepts of DS, i.e., math (LinAlg & Stats) and python, supervised- & unsupervised learning, and pretty much everything involved in a typical model-building pipeline. By the way, twelve sessions surely won’t be enough to cover all existing ML techniques today, but at least we got to see a high-level overview of most of them. Each session was held live and online via ZOOM (where the instructor and students could share each other’s screen); these sessions were also recorded so we were able to download the videos after. Course materials (e.g., lecture notes, jupyter notebooks) were distributed via git-hub, so I could keep these notes for future reference. I wished there were more homework sets given to help solidify the concepts we learned in class, but I suppose I could just google search for various datasets and explore them myself.
During the course, we were strongly encouraged to complete a mini DS project. About half the class (including myself) selected a project, worked on it throughout the course and presented it at our last session. This was a good experience for me, as I had never worked on an end-to-end DS project before. I’ve taken a couple of self-paced MOOC’s, but the live (online) classroom setting in this course was more helpful for me, as I somehow felt accountable to keep up with the materials as the course progresses.
I’d recommend taking this course, if you have some programming experience with python and are interested in getting hands-on coding experience with ML. By the way, the tuition for this course can be applied to the bootcamp, if accepted to the program. So, it’s basically a free, pre-bootcamp (warm-up) training.
I would highly recommend this course to individuals who, like myself, come from a traditional science background. I can say without doubt that this program helped me kickstart my career and provide a level of credibility--which is important when recruiters are questioning your lack of a computer science degree. I would think of Metis as an internship, dedicated learning environment for all things data related, and an opportunity to feel part of the bigger San Francisco big data community.
Please feel free to connect with me on LinkedIn if you would like a deeper dive on the program. I don't bite!
Before attending Metis I'd done a one week bootcamp and tried learning more data science by myself but struggled to know who to turn to when I get stuck and also didn't really know how to approach the plethora of resources on the internet. Metis gave me:
a) the skills across a fairly broad base of topics;
b) the confidence and 'knowing how to learn' to go away and learn by myself;
c) access to a brilliant careers team;
d) a community of people to ask for help and support!
You complete 4-5 projects to practice a range of skills and can tailor those according to your interest. This approach really suited me from the perspective of being able to explore something of interest for a limited period of time.
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.