Metis - CLOSED

Pros:
- 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... Read More 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.

Cons:
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 was cautiously optimistic when starting Metis. I had heard of these bootcamps but didn't know too much. I chose to the pull the trigger because some close and trusted friends had completed similar bootcamps and encouraged me to go for it. Metis blew... Read More my expectations out of the water. I loved it. Each project folded in a new mathematical machine learning skill with a pragmatic programming skill. I learned a ton, the instructors were wonderful, and everyone involved was a ton of fun. The program was definitely intense. Don't expect to have any life outside of Metis. But it's only three months and if data science is where you want to be then it's totally worth it.

Metis fosters a collaborative cohort style of learning in which students get practical experience of the data science work flow. Students manage a shared git repository, do independent research projects to present, and finish and present 5 data science... Read More projects covering ETL, linear regression, supervised and unsupervised learning, NLP, and more.

Prior to April 2017, I had a bachelors degree in cultural anthropology and no professional work experience. I graduated college in 2015 and spent a couple years "figuring it out", taking every MOOC under the sun and teaching myself how to program. Somewhere in those two years between college and Metis I decided I wanted to pursue data science and pushed full speed ahead on self education. I loved the free online resources out there, but everything seemed so safe and contained. I could work with the Iris dataset and read documentation to do what courses instructed me to do, but give me anything abstract and I was at a loss. I decided I needed a more structured and in person training and landed on Metis. Now I am a junior data scientist working at an exciting young company here in Chicago and, most importantly, I've like I was well prepared for this position.

The instructors were sensitive to the needs of the students, but also sure to challenge you. They weren't going to let you fail, but they also weren't going to hold your hand along the way. It's a rigorous curriculum with the 5 projects ranging from 1-4 weeks in duration. The projects start structured and slowly become more free-form until the final project where the only instruction is to do something interesting that you're excited about with data science/machine learning. Having the practical experience really gave me a leg up in interviews, it gave me a means to turn a soft-science background with no professional work experience into a conversation about the interesting things I have done with data.

The staff is supportive even after Metis ends. While I was looking for a job after graduation, I was connected with some wonderful people in the DS scene through the Metis staff for informational interviews and often introduced to HR reps for jobs I was interested in. Metis provides coaching on networking and hosts events during and after the cohort to keep you connected and help you shake the right hands. At no point is this process an easy one, but if you're willing to put in the work I promise it's worth it.

About a year ago, I was in your position. I was reading reviews about data science bootcamps and weighing my options. At the time, I was a full-time lawyer (no formal STEM background), watching MOOCs in the evenings and teaching myself python on the weekends.... Read More 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 12 weeks, 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 in Seattle and came in with an advanced degree in neuroscience with bench science experience (no coding!). Do the prework and do not procrastinate on that! My programming skills weren't as strong as the rest of my cohort... Read More 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 (eg digging through 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 take those skills to the next level.

I am giving the curriculum less than 5 stars because while there was great coverage for 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... Read More 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.

The number of skills gained through the cohort is immense and had a lot of depth in the teachings.
The support was great and the instructors were available and approachable at every step.
The goal defined for every project was clear and the tasks, though... Read More initially seemed unattainable, were being met with a lot of support and individuality.
The soft skills required for sustaining in the industry were focused immensely.

I took the d3.js course with Mollie Petit and I have to say that Mollie was a great instructor. She was really helpful and positive throughout the course and provided a lot of resources for additional learning. I learned a lot both about data science... Read More as well as the practical application of d3.js.

The only thing I wish was different was that we could have a bit more time and/or the class was more spread out so as to allow for a slower learning pace.

Overall the course was great and I would highly recommend the class for anyone interested in data science or d3.js specifically.

After several years of working as an analyst at various tech companies, I decided to explore the world of data science because I didn't feel that I was being challenged in my daily responsibilities and didn't have sufficient guided opportunities to learn... Read More more advanced techniques on the job. This began with preparatory MOOCs to build my Python skills followed by more in-depth courses or 'specialization' tracks that focused on data science. While I found plenty of worthwhile material online, staying engaged and motivated in the virtual setting was surprisingly difficult. Some classes were too easy and others too hard, and it's not that efficient posting on message boards whenever you have a question. I think most people would agree that in person education is more fulfilling.

I researched a bunch of in person, part-time, intro to data science programs and kept returning to a few well-known ones in San Francisco. I even had the chance to visit their locations to get a sense of what the environment was actually like on a random day. Looking for something more intimate, I was turned off by a couple of the larger providers even though their buildings and classrooms were impressive. Having worked at a small company that rented a co-working space, I found the busier and flashier places more distracting. I also weighed the curriculums and selectivity of the programs. Metis seemed to be the goldilocks option for me, not too big and not too small, with an earnest focus on top-notch teachers in the industry, challenging coursework, and project-based application of knowledge. I thought that maybe Metis' Intro to Data Science class would be easier given my exposure to those MOOCs. I was simultaneously right and wrong; I had confidence going in, but quickly realized that Metis offers way more than a superficial or cursory experience.

There is so much material that one can dive into during this Intro to Data Science course that I honestly think it's overwhelming (at some point) to most people. But it was a great thing because I developed a lot of respect for what data science is and where it's going. You're provided a lot of outside resources to prepare for each class, and the interactive teaching style provides effective guidance combined with the unique perspective of your instructor. Following each class you will have even more to dig into. This course is what you make of it - if you do the bare minimum it may not seem a whole lot different from cruising through a MOOC (no one's gonna kick you out). However, the breadth and relevance of subjects covered at each stage offer innumerable opportunities to develop a deeper understanding that could mean the difference between being merely familiar with data science and making legitimate progress towards expertise in the field over several years. What Metis does well is expose you to this world while providing fantastic teachers to help you break through the complexity and understand the how / why / when of data science that applies in real world settings.

This program is just an introduction, but it's an excellent one that will inspire you and build skills that you can put to use right away. Don't believe anyone that tells you it's possible to become a 'true' data scientist in 6, 12, or even 18 weeks. One of the most powerful things my Metis instructor said was about why aspiring data scientists tend to stumble when they get to job interviews. If you don't dedicate the time, ask questions when you're out of the loop, confront the areas you find tricky, or aren't honest about what you don't know - it shows. Metis' teaching style is highly conducive to the passionate learner, and I recommend the in person Intro to Data Science program to anyone who's eager to get to the next level, with some hard work of course. I'm so happy with the course that I'm now enrolled in Metis' full-time Data Science Bootcamp, which will be quite the ride.

The Data Scienct Bootcamp is the most rewarding learning experience I've ever had. Super knowledgeable and patient instructors to guide us through each step of process. Amazing career advisor providing practical and professional career advice. I was struggling... Read More a lot in the beginning, overwhelmed by the large amount of learning materials we were exposed to. But it turned out to be fine. It's hard to believe that so much can be learnt in a short 3-month period, but it's true and it can be done. Metis is where I found myself consistently pushing my limit, learning more new stuff, and becoming better at learning, which is a key skill for data scientists.