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!

Pros:
– Amazing instructors. They know both the theory and the practice of the material they’re teaching, and they were always happy to help me work through some weird problem or just figure out what to do next.
– I loved the project-based curriculum, where I had to do everything yourself: pick a topic, find the data, plan everything out, build a model, make it better, figure out how to present it. I could work on stuff that actually interested me and I had real ownership of the product. At the end of the program I had a real portfolio of stuff I was proud of, not just a list of skills.
– Focus on presenting. Two to three weeks of project work needs to be condensed into a four-minute presentation, which is tough! At the beginning of the program I thought I hated presenting slide decks, but after five chances to get better at it, I actually really enjoy it.
– Excellent career support – LinkedIn and resume workshops, mock interviews and whiteboarding practice, great alumni network, and unlimited post-graduation support. They really, really want you to get a job. (I received and accepted a great offer exactly a month after graduating, from a company that saw me present at Career Night.)
– The challenging application process means that all of my peers were very smart cookies. I learned a lot just from finding out what other students were doing.
– Tons of extra material available if you want to dive deeper into any particular topic that interests you.

Cons:
– The rapid pace means that all of my projects felt (to me, at least) a little unfinished – there was always some more I wanted to do, but the presentation deadline always hit so fast! I feel like I could have done one less project if it meant more time to work on the others.
– The focus on preparing for a job means that some interesting topics aren’t covered in as much depth. For example, you’ll learn the basic theory of deep learning and neural networks, but they don’t spend that much time on it, because companies don’t really hire boot camp grads to build deep learning systems.
– The curriculum is constantly under development (which is good!), but that meant that there were occasional inconsistencies, like programming challenges that didn’t always exactly relate to what we were covering that week.

I really enjoyed my bootcamp experience! I was pretty much coding daily, learning new concepts, and implementing machine learning (ML) algorithms into various projects pertaining to my interests. It was a blast!!!

Some things that I really enjoyed: (1) The format emphasizes creating design-to-product pipelines for data science projects. Having done 4 independent projects, I felt ready to tackle new problems, (2) my instructors, Chad and Cliff, provided a good mix of academic and pragmatic approaches to learning data science and completing DS projects, (3) after completing the bootcamp and reviewing class material for a couple weeks, I felt ready for interviews, (4) we practiced pair programming on a daily basis, (5) I felt a sense of camaraderie with my peers. (6) in terms of career support, our counselor (Marybeth) was very helpful. She helped me with everything I needed, from resume/cover letter review, job hunting strategy, connecting with other recruiters, etc.

Some things to improve (perhaps only for my cohort): (1) having code reviews or detailed project reviews with students would help I think, (3) I would’ve wanted to have more whiteboarding practice (this one didnt come up until the end, during mock interviews), (3) learning more DS use cases for various business problems would’ve really helped.

Advise for future students:
* 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.

I recommend Metis to anyone who wants to pursue data science and analytics as a career. You actually gain enough knowledge and experience to move your career forward, or to pivot in that direction. The material is fantastic, it’s clear that a lot of thought and effort has gone into not only producing it, but revising it to improve it, and curating it to keep it up to date. The instructors are brilliant, they’re actual practitioners of the material, and they’re excited to have you learn it too. The staff is wonderful, they make the bootcamp an enjoyable place to spend three months, and the career services support is way better than anything your college pretended to give you- it’s personal, thorough, ongoing, and effective.

As with everything, you get out what you put in, but there’s ample material to strive and learn a lot, and the instructors are supportive and available to help you learn as much as you’re willing to. It’s not easy, and it shouldn’t be- if it were, you wouldn’t be learning anything. It takes a significant amount of time and effort, but the payoff is worth it.

I decided to do Metis for several reasons. After graduating, I spent several years in a variety of positions mostly related to project management and business operations but was looking to get into more analytical/technical professions. I was taking courses on Udacity and Udemy for about a year; while I found those courses helpful and informative, but they were not enough on their own to facilitate a career transition.

I learned about Metis from a close friend of mine who attended. It was a highly transformative experience for him, and he was able to secure a position at a startup where he actually started and is now building the data science team.

Like anything in life, you’ll get out of it what you put into it. One thing that makes Metis so great is the community that you join. Some of my closest friends are students that attended my cohort (and even previous and subsequent cohorts). We all helped each other through the challenges that we faced throughout the bootcamp. We learned as much from the instructors as we did from each other by working through our problems together. (Always be willing to ask questions!!!).

While it would be possible for me to (more or less) learn this material on my own, I would not have been to do so as quickly and comprehensively. That said, even if I learned this all on my own, there’s no way that I could have achieved the same level of confidence, not without being surrounded by the great instructors, staff, and peers that Metis provides. During the bootcamp, you’ll get a clear idea of where the bar is and what you need to do to reach it.

The career counseling was also highly beneficial. They are very honest about the career options you should expect upon completing the bootcamp, and they do their best to connect you with hiring managers and representatives where your unique skills and background will be appreciated.

Without any technical degrees or relevant experience, I was told to expect the possibility of a data analyst position and not a data science position. I had about a 50-50 split in terms of the interviews I was getting between data scientist and analyst positions. Three months after completing the bootcamp, I received two offers from leads generated through the Metis career counselor. One as a Senior Data Analyst and the other as a Data Scientist. I accepted the offer as a Data Scientist.

Knowledgeable Instructors: I was at Chicago bootcamp. My instructors Lara and John are very knowledgeable with rich industry experience and deep understanding of theories. I like the way they instruct data science in a clear and practical way. They are always patient and super helpful.

Well-Structured Curriculum: I enjoy the project-based curriculum which prepares us exactly for what we would do at a data scientist role. We practiced pair programming on a daily basis. We also had weekly coding challenges.

Interesting Projects: I did 5 projects at Metis. Those projects cover regression, classification, clustering, NLP, and time series analysis. All the projects were done in two/three weeks, which means you need to push yourself hard and dive into the machine learning algorithms.

Presentation and Communication Training: I am not a native speaker. My English proficiency is limited and my communication skill has plenty of room to improve. I did presentation for each of my projects. My instructors and advisor gave me invaluable advice on presentation and communication!

Career Support: Metis’s career support is amazing. My career advisor Ashley is super helpful! She gave me tons of suggestions and help with resume, cover letter, job hunting strategy, connection with recruiters and alumni, interview skills, post-graduation 1 on 1, etc.

Cohort Peers: Cohort peers are smart and from different background. It’s a really good experience to know so many interesting people who have same passion in data science. I enjoy the collaborative environment at Metis. We helped each other out with problems and keep in touch after graduation.

In short, I would say Metis is a life changing experience to me. Highly recommend!