Metis

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.

Highly recommended.

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!

This is a review for Metis Data Science Bootcamp (NY).

As the title said it all, I think taking Metis Data Sciece Bootcamp is the best decision I ever made. Instead of spending $100,000 and 2 year of your life taking a master of data science in school, Metis helps you become a data scientist in 3 months and $88,000 less.The bootcamp is intesive (which bootcamp isn't?), but they do make sure you get the best out of the 3 months with them. I have never been in and school/class where I learnt so much in 3 months. I went from knowing nothing about python to building app by the end of the bootcamp.

The instructors are extremely nice and helpful, and super knowledgeable. They was able to answer all (seriouly, all) of my questions. Even on weekend, you will be able to ask them questions, and have the answer some time instantly.

The job assistance is amazing. They will help you networking, fixing your email, your cover letter, anything... you name it. And the job assisstance goes well beyond the 3 months bootcamp, Metis talent team will help you until you get a job. They are very amazing.

The bootcamp also have a lot of fun activites, thanks to our lovely coordinator.

All in all, it was an amazing experience.

 

 

For me, Metis was a positive and fun experience with a great collaborative environment and knowledgeable instructors. I personally learned a lot coming from someone with less computer science experience. I believe it did help me land my current data scientist position in terms of confidence in the field, d3 experience, and having data science projects to show. I would recommend the program especially if you are looking for a data science position that doesn't require a PhD. They do put you in contact with a lot of hiring partners as well.  

One of the best personal growth, and educational experiences of my life.  The instructors are incredibly dedicated to the success of their students, and are seasoned experts and practicioners in the Data Science field.  If you attend this course you will be among a very diverse cohort of students from wide ranging professional, educational, and cultural backgrounds.  About 1/3 of our cohort had Phd's.  Another 1/3 were fresh out of undergraduate with degrees in mathematics or comp sci.  The remaining 1/3 had left their jobs at finance or tech startups to deepen their knowledge in the field.  You can be certain that every student there will be incredibly intelligent and driven to succeed.  You will make great friends, build contacts in the community, and will get plenty of assistance and advice in the hiring process.

That being said, this is not for everyone by any means, and there are a few very important considerations to keep in mind before deciding on whether this course is right for you:

1. This is not college
   • Your success will not be measured in grades or a diploma, but rather in how well you understand the material, and whether or not you'll be able to walk out of Metis ready to apply the new knowledge.
   • Your skill level, competence, and employability in Data Science upon graduating is entirely on you.  If you aren't interested or capable of fully immersing yourself in the course in and out of class, practicing coding and wrangling on your own time, regularly staying up late reading anything and everything related to the material, or proactively seeking assistance or advice from instructors, students, or members of the data science community - don't bother.
2. 3 months is not a long time
   • You will not be a data science master in 3 months.
   • Becoming proficient at the industry standard data science toolkit is like anything else - playing guitar, learning Spanish, etc.  You need to commit to practicing constantly, or you simply will not build upon or retain the skills.
   • Every hour of every day counts over the 12 weeks.  Make the most of your time.
   • Be prepared to continue practicing after graduation.  At this point, it's all on you.  If you can't commit to practicing and staying engaged after leaving, you will forget this stuff fast.
3. Phd's and quantitative backgrounds may not be necessary for becoming a data scientist, but it certainly helps - especially in the job hunt.
   • Once on the job search you will quickly realize that the expectations of data science applicants are exceptionally high.  
   • The data scientist is still a very coveted profession and in spite of the increasing demand, companies are still incredibly selective about who they'll even consider.  There may not be an unlimited supply of Phd's out there with machine learning and database skills, but there are definitely enough for Google, Facebook, Apple, etc.
   • Ultimately, the Data Scientist archetype at the moment seems to be someone who is a master in their field, extremely good at math, and very competent programmers.  It is not a position for beginners.  Any level of expertise, research, or advanced academic background you have will be important.
4. Don't go into the course cold turkey
   • Practice ahead of time. Do the pre-course work. All of it.
   • I would highly suggest learning basic SQL before taking this course if you don't know it already.

The class covered Python,  probability, stats, linear algebra and calculus.  

The materials were very clear and learner friendly, with many embedded examples.  Very well suited for distance learning format.  

Replays of the classes were available if you couldn't make the live session.   The class isn't "graded"... you put in as much work as you want,  If you already know some of the topics very well that probably gives you the flexibility to skip a class.  

The instructor was always well prepared, and also offered an hour or two beyond normal class hours for consultation.  He knew the material and adjacent topics very well, and supplemented his explanations with "in the field examples" from his work experience.  He was an excellent communicator and succeeded in creating an environment where all felt comfortable asking questions.  He seemed to really care that he successfully answered people's questions.  

There were about 12 other people in this class.  We were a diverse group, which made the class even more interesting.  All were serious about learning, but friendly and patient with one another.  

Motivation: I am a mid-career student who has managed data scientists in the past but never really got very technical with implementing models. I took this course to become more familiar with machine learning and AI in order to complement my technology skills (programming/infrastructure) and to better manage data scientists in the future.  

Curriculum: The curriculum is well thought out and focuses on both practical implementation along with theory. The curriculum is designed to truly give you both a birds-eye view and hands-on skill with all parts of machine learning/AI, big data, visualization, Python, environment setup, etc. 

Teachers: The teachers are top-notch; they were experts who have held significant roles in industry and not purely focused on academic research. 

Job Assistance/Training: The job assistance and training also gave me a great new vantage point with which to conduct a job search. They even provide amazing post-bootcamp resources like: career coaching, mock-interviews and an alumni Slack channel.

Bottom-Line: Metis was a great experience from beginning to end. I was part of the first online-only cohort and it was a seamless experience from interacting with teachers to collaborating with your fellow students and teaching assistants. I think all-in-all this was a great value for money but it depends on how focused you are to get the results you want!

Having worked as a data scientist for a little over a year, I can say with full confidence that Metis has been an extremely valuable experience.  

I came into Metis without any technical work experience. I graduated with a BA in Economics and worked in a nontechnical role for 2 years immediately after graduating college. I was super unengaged at my first job and started taking classes in programming and data science in my free time. After a year of taking these classes part-time, I decided I wanted to make the career switch to data science. Metis stood out the most to me since you get the opportunity to work on 5 different projects under the guidance of data science professionals, along with receiving career support.

Though the program is only 12 weeks long, you definitely learn a lot. The instructors are very involved and really care about your learning. I was super lucky to have such sharp and collaborative cohort mates, which made the program even more rewarding. You also receive excellent career support during and after the bootcamp until you land a job. Since I was coming from a nontechnical background, I made sure to follow the advice from our career advisor as much as possible (Thank you, Andrew!). I eventually got my current role through a referral from a Metis alum. Even though it's been more than a year since I completed the program, I still refer to some of the material from class and keep in touch with the Metis community. 

Tips:
- Complete the pre-work before starting the bootcamp
- Take a class on Python before the bootcamp if you do not have a computer science background (I recommend the Introduction to Python Programming edX courses from Georgia Tech) 
- Follow the career advice and network as much as you can
- Collaborate with your cohort mates 
- Continue to sharpen your skills even after the bootcamp

I took a semester off from college my junior year to do Metis and while I did have a few small issues with the bootcamp, my overall experience was very valuable and ultimately led to an internship + full-time job offer in data science.

My cohort was full of very bright and motivated students which really enhanced my experience because many of my evenings were spent coworking and bouncing ideas/concepts off others. Metis does a good job filtering via interviews for students who will benefit the most from the program. I've also got a great network now of data folks as many from my cohort have gone on to awesome data jobs at companies like Google, Apple, Nike, Genentech, and IBM. That said, doing the bootcamp alone is nowhere near enough to land a serious data science job. Most who I know, spent an additional 3-9 months reviewing, studying, taking on contract work, networking, and interviewing after the conclusion of the bootcamp before hitting their payoff. 

The curriculum is fast-paced. The only skill you'll come out remotely procient in is Python (and even then you'll rarely touch object-oriented concepts), the rest you'll only have glanced the surface of. For instance, our cohort spent merely 2 days on javascript and D3 visualization. We were encouraged to copy D3 templates and finesse the visualizations if that's a direction we wanted to take with out 3rd project. The same was true of tools like SQL, MongoDB, Hadoop, Spark, and Flask. That said, the bootcamp gives you a great overview of all the concepts and tools you'll need to be strong in to be a legit data scientist. I recommend taking notes during lectures and reviewing them after the end of the program as well as covering a bunch more materials that I'll list at the end of this review.

Our two instructors (Debbie & Joe) did a really good job for such an intense program. Joe who at the time was teaching his second cohort is one of the best teachers I've ever had the pleasure of learning from. He has immense talent for explaining technical concepts and theory in a way that is simple and clear, and is amazing at addressing the very core of any followup questions. He was invested into the program and often stayed past 5pm, sometimes up to 7-8pm on the days leading up to a project deadline. Debbie was great as well, but came from a different background. She was forced last minute to teach our cohort, and thus had to deal with moving to SF from the east coast at the start. While she was also great, I would have preferred there be more organization so that our second instructor could be fully prepared for what feels at times like a 12-week sprint. 

 

Resources to review after a bootcamp like this to get a data science job:

• Learn OOP in Python using any online resource + do a full programming interview book
• Andrew Ng's Intro to Machine Learning course (even better if you review before the bootcamp)
• Andrew Ng's Deep Learning Specialization (if you want to go into more heavy ML/DL)
• Hands-On Machine Learning with Scikit-Learn and TensorFlow (O'Reilly book that reviews a lot of the bootcamp's material)
• Fast AI's MOOC
• All of Statistics - A Concise Intro to Statistical Inference, 20th Ed. 
• Ian Goodfellows "Deep Learning" (if you want to go into ML research or grad school) 
• Practice tools like SQL, Flask, Spark on your own