Metis - CLOSED

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... Read More 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 semester of college (technically and about networking/job search/careers) and I enjoyed it much more.

I chose Metis because of its accreditation and encouraging reviews and I'm glad I did. Although it was probably the most challenging thing I've ever done, I was also having fun in the process and came in everyday eager to learn more. Not only did the... Read More bootcamp equip me with the necessary programming skills I needed to transition to a more technical career, I also built incredible work ethic in the process and befriended and connected with many other intelligent, driven students in the collaborative environment.

My instructors at Metis were very knowledgable and were great at communicating the data science concepts and machine learning algorithms. At the same time, they were patient enough to answer our many questions. The schedule and curriculum was well-rounded and we were trained to be better presenters and communicators with our 5 data science projects/presentations that were due during the course. I couldn't be happier with my decision to attend Metis and I would highly recommend it to anyone who's looking to get into this field.

Metis isn't for the faint of heart. What this program is NOT: an easy, magical path to coast through for 12 weeks to land a data science position. What this program IS: a well structured, intense immersive that can lead you to your goals when doubled... Read More with appropriate effort.

My background is from business school. I worked as a Tax accountant CPA, then management consultant for finance related projects, and then as a community economic development volunteer in the Peace Corps. Hard skills such as programming, statistics, or any kind of science were not my forte - but rather I'd developed experience in problem solving and communication skills more than anything. However, one fascinating part of Metis was the diversity of backgrounds among my cohort - and the ability for many people to succeed in the program.

I strongly encourage any potential applicant to be thorough in their preparation and vetting process. If you are able to pass the entrance exam for Metis, and know why you believe the program will help you achieve your goals, then I would highly recommend it ensuring you have the discipline and time to focus on this for 12 weeks.

For my anecdotal experience, Metis was fantastic. I chalk a lot of that up to having done substantial thinking in deciding why I wanted to do a data science bootcamp and how it would help me make the career transition from accountant/consultant to data scientist.

So why Metis then?

- I found the instructors extremely knowledgeable and accessible
- My cohort peers were intelligent, engaging, and had diverse backgrounds. I think a large part of that is the Metis admittance process
- Dedicated career support (Marybeth in Seattle) was quite helpful in navigating and structuring the job hunt process
- The 5 project structure of the program was invaluable. In many of my job interviews I found being able to talk about how I did 5 projects - 4 entirely solo and which included gathering my own data - a huge asset

Additionally, the physical space was perfectly adequate and not run down like I've heard rumors of some other bootcamps. There clearly is support after the bootcamp - but finding a job is not handed to you on a platter. In my personal case, I spent about 12 weeks job searching after the bootcamp as well. It paid off in the end, and I would have done Metis 10/10 times again, but don't expect a job to be handed to you afterward. The Metis presence in Seattle is smaller than some other bootcamps, but if you are willing to expand your job search outside Seattle I personally found Metis connections help me make headway in over 10 different job interviews.

The curriculum continues to bee updated and I'm glad to hear they spent some more time on time-series for the cohorts after my own.

In summary, I recommend Metis if you do the hard work in visualizing how the immersion will help you achieve your next goals. If you believe a data science bootcamp is the right step for you, and you're willing to put in the time and effort into the program, Metis is an excellent investment of your time and money. Please feel free to reach out if you have any questions.

My background was in management consulting and I had some experience with data analysis, but the amount I learned here was unreal. I was really interested in machine learning and neural networks but found I was having trouble learning them on my own in... Read More an unstructured way. Metis was perfect for this and aligned with my preferred learning style really well. I learned everything I wanted to and then a whole lot more. They cover just about everything you'll come across as a data scientist in varying degrees of detail, but then set you up to learn so much more after you're done. I spent the weeks after I graduated reading and deepening my knowledge about all of the stuff I learned in Metis, and tutorials and research papers that would have been way over my head pre-Metis just didn't scare me anymore. It's really cool! The instructors were outstanding and could answer just about every question you'd throw at them, and then they went above and beyond to make sure you understood everything before moving on. Take advantage of that because they're all really knowledgeable and more than willing to help. I actually spent quite a bit of time learning things that interested me that weren't in the curriculum for my projects and they even had answers about that stuff too. I really think you'll get a lot out of this, but be prepared to put in the hours. The class goes from nine to five, but if you're willing to work nights and weekends and squeeze every bit of knowledge you can out of this then you'll be very impressed with what you know and can do by the end.

If you are looking for an in-person, immersive Data Science bootcamp that will give you skills and confidence in this burgeoning field, you need look no further than Metis. The highlights:
- Passionate, intelligent, yet personable and caring staff
- Applicable... Read More curriculum, a good balance of broad and deep
- Bright, motivated, supportive peers
- A true cheerleader in the Career Support Advisor

I appreciated that there was a significant amount of "pre-work" recommended. This isn't a zero-to-hero bootcamp, and it shouldn't be. Everyone walks in with a baseline, so you can get more or less straight to work learning about Data Science. I enjoyed the project-based curriculum and believe it was a very effective way to teach new concepts. As you learn, you apply it. Instructors are highly available to assist with any questions or issues -- though they sometimes got monopolized by students seeking more help than others. They are incredibly patient, kind, and wise. Classmates were friendly and always willing to help -- it felt highly collaborative, and never overtly competitive. Metis puts forth a lot of effort to make it feel like a community and that each student is valued.

Beyond the Data Science, Metis teaches you *grit*: how to not give up, to find a way. *time management*: you don't have a lot of time for your projects -- so you learn how to time-block and set deadlines for yourself. *how to learn, and learn fast!*: don't know something? Google it. Find a book or a blog. Ask someone. Get creative. These are skills that will serve you well in The Real World.

My only quibble is that I was expecting a bit more from the Metis network in terms of finding a job. Perhaps the Seattle program is too new to have a vast expanse of alums armed with a trove of jobs, just waiting for us to graduate to bestow them upon us. Maybe I am just lazy and entitled. I should note that some alums were very involved and eager to help. Also, Marybeth, the career advisor, is an amazing resource. She is highly knowledgeable -- and connected -- and is also always there for moral support. She'll help you structure your time post-bootcamp and negotiate that inevitable job offer.

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... Read More 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've attended a lot of highly ranked educational institutions, including elite private colleges and the most innovative university in the United States. I have a doctorate, I've worked as a professor, and I've helped design curriculum. So when I say that... Read More Metis is the most focused, most professional, and most valuable educational program I’ve ever been a part of, it means a lot. I went from adjuncting at poverty wages in an academic field with no future, to data scientist at a financial institution, with a salary that I consider quite generous.

Metis is a 12 week bootcamp, that is truly 9-5 and then some. Each day at Metis begins with pair programming, where you’re given a problem and have to work out a solution with a classmate in about an hour. Pair programming solidifies the concepts that you’ve been learning, and is great practice for technical interviews and the real world. The problems are like those that you’ll see at a technical interview, and you have work with and explain your thinking to, people with a wide variety of backgrounds and levels of technical expertise. Sometimes you’re working with someone with a software engineering background, and you can whip up some kind of nifty recursive solution in minutes. And sometimes you’re the person who has a solution, and you have to help a classmate who’s struggling figure it out.

Mornings at Metis consist of structured instruction; lectures and prepared Jupyter notebooks that demonstrate concepts and best practices in data science. The coursework is brisk, and designed to take you from “What is a list comprehension?” to fancy stuff like implementing deep neural networks on AWS cloud servers. Lectures cover regression, classification, data structures, time series, natural language processing, and neural networks. Afternoons have talks about the job application process, and guest lectures from industry data scientists, as well as short presentations from students on things they’re experts in; everything from A/B testing to getting the most out of the Linux command line.

The meat of Metis are the projects. The first project is assigned, with an imaginary client and a small group, but the four remaining projects are up to you, with only the loosest guidelines, like “use linear regression” or “do unsupervised learning”. The best way to learn data science is to do it, and in projects you’ll learn problem formulation, data collection via web scraping, data cleaning and management, algorithms, and communication, as every project is finished with a four minute presentation to the class. The last presentation is in front of hiring partners at career day. The Metis projects are a great way to build a unique portfolio as a beginning data scientist.

Even though you’re working independently, you won’t be alone. The instructors, I had two in my 22 person cohort, are on hand to answer questions and help you think through your problem, and there are TAs, graduates of the last cohort, to help with debugging. Don’t get too attached to the TAs; they have a tendency to get hired away mid-course.

The bootcamp doesn’t require a lot of mathematical and programming proficiency when you begin. I recommend that you focus on the data science itself, and try and come in as well prepared as possible. Knowing your way around Jupyter notebooks and the Pandas and Numpy libraries will save precious time during the course. I really like Sweigart’s “Automate the Boring Stuff with Python” as as a place to start if you’ve never coded before. For machine learning, Raschka’s “Python Machine Learning” is a great desktop reference; I think half the class had a copy by the end.

I am very satisfied with Metis. The program, staff, other students, and ongoing career support were all top-notch. The only thing which I thought could be improved was the data visualization portion, which was merely serviceable, but I hear that it is being updated for 2019 to take into account Bokeh, Tableau, and Plotly. Obviously, you’ll get out of Metis what you put into it, but if you’re willing to work hard, it is an incredible opportunity.

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... Read More 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 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... Read More 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... Read More 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.