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 provides students with on-site instruction, and access to speakers, mentors, events, and job support. 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. Successful Metis alumni will have skills in Python, Bash, algorithms, linear regression, machine learning, NLP, databases, and interactive data visualization. The data science curriculum is delivered through project-based, hands-on, collaborative learning.
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 in NYC and San Francisco, which allow non-U.S. students to attend technical and vocational programs in the U.S. 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 4.87
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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
- July 22, 2019
- Class size
- Minimum Skill Level
- Absolute Beginner
- Placement Test
More Start DatesJuly 22, 2019 - OnlineApply by July 22, 2019September 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 - 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, 2019September 23, 2019 - SeattleApply by August 19, 2019January 6, 2020 - SeattleApply 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
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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.
- 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.
I graduated from the Seattle Fall cohort (the second cohort in Seattle) in December. I come from a pure math background, and I'm a recent Ph.D. grad who was pretty nervous about going into data science.
Metis is everything I wanted out of a bootcamp. The project based curriculum works really well, especially since the topics of projects 2-5 are entirely self directed. It's easy to get excited about learning this stuff when you're applying it to a topic you really care about. That also makes it easier to pick yourself up when you fall -- and you'll fall a lot.
The lectures are an excellent introduction to the material, too. I had the opportunity of both learning from the Fall 17 instructors and TAing for the Winter 18 instructors, and all four of them were awesome.
Some quick warnings. Definitely do the prework. Definitely work ahead as much as possible -- everything sneaks up on you, from project MVPs to student investigations. This bootcamp is extremely intense -- I had to defend my Ph.D. in the middle of it, and that was the next thing to impossible. Don't do that. :P
Career resources are incredible, too. The job hunt is a long, demoralizing, frustrating process, and our career advisor made everything go so much more smoothly. I had no idea how much I was doing wrong, or making things harder for myself, when I was trying to figure out everything on my own.
The community of alumni is a great resource. My cohort actually had Thanksgiving together -- not every one will be that closely knit, but you'll definitely make friends and business contacts, and you have the whole network of Metis alums to draw on.
One small gap in the curriculum -- at least for me -- was some fundamental statistics stuff, especially related to experimental design and a/b testing. But the curriculum is evolving, and Seattle is still a young campus. Be forewarned, though, if your basic statistics is a little shaky like mine was, you might need to do some self-study in some areas.
If you can afford it (both in terms of time and money) then Metis will give back so much more than you put in. Highly recommended.
I attended the Fall 2017 bootcamp in Chicago. From the beginning, Metis was very helpful to me. I live in the DC area and the NYC cohort was full, but they helped me come out to Chicago so that I could still participate. They were very helpful (Nathan Vermeiren) with my housing search and all of my other out-of-town needs. I ended up staying right next to the Metis office which worked out great. It was actually helpful for me to be away from home because I didn't have any of my usual distractions/duties to do, so I recommend going out of town. The only downside to going out of town was that Metis was not as connected in the DC area. Metis did continue to support me fully in the job search until I obtained employment and I had no trouble contacting the DC Metis alums who were quite helpful.
The curriculum is great. They spend a small amount of time on many methods and tools used in data science. They are not simply choosing the tools that are 'always done'. I know our teachers went out of their way to have us use the most current tool for the job (like spark vs. hadoop) and/or the best tool for the job. Often they would show us several ways to do something and then we would choose the one that we liked best or were familiar with. One of the many things that make this bootcamp more valuable than sitting in your PJs on coursera, is the ability to ask them questions (more on this below). Another is that you will learn the method/tool and then USE it in a major project, not just a homework. This project will be something that you are proud of and can put on your resume. These projects are the reason I got job interviews. Not my PhD, or my peer reviewed publications, but my ML projects from Metis. The interviewers would ask far more questions about my metis projects and I was able to speak about the methods with confidence and authority because the project required me to know what I was doing.
The timing of the teaching is just right, in that they teach it to you the day before you need to start using it in your project. The best way to learn is to use it right away, and you will. Cloud computing/storage (AWS, Spark,hadoop) and databases (SQL, MongoDB) are other topics where it is valuable to have an in-person teacher. These are things that are difficult to get right when reading a forum because some things will be specific to your hardware or router. It would have taken me MUCH longer to figure out how to use AWS by myself.
Teachers are what makes the Metis experience. Our teachers were Zach Miller and David Ziganto. These 2 are truly great instructors and also great mentors. They are incredibly knowledgeable about all things Machine Learning and Python, and are so patient when you have questions. They also have many informative stories about their experiences when working in data science or interviewing data scientists for jobs. They taught us about what you do as a data scientist, and pitfalls to watch out for. One of the things that they manage to do is push you really hard and enforce difficult deadlines, but at the same time support you. They aren't going to give you answers (like any good teacher) but they will help you get out of being stuck on one thing for too long. They come and sit with us ALL day, every day and were never dismissive or impatient.
I have never had someone be so hard on me about presentations. They have incredibly high standards and it has taught me so much about something I thought I was already good at. I didn't fully understand why they wanted these 5-6 min presentations until I had my first interview and realized that a quick presentation is what you are doing every time you get an interview. It's literally interview prep, w/o labeling it that way. The career advisor Ashley Purdy was a big part of the presentation brigade and she helped me communicate complex things in a less technical way (which is very important for a data scientist). These three never let up on me for a single second and it was perfect.
I paid for bootcamp so that I could get better and that means I need to be pushed into a place(s) where I am not comfortable. I can do comfortable projects on my own. Full disclosure: I did not always succeed when they encouraged me to do things that were harder and that was ok. They were there for me when I failed and helpful about what I could do to deal with it. I know that's not always the case (it's completely possible to fail bootcamp if you are underperforming consistently), but because I choose something that was less 'safe' for me, they took this into consideration, and made sure that I was mentally/emotionally ok.
Ashley Purdy (career advisor) pushed me to do things that I never would have done in my search for jobs and online presence. Metis required me to write a blog, which has earned me more than one interview, and I now enjoy writing posts. Our teachers have even promoted my posts via linked in and data science weekly. Ashley also showed me how to cold call people on linked in and just talk to them about their job (informational interview). This was very scary and really paid off, as one of the people I spoke with got my resume in front of the right people which led to me obtaining the job I wanted! She is there when I have questions about salary negotiation or whether a recruiter is just spamming me. There were also some great presenters that came to talk about what they do at company X as a data scientist. The Metis alumni is a great community and will really help to build your data science contacts.
Metis’ career advisor (Ashley in Chicago) is one of the main reasons I chose Metis over some other bootcamps. Metis is very invested in whether you are able to obtain a job that you love. They don’t just present you to a few employers and say ‘bye’; they continue to make sure that you are structuring your days and applying to places that will be a good fit for you. It was always very clear that they were not just trying to get me to take any job so that they could check a box. In fact, they encouraged me to hold out for what I want rather than take the first thing I was offered.
Zach, David, Ashley and Nathan are also just really great people. They have a great sense of humor and are fun to be around. We had plenty of good laughs together (students+staff) and I know I had a great time while also being stressed out. This is why I always say, it's like grad school (in fast-forward) w/o the emotional abuse. lol
Students were another important part of the bootcamp. Every student was highly motivated and smart. We hall had various areas of expertise and I liked learning from them. It's very much a feeling of 'we're all in this together' and even when I wanted to slack off, I was inspired by them to continue to bring my 'A game' because they operated at such a high level. They were an important part of the high standards that Metis and our teachers set.
This bootcamp is intense. If you have been through a PhD program, it's kinda like the night before you have to send your abstract/poster to the conference committee and you all stay up all night together working on it. Except that's every day for 12 weeks. You will want to spend your weekends on your projects, and come home pretty late (not everyone does this, but I did). So, be prepared for that level of work. It's completely worth it, but not possible for everyone. There's a reason they call it a bootcamp...
In sum, I highly recommend the Metis bootcamp to people who can devote the time. I obtained 2 very good job offers, in writing, within 2 months of the graduation date. I now have a job at Booz Allen Hamilton as a data scientist that is exciting, and I am ready to connect you to my network once you graduate from Metis!
The Metis data science bootcamp is three months of intense learning. The curriculum is a data science survey course covering a wide range of data science topics from linear regression to natural language processing. Each topic is divided into approximately two week segments where you learn about the math and details behind the topic and at the same time, work on a project implementing what you learned.
I attended Metis to make a career change. I was formerly a trader for 11 years. For me, the lessons and coursework at Metis were extremely challenging. I spent every day and night working on projects, debugging code, and reviewing material and still felt over my head for much of the bootcamp. I was putting in 12+ hour days, seven days a week.
Metis especially shines in three aspects. One is the quality of the students. My cohort had 11 people in it. There was a large range of math and programming skills in the cohort. The students with more advanced technical skills were able to take advantage of material and produce more advanced projects than others. Everyone completed a portfolio of projects. Given this differing skill level, the learning atmosphere was collaborative. Every student was stressed and overworked. We used this common bond and help and support each other.
Another strength of Metis is the quality of the instructors. The instructors for my cohort were tough but fair. Their data science knowledge was impressive. They held our cohort to a very high bar. I both succeeded and failed at times, but they were there to guide me on how to improve and make progress.
Finally, Metis focuses on getting its graduates employed. Throughout the entire bootcamp, there is an overarching theme of getting employed. Each campus has a full time career advisor who knows the ins and outs of navigating the data science job market. Upon completing the program, you will have a portfolio of five projects to show employers that you are capable of performing as a data scientist. The instructors gave us insight into what employers are looking for in terms of the interviewing process and job performance. This combination of projects, instructors, and career advisors is how I managed to land job interviews and eventually get hired.
One thing I wish I knew before Metis:
I would have gotten more out of Metis if I had more of an introduction to the machine learning algorithms. Understanding the machine learning algorithms was the most challenging part of the course. There were times where we spent two hours on an algorithm then moved on to the next topic. It was impossible for me to understand this complex new material in that short amount of time.
Before Metis, I was not competitive for any data science positions. I wasn't even competitive to receive an interview.
After completing the 3 month program at Metis, I was competitive for entry level data analyst and data science positions. I received an offer 2 months after the bootcamp ended.
- 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 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.
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 hope this review helps other people in a similar situation as mine. I came to the 12-week bootcamp with little to no relevant professional experience (I was in software sales beforehand), but willing to do whatever it took to break into a career in Data Science.
If you want to transition career paths like I did, Metis is absolutely the way to do it. I had several interviews with interested employers within days of graduating, with one interview coming just minutes after I presented my final project at career day -- and this all during the holidays, when recruiting is generally slower.
Metis provides the tools and support that are impossible to get for a career-switcher like myself. Yes, you can crack a book on statistics and do some Hackerrank coding -- and you should do those things anyway! -- but that is nothing compared with the legitimacy that I've gained in employers' eyes by investing time and money in the program. Moreover, the career support is excellent and doesn't expire; the Career Services staff have done an outstanding job of building a hiring network that you as a Metis student have full access to, and they give top-notch advice on the minutiae of resume writing, LinkedIn profiles, and so on.
In my opinion, a further vastly underrated aspect of Metis is the strength of the alumni network. By graduating from the program, I instantly have connections to alums that have ended up at major companies in many different industries. Browse the LinkedIn profiles of Metis alumns and you can see for yourself.
I will give two pieces of advice: one for those making a decision about Metis, and one for those who have decided to attend the bootcamp.
My advice in making a decision whether to attend is to contact as many alums as you can via LinkedIn or through their blogs, and ask them about their lives pre-, post-, and during Metis. You will learn a lot about what Metis students are like, and whether you can see yourself as one of them.
My advice for you, if you will be attending, is to take as much initiative as you can to learn what you can beforehand. I personally read and noted "Intro to Statistical Learning," a Machine Learning textbook, and I found that I got much more out of the bootcamp because of that. You may decide that Andrew Ng's Coursera course is more your speed, or maybe something else -- but putting in the work regardless will pay off. The bootcamp is only 12 weeks long, and you can't possibly learn 100% of everything that's thrown at you. Putting in the sweat equity ahead of time will pay enormous dividends if you're not seeing most of the machine learning concepts for the first time.
I financed the bootcamp myself with savings. It is obviously not cheap to attend, and I thought long and hard before finally submitting my payment. But in end the desire to change careers won out, and I have not looked back since. The bootcamp was worth every penny that I spent, even if it was nerve-wracking to commit to at first.
In sum, I would wholeheartedly recomend Metis to anybody that is truly motivated to change careers, even if it is a significant change like it was for me.