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 covers Python, Bash, algorithms, linear regression, machine learning, NLP, databases, and interactive data visualization. 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. The data science curriculum is delivered through project-based, hands-on, collaborative learning and Metis provides students with on-site instruction, and access to speakers, mentors, events, and job support.
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 across all U.S. campuses, which allow non-U.S. students to attend technical and vocational programs. 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 4.88
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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
- September 23, 2019
- Class size
- Minimum Skill Level
- Absolute Beginner
- Placement Test
More Start DatesSeptember 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 - SeattleApply by August 19, 2019January 6, 2020 - SeattleApply by December 2, 2019September 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, 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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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:
- 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.
- 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.
- 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.
- 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.
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.
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
I attended the data science bootcamp at Metis in summer 2017. I was from a very different major gearing towards learning more on data analytics, thus in transition of career direction and expertise field at that time. Attending Metis has played a key role in terms of providing me very proactive education to accomplish the transition in such a short period of time. I truly learned a lot in many aspects from the education itself, how to really go out there and interact in the data science society/community, and last but not least how to efficiently present myself to the job market. I'd humbly like to say that I'm glad I decided to learn from Metis, which I believe ultimately made me able to currently work as a data scientist in the competitive industry.
When deciding to enroll in a bootcamp, spending focused time on building skills and a portfolio are the draw, but really your biggest long-term benefit will be the network. With Metis, I have nothing but great things to say about the community. There's a culture of collaborating and learning-by-doing in the classroom. I loved taking three months to surround myself with smart people from diverse backgrounds and areas of expertise. I chose Metis primarily because of its focus on rapid deliverables and that I would walk away with a portfolio of projects to speak about with employers. But I've been amazed at the strength of the alumni community and don't know how I would begin to manage a job search without it.
I was part of the earlier cohorts for Metis and found that they were still trying to smooth out their curriculum. I found the job assistance to be substantially lacking. They have career partners but none of them had hired former Metis alum which made me question their 'participation' in the bootcamp or if it was false advertising to potential students. I know some students have gone off to do awesome things, but I would say that this is the exception rather than the norm.