Apply by 12/16 to take advantage of 2018 bootcamp tuition for 2019 cohorts. Apply

The Only Accredited Data Science Bootcamp

Immersive Data Science Training & Career Support

Metis's 12-week accredited data science bootcamp is an immersive program designed to give you the skills and connections you need to launch a career in data science. Career Advisors are dedicated to helping students and grads get hired, while Sr. Data Scientists bring real-world experience to the classroom and guide students as they use real data to build a 5-project portfolio.

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Here's what you'll learn at the bootcamp.

Course Details

12-Week, in-person Bootcamp

Monday - Friday, 9:00am - 5:00pm

2-3 hours of classroom instruction daily

4-6 hours of development and project work

Prerequisites

Experience with programming

Experience with stats

Cost: $16,000*

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*Effective January 7, 2019 the tuition for the Metis Data Science Bootcamps in all cities will increase to $17,000. Accepted students who have signed and returned their enrollment agreements on or before January 6, 2019 will receive the current tuition of $16,000. We recommend students apply before December 17th, to ensure enough time to go through the full admissions process and meet the January 6 enrollment deadline. If you are not ready to start in the Winter cohort, we offer deferment options. Remember you must complete the process before January 6th.

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3 years running

Online Pre-Work

25 hours minimum of academic pre-work and variable hours to setup.

Bootcamp Summary

Week 1: Introduction to the Data Science Toolkit

Exploratory Data Analysis, Bash, Git & GitHub, Python, pandas, matplotlib, Seaborn

Week 2: Linear Regression and Machine Learning Intro

Web scraping via BeautifulSoup and Selenium, regression with statsmodels and scikit-learn, feature selection overfitting and train/test splits, probability theory.

Week 3: Linear Regression and Machine Learning Continued

Regularization, hypothesis testing , intro to Bayes Theorem

Week 4: Databases and Introduction to Machine Learning Concepts

Classification and regression algorithms (Knn, logistic regression, SVM, decision trees, and random forest), SQL concepts, cloud servers

Week 5: More supervised learning algorithms & web tools

Naive Bayes, stochastic gradient descent and intro to Deep Learning, Full stack in a nutshell: Python Flask, Javascript and D3.js

Week 6: Statistical Fundamentals

MLE, GLM, Distributions, Databases ( RESTful APIs, NoSQL databases, MongoDB, pymongo) Natural Language Processing techniques

Week 7:  Unsupervised Machine Learning

Various clustering algorithms, including K-means and DBSCAN, dimension reduction techniques (PCA, SVD, LDA, NMF)

Week 8: More Deep Learning & Unsupervised Learning

Deep Learning via Keras, Recommender Systems

Week 9: Big Data

Hadoop, Hive & Spark, Final project initiated

Week 10-12:  Final Project

Download Full Syllabus

Consider one of our part-time professional development courses.
Gain the skills you need + apply your tuition paid towards the bootcamp.

Financing
Third Party Financing

We partner with Skills Fund, an innovative financing company that offers financing options for students accepted to our bootcamp.

Scholarship
Scholarships & GI Benefits

We offer a $3,000 scholarship for women, members of underrepresented groups, the LGBTQ community, or veterans of U.S. military personnel. Learn about GI Benefits.

We are committed to creating a culture of inclusion within the exciting and growing field of data science. We aim to foster an equal and representative data science community amongst our staff and within our classrooms, filled with individuals of all technical, educational, and personal backgrounds.

Because of this, we offer $3,000 Data Science Bootcamp Scholarships to women, members of underrepresented demographic groups*, members of the LGBTQ community, and/or veterans or members of the U.S. military.

Why?

- Women make up less than one-third of all employees in the tech sector and just 11% of data scientists1 – additionally, they are paid significantly less, on average.2

- Women of color represent less than three percent of those in technology fields.3

- Only four percent of those in software development, application, and systems jobs are African-American and just five percent are Hispanic or Latino.4

- There are 20% fewer LGBT individuals in government STEM-related jobs than should be expected5 and 43% of the STEM workforce is closeted.6

We must reverse these trends and create more avenues for talented individuals from these groups and communities to enter, remain, and thrive in the field of data science.


* Underrepresented demographic groups include African Americans, Mexican-Americans, Native Americans (American Indians, Alaska Natives, and Native Hawaiians), Hispanic and Latino Americans, Pacific Islanders, and mainland Puerto Ricans.

International
International Applicants

International students can attend the Metis Data Science Bootcamp in New York City or San Francisco on an M-1 Visa. Details here.

Hear from our grads.

Read our reviews

Meet your all-star data science instructors.

Debbie berebichez

Debbie Berebichez

Chief Data Scientist

Debbie, Chief Data Scientist at Metis, uses her "physics glasses" to solve challenging real-world problems and promote critical thinking.

Deborah Berebichez is a physicist, data scientist and TV host. She has expertise in scientific research and advanced analysis and she has helped automate decision-making and uncover patterns in large amounts of data. Her passion lies in merging critical thinking skills with practical coding skills. She specializes in drawing connections between the approaches used in data science and the challenges organizations face. Deborah has a Ph.D. in physics from Stanford and completed two postdoctoral fellowships at Columbia University's Applied Math and Physics Department and at NYU's Courant Institute for Mathematical Sciences. She is a frequent mentor of young women in STEM. Her work in science education and outreach has been recognized by the Discovery Channel, WSJ, Oprah, Dr. Oz, TED, DLD, WIRED, Ciudad de las Ideas and others.

Check out content by and/or featuring Debbie:

Podcast: Becoming a Data Scientist with Renee Teate

Talk: Keynote Speech at Grace Hopper Celebration 2017

Vinny senguttuvan

Vinny Senguttuvan

Senior Data Scientist

Vinny, a Metis data science instructor, views data as a facet of perception.

Vinny comes to Metis after leading a team of Data Scientists at High 5 Games. Prior to that he taught Machine Learning at General Assembly and built tools for Animators and Effects Artists at Blue Sky Studios (the company that made Ice Age, Rio and The Peanuts Movie). He has a Masters in Computational Engineering and another in Creative Writing. He enjoys the nexus of mathematics, computer programming, human perception and arts. Over the past three years, Vinny has been knee-deep in large distributed data -- aggregating, building recommendation systems, measuring popularity and predicting Lifetime Value. In that time, he also finished a first draft of his novel. An avid programmer, Vinny has won various programming contests including the Regional ACM Collegiate Programming Contest. He has taught at the University of Miami and has given talks and presentations at various colleges and conferences.

Check out content by and/or featuring Vinny:

Article: The Data Science Pipeline - Analytics Week

Lara kattan

Lara Kattan

Senior Data Scientist
Lara is constantly awe struck by how math and statistical models can so reasonably approximate the messy real world, and loves to share her passion for the theory. 
Lara comes to Metis from McKinsey, where she worked with financial institutions on risk modeling. Prior to settling into the practical world of consulting, Lara received a master's from the University of Chicago, where she had the privilege of thinking about problems with no real-world implications. There her passion for unstructured thinking and novel problem solving were nurtured and she took the lessons learned with her into conference rooms and PowerPoint decks. The statistical and mathematical elegance of data science are what animate her the most, but she gets her biggest joy from demystifying the rigorous mathematics for all audiences. If forced to snap out of her mathematical reveries, she's probably doing yoga, riding a bike, or drinking too much coffee. 
Jonathan balaban

Jonathan Balaban

Senior Data Scientist

Jonathan Balaban is a consultant, data scientist, and entrepreneur with ten years of private, public, and philanthropic experience.

As a data scientist, he has worked at McKinsey and Booz Allen Hamilton, and he has taught data science at General Assembly. He has led teams to design bespoke data science solutions that have driven revolutionary changes in client operations. Jonathan - sometimes successfully - leverages data science solutions in his personal life: on friends, racing, and training.

Check out content by and/or featuring Jonathan:

Github Repository

Article: Three Questions Business Leaders Should Be Asking About Apple’s New Machine Learning Kit

Talk: Fortune-Telling with Python: An Intro to Time Series Modeling (ODSC East 2017)

Talk: Fortune-Telling with Python: Intro to Facebook Prophet (ChiPy Users Group)

Article: Best Practices for Applying Data Science Techniques in Consulting Engagements

Alice zhao

Alice Zhao

Senior Data Scientist

Alice, a Metis data science instructor, enjoys making complex things easy to understand.

Alice comes to Metis from Cars.com, where she started as the company's first data scientist, supporting multiple functions from Marketing to Technology. During that time, she also co-founded a data science education startup, Best Fit Analytics Workshop, teaching weekend courses to professionals at 1871 in Chicago. Prior to becoming a data scientist, she worked at Redfin as an analyst and at Accenture as a consultant. She has her M.S. in Analytics and B.S. in Electrical Engineering, both from Northwestern University. She blogs about analytics and pop culture on A Dash of Data. Her blog post, "How Text Messages Change From Dating to Marriage" made it onto the front page of Reddit, gaining over half a million views in the first week. She is passionate about teaching and mentoring, and loves using data to tell fun and compelling stories.

Check out content by and/or featuring Alice:

Podcast: Data Science for Beginners (Learn to Code with Me Podcast)

Talk: Support Vector Machines: A Visual Explanation with Sample Python Code

Article: Geek of the Week (GeekWire)

Joe eddy

Joe Eddy

Senior Data Scientist

Joe loves tackling unusual problems with the tools of data science and has a passion for effectively communicating quantitative ideas.

As both a math enthusiast and a former competitive debater, Joe was drawn to data science by its place at the intersection of statistics, computing, and communication. He has worked on projects ranging from quantifying and comparing story plots to building a Bayesian model of human rights abuse rates that accounts for informational bias. Before transitioning into data science, he worked in various data analytics roles, most recently as an Equity Research associate on Credit Suisse's portfolio strategy team. He holds a B.A. in Mathematics from Columbia University, and is also a Metis alumnus. He currently coaches NYU's parliamentary debate team, and in his free time he enjoys playing piano, reading, and playing board games.

Check out content by Joe:

GitHub Repository

Article: Safety in Numbers: My 18th Place Solution to Porto Seguro’s Kaggle Competition  

Sophie searcy

Sophie Searcy

Senior Data Scientist

Sophie loves extracting representations and understanding from data and pushing data science to be a more positive and inclusive discipline.

Sophie Searcy comes to Metis from Elektra, a wearable startup that is replacing haptics with electricity. At Elektra she was cofounder and CTO, designing everything from the electronics to the framework for analyzing data. Before that she worked in the CoDaS lab at Rutgers where she combined cognitive science and theoretical computer science to build models of how people and machines teach and learn. She holds masters degrees in Electrical and Computer Engineering and Psychology. She is passionate about teaching, both in theory and in practice, and about making sure that data science is primarily a tool that is used to improve people's lives.

Check out content by and/or featuring Sophie:

Article: Deep Learning from Scratch with Python

Article: A Case for Diversity

Article: Why Data Scientists Should Make a Commitment to Diversity

Chad scherrer

Chad Scherrer

Senior Data Scientist

Chad enjoys that data science gives practical approximations to the complex uncertainties of reality.

Chad comes to Metis from a diverse technical background. After earning his mathematics PhD from Indiana University, Chad joined Pacific Northwest National Laboratory to work on statistical and computational challenges ranging from homeland security to high-performance computing and machine learning research. Following several of his publications at top-tier ML conferences, he turned to probabilistic programming, then still in its infancy. He has used these systems for consulting projects for industrial clients, and has led development and publication of several new ones along the way. In his spare time, Chad enjoys a wide range of music, and practices martial arts, where he has black belts in several styles.

Check out content by and/or featuring Chad:

Article: Bayesian Optimal Pricing

Robert alvarez

Robert Alvarez

Senior Data Scientist

Robert loves to break deep technical concepts down to be as simple as possible, but no simpler.

Robert has data science experience in companies both large and small. At Intel, he used his knowledge to tackle problems in data center optimization using cluster analysis, enrich market sizing models by implementing sentiment analysis from social media feeds, and improving decision making in one of the top 5 global supply chains. At Tamr, he built models to unify large amounts of messy data across multiple silos for some of the largest corporations in the world. He earned a PhD in Applied Mathematics from Arizona State University where his research spanned image reconstruction, mathematical epidemiology and oncology. Robert is an Adjunct Professor at the Leavey School of Business where he teaches Data Science and Machine Learning. In his spare time, he is a rum judge, avid traveler, and eater of all things coconut.

Content by Robert: 

Article: Creating Diagnostic Plots in Python and How to Interpret Them

Adam wearne

Adam Wearne

Senior Data Scientist

With a background in physics and quantitative finance, Adam has applied data science and machine learning solutions in a wide variety of research settings ranging from recognizing and clustering behavioral patterns to applying natural language processing techniques to help build superior equity portfolios.  Adam is passionate about teaching and is excited to share his industry experiences with students. 

Check out content by Adam:

Paper: Using Natural Language Processing Techniques for Stock Return Predictions

Paper: Characterization of liposomes and silica nanoparticles using resistive pulse method

Kimberly fessel

Kimberly Fessel

Senior Data Scientist
Kimberly uses her background in applied math to discover data's what, why, and how
Kimberly joins Metis from MRM//McCann, a leading digital advertising agency, where she focused on helping clients understand their customers by leveraging unstructured data with modern NLP techniques.  She is passionate about data storytelling and the power of compelling data visualizations to challenge pre-conceived assumptions.  Kimberly's enthusiasm for teaching comes from her days as an academic.  She holds a Ph.D. in applied mathematics from Rensselaer Polytechnic Institute and completed a postdoctoral fellowship in math biology at the Ohio State University.  In her spare time, Kimberly likes to stay active and particularly enjoys swing dancing, rollerblading, and jogging with her dog.
Cliff clive

Cliff Clive

Senior Data Scientist
Cliff is fascinated by the human component of data science and views every problem as an opportunity to explore the biases and behavior that cause data to not always to be what it seems.
Cliff has worked on user segmentation analysis for Microsoft Office, trained language understanding models for Cortana, forecasted demand at Amazon Fresh, and spent four years as a quantitative strategist in Chicago's hedge fund industry. Prior to that he obtained master's degrees in Statistics and Economics from the University of Chicago. More recently, Cliff has taken an interest in data democratization and specifically in finding viable applications for small businesses. Cliff is also a dancer and DJ of Brazilian Zouk, and spends much of his free time studying electronic music production.
John tate

John Tate

Senior Data Scientist

John is a data scientist with experience in machine learning, cloud technologies, and business intelligence.

John joins Metis from WithumSmith+Brown, where he was a manager in their data and analytics practice. At Withum, John led engagements developing end to end solutions for clients with applications ranging from data management and cloud infrastructure to predictive analytics and business intelligence. He has taught data science for General Assembly as well as Microsoft training workshops and professional education courses for financial professionals.

Colorfulnetwork

Connect with others & expand your network.

Learn from and network with 8-12 industry speakers throughout the duration of the bootcamp, attend Metis-sponsored events and Meetups on-location and throughout your city, and network with Metis alumni and other on-site developers and entrepreneurs.

Speaker Series

Ashwin kumar Bo peng Jessica kirkpatrick

Check out some of our upcoming and past speakers to get an idea of the types of industry leaders you’ll hear from (and network with) during your time in the bootcamp.

View Upcoming and Past Speakers

Made at Metis

Micheallai

See examples of projects created by Metis bootcamp graduates. Final projects analyze all kinds of data from politics, fashion, travel, sports, public health, and more.

View Recent Alumni Projects
Come visit us! Attend an event or tour a campus near you.

Upcoming Bootcamps

San Francisco

January 7

Early Deadline:
November 12, 2018
Final Deadline:
December 3, 2018
Apply
Chicago

January 7

Early Deadline:
November 12, 2018
Final Deadline:
December 3, 2018
Apply
Seattle

January 7

Early Deadline:
November 12, 2018
Final Deadline:
December 3, 2018
Apply
Seattle

April 1

Early Deadline:
February 4, 2019
Final Deadline:
February 25, 2019
Apply
View Full Schedule

You've completed your bootcamp training.
Now let's get you hired.

One-On-Ones

We discuss salary, location, industry, and culture to best align you with the right company and career.

Speaker Series

Learn from industry experts through our Speaker Series. View upcoming and past speakers here.

Workshops

Engage in workshops like resume writing, salary negotiations, and more.

Mock Interviews

Participate in a practice technical interview with professional data scientists.

Company Site Visits

Visit hiring companies and network with their data science teams.

Career Day

Present your final passion project to employers and meet hiring companies.

Post-Graduation Support

Get ongoing career guidance, support, and access to additional resources.

Consulting Projects

Work part-time as a data science consultant while you do your job search.

Check out our 12-week career support curriculum.

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Companies where our alumni work:
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Spotify gray Annalect gray
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Etsy gray Fitbit gray
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“We’ve been working with Metis for a few years now across several different geographies. Their talent bar is extremely high, and we repeatedly hire very strong candidates from them. Their curriculum, staff, and career partners tend to produce candidates that pass our rigorous interview process and then hit the ground running once they start.”

Brennan Biddle, Capital One

Watch a Q&A with 3 bootcamp grads:

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Learn more about our Career Services:

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