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Data Science Bootcamps

Accredited. Immersive.
In-Person. Career Support.

This full-time, 12-week data science experience hones, expands, and contextualizes the skills brought in by our competitive student cohorts. Incorporating traditional in-class instruction in theory and technique, students use real data to build a five-project portfolio to present to potential employers and have access to full career support throughout and after the bootcamp.

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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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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

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

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

Julia lintern

Julia Lintern

Senior Data Scientist

Julia, a Metis data science instructor, loves using data to study and explore phenomena.

Julia comes to Metis after working at JetBlue as a quantitative engineer. While at JetBlue, she used quantitative analysis and machine learning methods to provide continuous assessment of the aircraft fleet. Julia began her career as a structures engineer, where she designed repairs for damaged aircraft. In 2011, she transferred into a quantitative role at JetBlue and began her M.A. in Applied Math at Hunter College, where she focused on visualizations of various numerical methods including collocation and finite element methods. She discovered a deep appreciation for the combination of mathematics and visualizations and found data science to be a natural extension. Julia has also worked as an Expert in Residence for a company that provides data science training. She continues to collaborate on various projects including the development of stock trading algorithms. During certain seasons of her career, she has also worked on creative side projects such as Lia Lintern, her own fashion label.

Check out content by and/or featuring Julia:

Talk: Deep Learning with Keras (Strata Data Conference 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

Zach miller

Zach Miller

Senior Data Scientist

Zach loves looking for kernels of truth hidden within data and using that knowledge to bring understanding into novel situations.

Zach has spent the last eight years collecting, cleaning, and analyzing data from particle accelerators all over the United States, putting to use his PhD in nuclear physics. He's helped pioneer statistical analysis, simulation, and data collection techniques to make measurements from hundreds of terabytes of physics data. During these efforts, Zach has worked with Los Alamos National Laboratory, Brookhaven National Laboratory, the University of Kentucky Accelerator Lab, and the University of Illinois at Chicago. His passion is combining technical and programming skills with the educational chops he developed as a professor in order to create an intuitive, hands-on learning experience. In his free time, Zach plays numerous instruments, climbs rocks, and eats green chiles.

Check out content by and/or featuring Zach:

Talk: Roadmap: How to Learn Machine Learning in 6 Months (Chicago Data Science Conference 2017)

Article: Dear Aspiring Data Scientists, Just Skip Deep Learning (For Now)

Article: Recommendation Engines for Dummies

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

Roberto reif

Roberto Reif

Senior Data Scientist

Roberto is a scientist with a strong background in data analysis and image/signal processing.

Roberto comes to Metis from Sensoria Inc., where he led the signal processing team. He has worked in applications in the healthcare, internet of things, and business intelligence markets. He received a PhD in Biomedical Engineering from Boston University, and has co-authored several scientific publications, book chapters, and patents. He enjoys hiking and soccer.

Check out content by and/or featuring Roberto:

Talk: Visualization: Simplified

Article: Displaying Images in Tableau (with some help from Python)

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

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.

Brendan herger

Brendan Herger

Senior Data Scientist

Brendan Herger enjoys bridging the gap between data science and engineering, to build and deploy data products.

Brendan brings a unique combination of machine learning, deep learning, and software engineering skills. In his previous work at Capital One and startups,  he has built authorization fraud, insider threat, and legal discovery automation platforms. In each of these cases he's lead a team of data scientists and data engineers to enable and elevate his client's business workflow (and capture some amazing data).

When he's not knee deep in a code base, Brendan can be found traveling, sharing his collection of Japanese teas, and playing board games with his partner in Seattle. 

Check out content by Brendan:

Package: keras-pandas, a python package that allows users to rapidly build and iterate on deep learning models.

Blog post: Starwars Spoilers, a look at whether Reddit posts contain spoilers, using Deep Learning and Natural Language Processing

GitHub Repository

Damien martin

Damien Martin

Senior Data Scientist

Damien looks to data to build simplified models of the world, to help us understand and reason about it.

Damien has experience bringing esoteric subjects down to Earth. After completing a PhD in cosmology, he spent his time developing project-based learning in physics, math and computer science at small liberal arts colleges. He loves developing projects that are relevant and interesting, while still highlighting the important concepts. After leaving the classroom, Damien worked as a curriculum designer and data scientist for a small San Francisco recruiting startup for people looking for coding jobs. When he's not working on lectures, Damien can be found studying Wing Tsun, playing Go, or on a photo hike.

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

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

New York City

January 7

Early Deadline:
November 12, 2018
Final Deadline:
December 3, 2018
Apply
San Francisco

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
Chicago

January 7

Early Deadline:
November 12, 2018
Final Deadline:
December 3, 2018
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
Facebook gray Airbnb gray
Etsy gray Fitbit gray
Capital one gray Ibm gray
Apple gray Grubhub 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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