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.Schedule a Chat with Admissions
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
Experience with programming
Experience with stats
25 hours minimum of academic pre-work and variable hours to setup.
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
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 ProjectDownload Full Syllabus
Consider one of our part-time professional development courses.
Gain the skills you need + apply your tuition paid towards the bootcamp.
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.
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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.
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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.
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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.
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Talk: Roadmap: How to Learn Machine Learning in 6 Months (Chicago Data Science Conference 2017)
Article: Recommendation Engines for Dummies
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.
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Talk: Fortune-Telling with Python: Intro to Facebook Prophet (ChiPy Users Group)
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.
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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.
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Article: Geek of the Week (GeekWire)
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.
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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.
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Article: A Case for Diversity
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.
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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 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.
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.