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, 9:30am - 6:00pm
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.
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.
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.
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.
Debbie, Chief Data Scientist at Metis, uses her "physics glasses" to solve challenging real-world problems and promote critical thinking.
Julia, a Metis data science instructor, loves using data to study and explore phenomena.
Vinny, a Metis data science instructor, views data as a facet of perception.
David has a penchant for solving novel problems with machine learning and revels in sharing knowledge with others.
Jonathan Balaban is a consultant, data scientist, and entrepreneur with ten years of private, public, and philanthropic experience.
Zach loves looking for kernels of truth hidden within data and using that knowledge to bring understanding into novel situations.
Krishna comes to Metis from the advertising world having worked in a series of startups enabling online and television advertising.
Seth loves using math to turn organizations' data into money - and teaching others how to do the same.
Andrew is passionate about helping people make rational decisions and building cool data products.
Roberto is a scientist with a strong background in data analysis and image/signal processing.
Alice, a Metis data science instructor, enjoys making complex things easy to understand.
Joe loves tackling unusual problems with the tools of data science and has a passion for effectively communicating quantitative ideas.
Sophie loves extracting representations and understanding from data and pushing data science to be a more positive and inclusive discipline.
Chad enjoys that data science gives practical approximations to the complex uncertainties of reality.