Apply to the industry's only accredited, full-time, immersive data science bootcamp. For 12 weeks, you receive extensive in-person instruction and personalized support from our team of Senior Data Scientists and Data Scientist TAs. Receive ongoing career support and access to networking opportunities during and after the bootcamp, so you graduate qualified to obtain a data science position.
Find details here.
Early – Nov 14, 2016
Final – Nov 28, 2016
Early – Nov 21, 2016
Final – Dec 5, 2016
Early – Feb 6, 2017
Final – Feb 20, 2017
Early – Feb 13, 2017
Final – Feb 27, 2017
Early – May 1, 2017
Final – May 15, 2017
Early – May 8, 2017
Final – May 22, 2017
Early – July 24, 2017
Final – Aug 7, 2017
Early – July 24, 2017
Final – Aug 7, 2017
For 12 weeks, you'll receive extensive in-person, on-site instruction and personalized support from our team of Senior Data Scientists and Data Scientist TAs. To get a better idea of what you’ll learn in three months, here’s our full curriculum.
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.
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.
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.
Laurie, a co-designer of the Metis curriculum, enjoys building analytical tools for a wide range of clients and helping them use data to solve their business problems. Prior to her switch to data science, she studied social neuroscience at the University of Chicago, building machine learning models of imaging data to explain how the brains of incarcerated psychopaths perceive and process emotions. Her least favorite part of data science is pruning the scope of a project down to a manageable size. Actually, that is her least favorite part of most things. Laurie is currently involved in a number of projects for Metis.
Paul comes to data science as a physics convert. He studied physics and mathematics at MIT before taking a job with a government intelligence contractor. There he entered the realm of data science doing groundbreaking work with large scale text analytics via a technology called Latent Semantic Indexing (LSI). In this context, LSI allowed analysts to tease out deep yet subtle relationships between entities, concepts, etc. from hundreds of millions of raw text documents. During this time, he also taught night courses for professionals aspiring to be data scientists.
Michael comes to Metis from General Electric where he worked to establish their data science strategy and capabilities for field services and to build solutions supporting global operations, risk, engineering, sales, and marketing. He also taught data science and machine learning for General Assembly. Prior to GE, Michael spent several years as a data scientist working on problems in credit modeling at Kabbage and corporate travel and procurement at TRX. Michael holds a Bachelor's degree in Mathematics and a Master's degree in Computational Science and Engineering from the Georgia Institute of Technology where he also spent 3 years working on machine learning research problems related to computational biology and bioinformatics.
Holding a Master's degree in Economics, Joel has crafted data-driven models and solutions in business, finance, central banking, and academia. He is fascinated by the way data and algorithms are transforming the world around us. In addition to his work with marketing, media, financial, and economic data, he has built algorithms for artificial intelligence, including the self-driving car and a Bayesian card-sorting game. Joel has multiple years of teaching experience in Probability and Statistical Inference, Mathematics, and Economics.
Rumman comes to data science from a quantitative social science background. Prior to joining Metis, she was a data scientist at Quotient Technology, where she used retailer transaction data to build an award-winning media targeting model. Her industry experience ranges from public policy, to economics, and consulting. Her prior clients include the World Bank, the Vera Institute of Justice, and the Los Angeles County Museum of the Arts. She holds two undergraduate degrees from MIT, a Masters in Quantitative Methods of the Social Sciences from Columbia, and she is currently finishing her Political Science PhD from the University of California, San Diego. Her dissertation uses machine learning techniques to determine whether single-industry towns have a broken political process.
Andrew comes to us from LinkedIn, where he worked as a data scientist, on projects ranging from executive dashboarding, education, inferring profiles and skills standardization. He is passionate about helping people make rational decisions and building cool data products. Prior to that he worked on fraud modelling at IMVU (the lean startup) and studied applied physics at Cornell. Andrew grew up on a sheep farm in North Idaho. He loves snowboarding, traveling, scotch and reading about all kinds of nerdy topics.
Brian comes to Metis from PCCI where he led a team of data scientists building real-time predictive models for clinical decision support. Before that he was Chief Mathematician at Guardian Analytics, where he pioneered the development of fraud detection algorithms based on Bayesian behavioral modeling. He has an A.B. from Harvard and a Ph.D. from Brown, and has held teaching and research positions at the University of Washington, UC-Berkeley, and the American University in Cairo. He enjoys biking, swimming, traveling, and poker.
Before coming to Metis, Lingqiang got her PhD in Computational Neuroscience at Boston University, building machine learning models to decode different attentional states of the brain from neuroimaging data. She also studied the connectivity patterns of cortical nodes when the brain is at rest, a technique that sheds light into early detection of traumatic brain injuries (read: war veterans and football players etc). She later worked at an e-commerce company building search and recommendation tools. In her spare time, Lingqiang likes mystery novels and all things Sherlock. Her favorite food is the spicy cumin lamb skewers (yangrou chuan), a quintessential street food in northern China.
Reshama has over 10 years experience working in the pharmaceutical industry as a biostatistician. Reshama has her BA and MS in statistics (Rutgers University). She is also a francophile (Minor in French, Rutgers). She completed her MBA from NYU Stern in 2014 with a focus on business analytics and technology.
Ramesh comes to Metis from FiveStars Loyalty where he built data products to help merchants connect with their customers. Prior to that, he was an Insight Data Engineering fellow where he worked on big data applications. Previously, he co-founded Engineeroom360, where he built machine learning models to identify products trending in an online store and automatically move them based on its relevance to the consumer. Ramesh spends his spare time playing with his two sons and tinkering with Arudino / RaspberryPi.
Click photos for bios
International students may attend the Metis Data Science Bootcamp in New York City on an M-1 Visa. Details here.
The story is a familiar and unfortunate one:
Women make up less than one-third of all employees in the tech sector. Tech companies employ an average of 12.33 percent female engineers. Women contribute to just 1.2 percent of open source software.
Only four percent of people in software development, application and systems jobs are African-American and five percent are Hispanic or Latino.
Women of color represent less than three percent of the people in technology fields.
As a country, we need to reverse these trends and create more avenues for talented individuals from underrepresented demographic groups and communities to help drive our future economic growth. This scholarship is a step toward supporting a more diverse workforce. Scholarship funds are applied to Metis tuition only and are not transferable.
Scholarship eligibility is subject to validation. Metis has sole discretion in the award of the Scholarship and the right to revoke the Scholarship offer for prospective applicants at any time.
Underrepresented minority 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.
Third Party Financing: We partner with Skills Fund, an innovative financing company that offers financing options for students accepted to our bootcamp.
Each Monday - Friday consists of, on average, two to three hours of group classroom instruction and four to six hours of practical skill development and project work.
Applicants must have some experience programming (writing code) and studying or using statistics.
Once students are enrolled in the bootcamp, they are granted immediate access to our pre-work materials – a structured program 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. The pre-work materials ensure that students are ready to succeed in the bootcamp. All exercises must be completed before the first day of class.
Students jump right in, working with real data as they become acclimated with the core toolset that will be used for the remainder of the bootcamp. Project #1 (Benson) is completed.
We dive into some deeper content, focusing on regression, along with fundamental concepts for statistics and probability. We tackle web scraping (used to gather data for the Project #2), stored in flatfiles using fundamental Python I/O.
We introduce Bayes Theorem, another fundamental skill in statistical reasoning, and refine our regression models as we learn about regression model assumptions, transformations, and overfitting. Project #2 (Luther) is completed.
Concepts learned in regression are broadened by extending to the parent family of supervised learning. Students learn a suite of classification algorithms and concepts of bias-variance tradeoff.
Students learn more supervised learning algorithms. We also tackle machine learning topics that involve deeper use of scikit-learn functionality, introducing automated methods of feature selection, options for estimation including stochastic gradient descent, and advanced metrics for model evaluation.
We dive into unsupervised learning and natural language processing (NLP), and go deep into core machine learning concepts like the curse of dimensionality, dimension reduction, vector spaces, and distance metrics. Finally, to support the upcoming Project Fletcher, we introduce NoSQL databases and RESTful APIs, as well as begin culling project data from web APIs to be stored in MongoDB.
Leading into Project #4, we continue with NLP tools, including topic modeling, latent dirichlet allocation, and word2vec. We add several more unsupervised learning algorithms and learn formally about varieties of, and considerations in, choosing distance metrics. Project #4 (Fletcher) completed.
We have final lectures and challenges for big data tools and techniques, including MapReduce, Spark, Hadoop, Hive, Pig, and large datastores. Students then transition into full-time focus on their passion projects, working with instructors to build them out for Career Day. They hone their presentations over many iterations to showcase their work at its best! Project #5 (Kojak aka Passion Project) completed.
Upon graduating from the Data Science Bootcamp, students are prepared to pursue jobs as entry-level data scientists or data analysts, or in data science related roles. This means a student will:
Have a fluid understanding of, and practical experience with, the process of designing, implementing, and communicating the results of a data science project.
Be capable coders in Python and at the command line, including the related packages and toolsets most commonly used in data science.
Understand the landscape of data science tools and their applications, and will be prepared to identify and dig into new technologies and algorithms needed for the job at hand.
Have introductory exposure to modern big data tools and architecture, such as Hadoop and Spark; they will know when these tools are necessary and will be poised to quickly train up and utilize them in a big data project.
Learn from, and network with, 10-15 industry speakers during the duration of the bootcamp, attend Metis-sponsored events and Meetups on-location and throughout the city, and network with Metis alumni and other on-site developers and entrepreneurs.View our speaker series
The Importance of Visual Contrast When Communicating with Datarsvp
Liv Buli discusses what it means to be a Data Journalist and talks about the skills necessary to create compelling content.watch the video here
Leave fully qualified for a job in data science, and count on our Career Services to work closely with you, from day one of the bootcamp to day one of your new job. We provide a 12-week Careers Curriculum – concurrent with the bootcamp curriculum – as well as post-graduation support, to ensure you are as successful as possible in your job search.Join our employer network
We discuss salary, location, industry, and culture to best align you with the right company and career.
Learn from industry experts through our Speaker Series. View upcoming and past speakers here.
Engage in workshops like resume writing, salary negotiations, and more.
Participate in a practice technical interview with professional data scientists.
Visit hiring companies and network with their data science teams.
Present your final passion project to employers and meet hiring companies.
Get ongoing career guidance, support, and access to additional resources.
Work part-time as a data science consultant while you do your job search.