Data Science Bootcamps

accredited + in-person instruction + employment support

New York City

Fall bootcamp: September 19, 2016 - December 14,2016

Early Application Deadline*: July 25, 2016
Final Application Deadline: August 8, 2016

San Francisco

Fall bootcamp: September 19, 2016 - December 14, 2016

Early Application Deadline*: July 25, 2016
Final Application Deadline: August 8, 2016

Notice: Tuition increasing June 20. Details

  • Get Skilled

    Learn Data Science in 12 weeks with 100% in-person instruction from expert data scientists.

    Watch a Q&A with instructor Irmak Sirer to Learn More

  • Get Connected

    A busy Speakers and Events schedule and daily project work with instructors ensures that you are well-networked by graduation.

  • Get Hired

    Leave fully qualified for an entry-level data scientist job. Career Support is available to all graduates.

  • Julia Lintern

    Sr. Data Scientist

  • Vinny Senguttuvan

    Sr. Data Scientist

  • Paul Burkard

    Sr. Data Scientist

  • Laurie Skelly

    Sr. Data Scientist

  • Michael Galvin

    Sr. Data Scientist

  • Joel Wesley

    Sr. Data Scientist

  • Rumman Chowdhury

    Sr. Data Scientist

  • Andrew Blevins

    Sr. Data Scientist

  • Brian Lucena

    Sr. Data Scientist

  • Reshama Shaikh

    Data Scientist - TA

  • Ramesh Sampath

    Data Scientist - TA

  • Jennifer Raimone

    Career Advisor - NYC

  • Andrew Savage

    Career Advisor - SF

  • Leah Nicolai

    Program Manager - NYC

  • Heather Lockhart

    Program Manager - SF

The Details

This bootcamp runs in-person for 12 weeks, Monday through Friday, from 9 am - 6 pm. Download the week-by-week syllabus. Listen to instructor Irmak Sirer describe our philosophy and program in detail.

The Prerequisites

Applicants must have some previous experience programming (writing code) and studying or using statistics.

The Outcome

Upon graduating, you will be comfortable designing, implementing, and communicating the results of a data science project, including knowing the fundamentals of data visualization and having introductory exposure to modern big data tools and architecture such as the Hadoop stack. You should feel confident pursuing a job as an entry-level data scientist or data analyst. Read our syllabus

Total Cost: $14,000 for 12 weeks

Notice: Tuition Increasing June 20. Details

We offer a $2,000 scholarship for women,underrepresented minority groups, and veterans or members of the U.S. military

Third Party Financing: We have partnered with Skillsfund, an innovative financing company that offers financing options for students accepted to Metis.

Bootcamp Structure & Syllabus

The bootcamp experience is intense, but we aim to maximize learning while preventing burn-out. Metis believes that a student’s brain is like a muscle, and to grow without injury the brain must take time to recover. Each Monday - Friday consists of, on average, three hours of group classroom instruction and five hours of practical skill development and project work.

Download the week-by-week syllabus

Online Pre-Work

We’ll provide a Command Line Crash Course, tutorials to become familiar with Python, and a number of package installation tutorials (i.e., numpy, scipy, pandas, scikit.learn), as well as some preliminary statistics work. Test-out/check-out modules will let students know when they are “prepared enough” for class.

After the at-home pre-work phase, we will convene in class and spend our first 8 weeks together doing iterative, project-centered skill-acquisition. Over the course of four data science projects we’ll "train up" different key aspects of data science, and results from each project will be added to the students' portfolios. In the last four weeks, students build out and complete their individual Passion Projects, culminating in a Career Day reveal of their work to representatives from our Metis Metis Hiring Network.


Introduction to the Data Science Toolkit

Students complete an entire bite-sized data science project from start to finish. They start using Git for version control and the IPython environment with the pandas and matplotlib packages to perform exploratory statistical analyses and visualizations.

Read more about Week 1

Topics and Tools Covered in Week 1 include:

  • Review probability and statistics, including distributions, bootstrapping, hypothesis testing, maximum likelihood estimation, and Bayes theorem (This review spans the first three weeks.)
  • Use UNIX, Git, and IPython to organize data science project resources
  • Load and manipulate data with the pandas Python package
  • Visualize results using the matplotlib Python package
  • Communicate data science results


In the first week, students work in small groups using MTA turnstile data to estimate the volume of people on the street, so that (theoretical) nonprofits and companies can deploy street teams efficiently. The students are provided with the data and guided through exploratory data analysis and plotting so they can focus on new tools, brainstorming, and communication.


Design Process and Web Scraping

In preparation for Project 2, students start to learn one of the most important tools a data scientist uses: the iterative design process. They learn tools for web scraping and start fitting simple models to data. Also, they are introduced to cloud computing and work on remote servers.

Read more about Week 2

Topics and Tools Covered in Week 2 include:

  • Use the design process to iteratively explore the possible ways that a problem can be solved
  • Create and work in a virtual environment on a cloud computing service
  • Use Python’s Requests and Selenium packages to obtain data from web pages
  • Use Python’s Beautiful Soup package to parse the content of a web page to find useful data for subsequent analysis
  • Use the design process to iterate the concept for the Unit 2 projects
  • Complete a primer on web fundamentals including HTML, CSS, and JavaScript


Regression, Communicating Results

Students go in-depth on regression using scikit-learn and matplotlib. Choosing among the analysis methods and approaches to reporting their results, students finish the second project and present their findings.

Read more about Week 3

Topics and Tools Covered in Week 3 include:

  • Apply regression modeling with Python packages scikit-learn and statsmodels
  • Load, clean, and explore data using Python packages pandas, numpy, and matplotlib/pyplot
  • Experience how the design process influences analysis and results
  • Complete second project and communicate results to each other


For the first pass at machine learning, students dive deep into prediction with regression models. They experience the beauty of flat files, and learn to scrape information from web sites using tools like Python Requests, Beautiful Soup, and Selenium. After scraping together some movie box office data, students find and scrape more resources on their own and present their movie industry regression predictions to the class.


Databases and Introduction to Machine Learning Concepts

Students cover relational databases such as SQL and more ways of obtaining, cleaning and maintaining data. They are introduced to the concepts of machine learning and exposed to classification and supervised learning with a few examples such as logistic regression and KNN. They also discuss different types of feasibility related to data science questions and projects.

Read more about Week 4

Topics and Tools Covered in Week 4 include:

  • Use SQL databases to store and organize data
  • Explore supervised learning techniques including decision trees and random forests
  • Access stored data with MySQL querying language
  • Complete a deep applied survey of classification (supervised learning) techniques, such as logistic regression, k-nearest neighbors, etc.
  • Design and evaluate the computational feasibility of a third data project

Week 5

Machine Learning, Supervised Learning Techniques, Naive Bayes Algorithm

Students dig into more details and more algorithms for supervised learning including SVM, decision trees and random forests; techniques for feature selection and feature extraction; and concepts and applications for deep learning. Students choose to apply one or more of these algorithms as part of this Unit’s project.

Read more about Week 5

Topics and Tools Covered in Week 5 include:

  • Connect regression modeling to the broader family of machine learning techniques
  • Use supervised learning on Project 3; work in groups simulating in-house data science teams
  • Refine models with feature selection and feature extraction
  • Evaluate the efficacy and computational feasibility of various ML algorithms in different contexts

Week 6

JavaScript and D3.js

Students visualize projects using D3, a favorite tool for flexible and attractive presentations of data and relationships. Since D3 is a JavaScript library, students learn JavaScript essentials and the incorporation of other js libraries (jQuery, Bootstrap, etc.) that make the job much easier.

Read more about Week 6

Topics and Tools Covered in Week 6 include:

  • Learn the fundamentals of JavaScript
  • Explore basic principles of good visual design and communication
  • Use D3 to create interactive visualizations that are functional in any browser
  • Create novel data visualizations with D3 to illustrate Unit 3 project results in blog post format


Students form small groups that each work as an internal data science team at a fictional company in the insurance industry (details are left to the students to determine). Supervised learning algorithms and relational databases have been covered in class. Students work on their own classification models that fit within the overall goals of the company and the team. During McNulty, students perform a deep dive into the visualization package D3 and create their own APIs on the Python Flask micro framework to serve data from their databases to their visualizations.

Week 7

APIs, Data Collection Methods, NoSQL Storage, Web Apps with Flask

The project for the fourth unit involves text data. Students round out data acquisition methods with APIs and online database servers. Students also learn about NoSQL databases and start using MongoDB.

Read more about Week 7

Topics and Tools Covered in Week 7 include:

  • Use Python to download data from an API
  • Use NoSQL databases; parse and store unstructured data in MongoDB
  • Review database selection: non-relational (NoSQL) databases vs. relational (SQL) databases vs. no database (flat files)
  • Merge disparate data sets to practice ‘data munging’
  • Design and propose initial data collection for Unit 4 project

Week 8

Natural Language Processing (NLP)

Students analyze the text data collected in the previous week and learn about NLP algorithms. More unsupervised learning algorithms are explored. Students dive deeper into unsupervised learning and more algorithms, covering K-means, hierarchical clustering, mixture models and topic models. They also learn about how large amounts of data are handled, discussing parallel computing and Hadoop MapReduce. Project 4 presentations are presented as lightning talks.

Read more about Week 8

Topics and Tools Covered in Week 8 include:

  • Use Python’s Natural Language ToolKit and TextBlob library to perform natural language analyses on text data
  • Apply deep learning/neural networks, DBSCAN, dimensionality reduction (with principle components analysis).
  • Algorithms including KD-trees and locality sensitive hashing are learned.
  • Survey K-means, hierarchical clustering, and other “unsupervised learning” algorithms; applications on real data
  • Reflect on the strengths and weaknesses of each algorithm and its appropriate use
  • Outline “the data science stack” and design choices in data engineering fault tolerant systems
  • Set up Hadoop environment on cloud servers
  • Use Hadoop via Python bindings to write customized map-reduce jobs from scratch and run in Hadoop cloud environment
  • Discuss Hadoop: history & ecosystem, when & why, hype & reality
  • Complete Project 4 and present findings to class in lightning talk format


The last guided project focuses on unsupervised learning and NLP algorithms, NoSQL databases, and API data collection. Students work individually and have very few constraints for the design of this project.

Weeks 9-12

Final Project

Students work full time on their Final Projects, which they have been slowly designing through the first eight weeks. They also learn more about cloud computing, system architectures and feasibility evaluations.

Read more about Weeks 9-12

Topics and Tools Covered in Weeks 9-12 include:

  • Use the design process to isolate an appropriate problem to solve
  • Evaluate the computational feasibility of the problem
  • Choose data sources that can be used to address the problem
  • Design and implement an appropriate computational architecture
  • Design and implement an appropriate set of analysis steps
  • Design and develop a data visualization to clearly convey the results of the analysis to a layperson
  • Assemble final portfolio and present project at Career Day


Students are free to use anything covered in class or to learn something new to answer the questions they want to address. Some students know what will be their final project at the admissions stage. Others embark on entirely new turf. Every student works intensely and challenges him or herself to create something cool, interesting, useful, or worthwhile.


Upon graduating from the Data Science bootcamp, a student will be prepared to take an entry-level position on a Data Science team as a data scientist or data analyst. This means a student will:

  1. Have a fluid understanding of and practical experience with the process of designing, implementing, and communicating the results of a data science project.
  2. Be capable coders in Python and at the command line, including the related packages and toolsets most commonly used in data science.
  3. Understand the landscape of data science tools and their applications, and be prepared to identify and dig into new technologies and algorithms needed for the job at hand.
  4. Know the fundamentals of data visualization and have experience creating static and dynamic data visuals using JavaScript and D3.js.
  5. Have introductory exposure to modern big data tools and architecture such as the Hadoop stack, know when these tools are necessary, and be poised to quickly train up and utilize them in a big data project.

Fall bootcamp: September 19, 2016 - December 14,2016

Early Application Deadline*:
July 25, 2016
Final Application Deadline: August 8, 2016

Fall bootcamp: September 19, 2016 - December 14, 2016

Early Application Deadline*:
July 25, 2016
Final Application Deadline: August 8, 2016


Metis, a d/b/a of Kaplan Inc., is accredited by the Accrediting Council for Continuing Education & Training (ACCET), a U.S. Department of Education nationally recognized agency.

Supporting Documentation: CA Catalog. Information will be posted at a later date. 2016 Data collection still in progress.

Licensed by the State of New York