Data science is the interdisciplinary practice of managing and analyzing various kinds of data to produce usable insights.

Step #1: Learn itStep #2: Apply itStep #3: Stay up-to-date

Access the following free resources from across the web. Note that Bootstrap Ed does not own any of the resources featured on this page. 

Data Science Overview

  • Innoarchitech – What Is Data Science, and What Does a Data Scientist Do?
  • Thinkful – What is Data Science?
  • MIT – Introduction to Computational Thinking and Data Science Fall 2016 (6.0002), Lecturer: John Guttag
    • 15 sessions, approximately 45 minutes each
    • Playlist
  • Cognitive Class – Introduction to Data Science
  • David Evans – Introduction to Computing
  • MIT – Introduction to Computer Science and Programming Spring 2011 (6.00SC), Lecturer: John Guttag
    • 38 sessions, approximately 50 minutes each
    • Playlist
  • Cognitive Class – Python for Data Science
  • Harvard – Introduction to Computer Science I, Lecturer: David Malan
    • 20 sessions, approximately 50 minutes each
    • Playlist
  • ComputerScience.org – Computer Programming Languages
  • Google Developers – Foundations of Programming
  • Clare Corthell – The Open Source Data Science Masters
    • Note that only about half of the suggested materials are available for free
    • Curriculum

..

Math for Data Science

  • MIT – Linear Algebra (18.06), Lecturer: Gilbert Strang
    • 35 lectures, approximately 45 minutes each
    • Playlist
  • MIT – Single Variable Calculus (18.01), Lecturer: David Jerison
    • 39 lectures, approximately 50 minutes each
    • Playlist
  • MIT – Homework Help for Single Variable and Multivariable Calculus (18.01SC, 18.02SC); Lecturers: Christine Breiner, David Jordan, Joel Lewis
    • 87 videos (problem set solutions), approximately 5-10 minutes each
    • Playlist
  • MIT – Multivariable Calculus (18.02), Lecturer: Denis Auroux
    • 35 lectures, approximately 50 minutes each
    • Playlist
  • MIT – Homework Help for Multivariable Calculus (18.02SC), Lecturer: Joel Lewis
    • 70 videos (problem set solutions), approximately 5-10 minutes each
    • Playlist
  • MIT – Calculus of Complex Variables, Differential Equations, and Linear Algebra, Lecturer: Herbert Gross
    • 20 lectures, approximately 35 minutes each
    • Playlist
  • Khan Academy – Calculus
    • 199 videos, approximately 5-15 minutes each
    • Playlist
  • MIT – Fundamentals of Statistics (18.650), Lecturer: Philippe Rigollet
    • 24 lecturers, approximately 75 minutes each
    • Playlist
  • Khan Academy – Statistics
    • 68 lectures, approximately 5-15 minutes each
    • Playlist
  • MIT – Introduction to Probability (6-012), Lecturer: John Tsitsikli
    • 20 lectures split into shorter parts, approximately 5-15 minutes each
    • Playlist
  • Harvard – Probability (STAT-E 110), Lecturer: Joe Blitzstein
    • 35 lectures, approximately 45 minutes each
    • Playlist
  • MIT – Mathematics for Computer Science Spring 2015 (6.042J), Lecturer: Albert Meyer
    • 110 videos, approximately 5-25 minutes each
    • Playlist
  • MIT – Introduction to Algorithms (SMA 5503) Fall 2005 (6.046J / 18.410J); Lecturers: Charles Leiserson, Erik Demaine
    • 23 lectures, approximately 75 minutes each
    • Playlist
  • MIT – Design and Analysis of Algorithms Spring 2015 (6.046J), Lecturer: Srinivas Devadas
    • 24 lectures, approximately 20-80 minutes each
    • Playlist
  • Jay Wengrow – A Common-Sense Guide to Data Structures and Algorithms: Level Up Your Core Programming Skills
  • MIT – Advanced Data Structures Spring 2012 (6.851), Lecturer: Erik Demaine
    • 22 lectures, approximately 80 minutes each
    • Playlist
  • MIT – Signal Processing on Databases Fall 2012 (RES.LL-005 D4M), Lecturer: Jeremy Kepner
    • 18 sessions, approximately 10-60 minutes each
    • Playlist

..

Machine Learning and Artificial Intelligence

  • Google Developers – Machine Learning Crash Course
  • Google Developers – Machine Learning Glossary
  • Oxford – Machine Learning: 2014-2015
    • 14 lectures, approximately 50 minutes each
    • See slides and exercises
    • Course page
  • MIT – Introduction to Deep Learning (6.S191), Lecturer: Nick Locascio
    • 6 lectures, approximately 30 minutes each
    • Playlist
  • Cognitive Class – Deep Learning Fundamentals
  • Caltech – Learning from Data, Lecturer: Yaser Abu-Mostafa
  • Stanford – Machine Learning, Lecturer: Andrew Ng
  • Rutgers – Machine Learning, Lecturer: Alexander Kogan
    • 20 lectures, approximately 20-80 minutes each
    • Playlist
  • MIT – Artificial Intelligence Fall 2010 (6.034), Lecturer: Patrick Winston
    • 30 lectures, approximately 50 minutes each
    • Playlist

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Careers

  • KDnuggets – How to Become a Data Scientist: The Definitive Guide
  • Springboard – 109 Data Science Interview Questions and Answers for 2019
  • Udacity – Data Science Interview Prep
  • Hooked on Data – Red Flags in Data Science Interviews

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R Resources

Datasets

  • UC Irvine – Machine Learning Repository
  • Analytics Vidhya – 24 Ultimate Data Science Projects To Boost Your Knowledge and Skills (& can be accessed freely)

Complete the following exercises to apply your newly acquired knowledge.

  • SKILL BUILD
    • Work through the tutorial exercises on Guru 99, especially the “AI” and “BIG DATA” sections.
  • ANALYZE
    • Using one of the free datasets in UC Irvine’s Machine Learning Repository, identify a question, develop an analysis method, execute your analysis, and interpret your findings.
  • COMPETE
    • Participate in or follow competitions on Kaggle, a platform where individuals work alone or in teams on preset data analytics and modeling challenges.

Engage with the field on an ongoing basis. Note that Bootstrap Ed does not own any of the resources featured on this page. 

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