Getting Started with Data Science

Episode 9 was all about getting started with data science. Here’s the list of resources we touched on (and some we didn’t) in the episode. Data science is a broad field, and learning what you need to know to make it your profession can seem daunting. The most important thing is to dive in, find problems you like to solve, and learn as go. 

• Books
  1. Data Science
     1. Creating a Data Driven Culture
     2. Machine Learning for Hackers
     3. Programming Collective Intelligence
  2. General Data
     1. The Victory Lab
     2. Money Ball
     3. The Signal and the Noise
  3. Programming
     1. Code Complete
     2. The Phoenix Project
     3. Design Patterns
• MOOCs
  1. Andrew Ng’s Machine Learning course
  2. Lynda’s data science playlist
  3. Intro to data science on Udacity
  4. Thinkful’s data science course
     1. Online masters degree courses
        1. UC Berkley
        2. Carnegie Mellon
        3. Northwestern
• Side projects
  1. Kaggle
  2. Email Chris and Jonathon
• Blogs
  1. yhat
  2. David Behnke
  3. Walking Randomly
  4. Hunch
  5. Simply Statistics
  6. FastML
• NBViewer
• Work with an expert
• Stack Overflow
  1. Sites in the Stack Exchange network dedicated to data science
     1. Data Science
     2. Stats
     3. Computer Science
• GitHub
  1. Open Data Science Masters
  2. Hilary Mason
  3. Sean Taylor
• Twitter
  1. Andrew Flowers
  2. DJ Patil
  3. Hilary Mason
  4. Chris Diehl
  5. Sebastian Raschka
  6. Cam Davidson-Pilon
  7. Kirk Borne
  8. Carla Gentry