This is an adaptation of my original curriculum post. Here I want to keep track of what I’ve accomplished and what comes next.

  • Books to read
    • Data Science from Scratch by Joel Grus (Mostly done! Going to revisit later.)
    • Data Science for Business by Provost & Fawcett (Reading this now!)
    • Python for Data Analysis by Wes McKinney
    • Hands on Machine Learning by Aurélien Géron
    • The Art of Data Science by Elizabeth Matsui and Roger Peng
    • OpenIntro to Statistics by David Diez et. al
  • Paid Online Courses
    • The entire Data Scientist Career Track from DataCamp. (Done! Whew!)
    • Udemy
      • Python Megacourse
      • Python for Data Analysis & Visualization
      • Python for Machine Learning
      • Deep Learning x 4
  • Free Online Courses
    • Coursera
      • Machine Learning by Andrew Ng (Working on this…)
    • Google
      • Machine Learning Crash Course
    • Udacity
      • Intro to Computer Science (Working on this…)
      • Intro to Data Science
    • Stanford
      • Statistical Inference
      • Prob – Stats
      • Statistical Learning (certified)
      • Mining Massive Datasets (certified)
      • Algorithms 1 & 2 (certified)