Learning | Path for Data Scientist

  • Portfolio
  • Python Pandas / Numpy /SciPy
  • Apache Spark
  • Apache Hadoop


Mathematics for Data Science

Linear Algebra

  1. Khan Academy Linear Algebra series (beginner friendly).
  2. Coding the Matrix course (and book).
  3. 3Blue1Brown Linear Algebra series.
  4. fast.ai Linear Algebra for coders course, highly related to modern ML workflow.
  5. First course in Coursera Mathematics for Machine Learning specialization.
  6. “Introduction to Applied Linear Algebra — Vectors, Matrices, and Least Squares” book.
  7. MIT Linear Algebra course, highly comprehensive.
  8. Stanford CS229 Linear Algebra review.


  1. Khan Academy Calculus series (beginner friendly).
  2. 3Blue1Brown Calculus series.
  3. Second course in Coursera Mathematics for Machine Learning specialization.
  4. The Matrix Calculus You Need For Deep Learning paper.
  5. MIT Single Variable Calculus.
  6. MIT Multivariable Calculus.
  7. Stanford CS224n Differential Calculus review.

Statistics and Probability

  1. Khan Academy Statistics and probability series (beginner friendly).
  2. A visual introduction to probability and statistics, Seeing Theory.
  3. Intro to Descriptive Statistics from Udacity.
  4. Intro to Inferential Statistics from Udacity.
  5. Statistics with R Specialization from Coursera.
  6. Stanford CS229 Probability Theory review.

Bonus materials

  1. Part one of Deep Learning book.
  2. CMU Math Background for ML course.
  3. The Math of Intelligence playlist by Siraj Raval.