Data Science Curriculum

Data science integrates technology, science and art, it involves statistics, optimization, matrix analysis, theoretical computer science, programming, etc. For courses, the following are suggestions for specific course settings and related materials:

Table of Contents

• Mathematics
  • Calculus
  • Linear Algebra
  • Discrete Structures
  • Elementary Statistics and Probability
  • Linear Model
  • Optimization
• Computer Science
  • Python for Data Science
  • Algorithms and Data Structure
  • Database Systems
  • Theory of Computation
  • Machine Learning
  • Data Mining
  • Artificial Intelligence

Curriculum

• Calculus

  • Textbooks
    • Calculus (Gilbert Strang)
  • Courses
    • Single Variable Calculus with Prof. David Jerison (MIT open course)
    • Multivariable Calculus with Edward Frenkel (UC Berkeley: MATH 53)

• Linear Algebra

  • Textbooks
    • Linear Algebra and Its Applications (Strang)
  • Courses
    • Gilbert Strang: Linear Algebra (MIT open course)

• Discrete Structures

  • Textbooks
    • Discrete Mathematics and Its Applications (Kenneth, Rosen.)
  • Courses
    • Mathematics for Computer Science (MIT open course)
    • Discrete Mathematics (Shai Simonson)
    • Discrete Mathematics (NTHUOCW)

• Elementary Statistics and Probability

  • Textbooks
    • OpenIntro Statistics (Christopher D. Barr, David M. Diez, and Mine Çetinkaya-Rundel)
    • Statistics by Philip B. Stark
    • Probability and Statistics (Open + Free - April 2017 release)
    • Seeing Theory (Daniel Kunin)
    • Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence
  • Courses
    • Statistics 110: Probability (Harvard)
    • Statistics and probability (Khan Academy)

• Linear Model

  • Textbooks
    • An Introduction to Statistical Learning with Applications in R (James, Witten, Hastie and Tibshirani)
    • Core Statistics (Simon N. Wood)
  • Courses
    • Statistical Learning (Stanford Online)
    • Regression Analysis (Dr. Soumen Maity, IIT Kharagpur)

• Optimization

  • Textbooks
    • Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares (Boyd and Vandenberghe)
    • Convex Optimization (Boyd and Vandenberghe)
  • Courses
    • Convex Optimization (Stanford Online)
    • Machine Learning 10-725: Convex Optimization (Carnegie Mellon University)

• Python for Data Science

  • Textbooks
    • Introduction to Computation and Programming Using Python: With Application to Understanding Data (Guttag, John)
    • Course notes for Computational Statistics and Statistical Computing (DUKE STA 663 (2019))
  • Courses
    • Introduction to Computer Science and Programming in Python (MIT open course)
    • Google's Python Class (Google's Python Class)
    • Composing Programs (John DeNero)
    • Practical Data Science (CMU 15-388/688 - Spring 2018)
    • Google's Colaboratory (Google's Colaboratory)

• Algorithms

  • Textbooks
    • Algorithms (S. Dasgupta, C. H. Papadimitriou, and U. V. Vazirani)
  • Courses
    • Introduction to Algorithms (MIT open course)

• Database Systems

  • Textbooks
    • Database Systems:The Complete Book (Ullman et al.)
  • Courses
    • Introduction to Databases (Stanford Online)

• Theory of Computation

  • Textbooks
    • Introduction to the Theory of Computation (Sipser, Michael.)
    • Introduction to Theoretical Computer Science (Boaz Barak)
  • Courses
    • Theory of Computation (Neso Academy)
    • Theory of Computation (Shai Simonson)
    • Theory of Computation (Theory of Computation-nptel)

• Machine Learning

  • Textbooks
    • Machine Learning (Mitchell)
    • Neural Networks and Deep Learning (Nielsen)
    • Deep Learning with Python (François Chollet)
    • Python Machine Learning (2nd edition) (Raschka, Sebastian and Mirjalili, Vahid)
    • Learn and use machine learning (Google)
  • Courses
    • Machine Learning (Coursera)
    • Machine Learning Crash Course (Google)
    • Machine Learning for Engineering (NPTEL-NOC IITM)
    • Machine Learning for Intelligent Systems (CORNELL CS4780)

• Data Mining

  • Textbooks
    • Mining of Massive Datasets (Leskovec et al.)
    • Machine Learning (Andrew Ng)
    • Introduction to Data Mining (Tan, Steinbach and Kumar)
  • Courses
    • Statistical Aspects of Data Mining (Google Tech Talks)
    • Mining Massive Datasets (Stanford Open)

• Artificial Intelligence

  • Textbooks
    • Artificial Intelligence: Foundations of Computational Agents (David L. Poole and Alan K. Mackworth)
  • Courses
    • Artificial Intelligence (MIT open course)