Data Science Curriculum

1. Introduction to Data Science
    Data Science A-Z: Real-Life Data Science Exercise – Udemy
    An Introduction to Statistical Learning – eBook
   

2. Statistics
    Statistical Thinking for Data Science and Analytics – edX
   ☐Fundamentals of Statistics – MITx 18.650x
   

3. Probability
   ☐ Introduction to Probability – The Science of Uncertainty – MITx: 6.041x
   

4. Calculus
    Multivariable Calculus – Khan academy
    Mathematics for Machine Learning: Multivariate Calculus – Coursera
   

5. Linear Algebra
    Linear Algebra – Khan academy
    Essence of Linear Algebra – 3Blue1Brown
    Mathematics for Machine Learning: Linear Algebra – Coursera
   ☐ Linear Algebra – MIT 18.06
   

6. Python
    Complete Python Bootcamp: Go from Zero to Hero in Python – Udemy
    Introduction to Computer Science and Programming Using Python – MITx: 6.00.1x
   ☐ Introduction to Computation Thinking and Data Science – MITx: 6.00.2x
   

7. Python Data Science Library
    Python for Data Science and Machine Learning Bootcamp (Section 1 – 13) – Udemy
   ☐ Python for Data Analysis – eBook
   

8. R programming
   ☐ Data Analysis for Social Scientists – MITx 14.310x/14.310Fx
   ☐The Art of R programming – eBook
   ☐ Data Science Harvard Professional Certificate Program – edx
   

9. Tableau
   ☐ Tableau 10 A-Z – Udemy
   

10. Machine Learning
    ☐ Machine Learning A-Z: Hands-On Python & R in Data Science – Udemy
    ☐ Machine Learning with Python: From Linear Models to Deep Learning – MITx 6.86x
    ☐ Machine Learning by Stanford University – Coursera
    ☐ Introduction to Machine Learning for Coders – fast.ai
    ☐ Hands-On Machine Learning with Scikit-Learn & TensorFlow – eBook
    

11. Deep Learning
    ☐ Deeplearning.ai – Coursera
    ☐ Practical Deep Learning for Coders – fast.ai
    ☐ Deep Learning by Ian Goodfellow et al. – eBook
    

12. Artificial Intelligence
    ☐ Introduction to Computer Vision by Georgia Institute of Technology – Udacity
    ☐ Convolutional Neural Networks for Visual Recognition – Stanford CS231n
    ☐ Natural Language Processing with Deep Learning – Stanford CS224n
    

13. Additional resources
    ☐The Elements of Statistical Learning – eBook
    ☐ Applied Predictive Modelling – eBook