Data Science References

These are, among others, some references that I find helpful to learn data science.

Books:

1. Hastie, T., Tibshirani, R., & Friedman, J. H. (2001). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.
   https://web.stanford.edu/~hastie/Papers/ESLII.pdf

2. Strang, G. (2016). Introduction to linear algebra. MA: Wellesley-Cambridge Press.
   https://math.mit.edu/~gs/linearalgebra/

3. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning. Springer.
   https://web.stanford.edu/~hastie/ISLR2/ISLRv2_website.pdf | https://www.statlearning.com/

4. Rajaraman, A., Ullman, J. D. (2014). Mining of massive datasets. Cambridge: Cambridge University Press. http://infolab.stanford.edu/~ullman/mmds/book0n.pdf | http://www.mmds.org/

5. Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals. New York, NY: John Wiley & Sons.
   

6. Provost, F., & Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. Sebastopol: O'Reilly.
   

7. Pólya, G. (1971). How to solve it: A new aspect of mathematical method. Princeton: Princeton University Press.
   

8. Bruce, P., & Bruce, A. (2017). Practical statistics for data scientists. Sebastopol: O'Reilly.
   

9. Lazzeri, F. (2020). Machine learning for time time series forcasting with python. Indianapolis: John Wiley & Sons.
   

10. Gee, S. (2014). Fraud and fraud detection: A data analytics approach. New Jersey: John Wiley & Sons.
    

11. Baning, R. (2018). Hands-on recommendation systems with python. Birmingham: Packt.
    

12. Boyd, S., & Vandenberghe, L. (2018). Introduction to applied linear algebra: Vectors, matrices, and least squares. Cambridge: Cambridge University Press.
    https://web.stanford.edu/~boyd/vmls/vmls.pdf

13. Shalev-Shwartz, S., Ben-David, S. (2014). Understanding machine learning: From theory to algorithms. New York: Cambridge University Press.
    https://www.cs.huji.ac.il/w~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf

Video Playlists:

1. Starmer, J. (n.d.). Machine Learning Playlist [StatQuest with Josh Starmer]. YouTube. https://www.youtube.com/playlist?list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF
2. Guttag, J., et al. (n.d.). MIT 6.0002: Introduction to Computational Thinking and Data Science Playlist [MIT OpenCourseWare]. YouTube. https://www.youtube.com/playlist?list=PLUl4u3cNGP619EG1wp0kT-7rDE_Az5TNd
3. Starmer, J. (n.d.). Statistics Fundamentals Playlist [StatQuest with Josh Starmer]. YouTube. https://www.youtube.com/playlist?list=PLblh5JKOoLUK0FLuzwntyYI10UQFUhsY9
4. Ng, A. (n.d.). Stanford CS229: Machine Learning by Andrew Ng Playlist [stanfordonline]. YouTube. https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU
5. Sanderson, G. (n.d.). Essence of linear algebra Playlist [3Blue1Brown]. YouTube. https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab
6. Python Programming Tutorials (Computer Science) Playlist [Socratica]. (n.d.). YouTube. https://www.youtube.com/playlist?list=PLi01XoE8jYohWFPpC17Z-wWhPOSuh8Er-
7. Strang, G. (n.d.). MIT 18.065: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning Playlist [MIT OpenCourseWare]. Youtube. https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k

Others:

1. http://vision.stanford.edu/teaching/cs131_fall1415/schedule.html
2. http://web.stanford.edu/class/cs124/
3. http://web.stanford.edu/class/cs246/
4. https://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/index.htm
5. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/index.htm
6. https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/
7. https://stanford.edu/~shervine/teaching/cs-229/refresher-probabilities-statistics
8. https://gwthomas.github.io/docs/math4ml.pdf