Data Science Lectures / Books
If you are or become an Actuary and/or you are interested in learning more about Data Science in general, then you are on the right page!
Below we list more lectures, courses and books which we think are perfect for Actuaries to learn more about Data Science. The material below is primariliy learning material on basic knowledge in Data Science. Be aware that they do not discuss actuarial topics.
Lectures in Data Science
- Computational Statistics, ETH Zurich, P. Bühlmann, M. Mächler, 2016
- Machine Learning, EPF Lausanne, M. Jaggi, U. Rüder, 2018
Courses in Data Science
- Introduction to Machine Learning, M. Mayer, 2021
Books in Data Science
Below we list several books which we think are well suited for actuaries, providing basic knowledge in various Data Science areas. We intentionally restrict to a few books, as there are a lot more available.
Mathematics / Statistics:
- Data Science and Machine Learning, D.P. Kroese, Z.I. Botev, T. Taimre, R. Vaisman, Chapman and Hall/CRC, 2019
- The Elements of Statistical Learning, T. Hastie, R Tibshirani, J. Friedman, Springer, 2009
- An Introduction to Statistical Learning, G. James, D. Witten, T. Hastie, R. Tibshirani, Springer, 2015
- Computer Age Statistical Inference, B. Efron and T. Hastie, Cambridge, 2016
Data Science with R:
- R for Data Science, G. Grolemund, H. Wickham, O’Reilly Media, 2017
- Machine Learning with R, B. Lantz, Packt, 2015
- Interpretable Machine Learning, C. Molnar, Leanpub, 2020
Data Science with Python:
- Data Science from Scratch, J. Grus, O’Reilly Media, 2015
- Python Data Science Workbook, J. VanderPlas, O'Reilly Media, 2017
Neural Networks and Deep Learning:
- Deep Learning, I. Goodfellow, Y. Bengio, A. Courville, MIT Press, 2017
- Neural Networks and Deep Learning, Nielsen, M., 2017
Understanding Statistics, Machine Learning and Data Science
Below we list various literature which provides an understanding about the fundamental concepts underlying statistics, machine learning and data science.
- Prediction, Estimation, and Attribution, B. Efron, Journal of the American Statistical Association 115:539 , 636-655, 2020
- To explain or to Predict?, G. Shmueli, Statistical Science 25/3, 289-310, 2010
- Statistical Modeling: The Two Cultures. L. Breimann, Statistical Science 16/3, 199-215, 2001