References

Baesens, Bart, Veronique Van Vlasselaer, and Wouter Verbeke. 2015. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection. John Wiley & Sons.

Bezanson, Jeff, Alan Edelman, Stefan Karpinski, and Viral B Shah. 2017. “Julia: A Fresh Approach to Numerical Computing.” SIAM Review 59 (1): 65–98.

Brooks, Harvey, and Chester L Cooper. 2013. Science for Public Policy. Elsevier.

Brzustowicz, Michael R. 2017. Data Science with Java: Practical Methods for Scientists and Engineers. O’Reilly Media, Inc.

Eder, Maciej, Jan Rybicki, and Mike Kestemont. 2016. “Stylometry with R: A Package for Computational Text Analysis.” R Journal 8 (1): 107–21. https://journal.r-project.org/archive/2016/RJ-2016-007/index.html.

Grus, Joel. 2019. Data Science from Scratch: First Principles with Python. O’Reilly Media, Inc.

Hansjörg, Neth. 2019. Data Science for Psychologists. self published.

Janssens, Jeroen. 2014. Data Science at the Command Line: Facing the Future with Time-Tested Tools. O’Reilly Media, Inc.

Provost, Foster, and Tom Fawcett. 2013. Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. O’Reilly Media, Inc.

R Core Team. 2019. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Silge, Julia, and David Robinson. 2017. Text Mining with R: A Tidy Approach. "O’Reilly Media, Inc.".

Thomas, Niclas, and Laura Pallett. 2019. Data Science for Immunologists. CreateSpace Independent Publishing Platform.

VanderPlas, Jake. 2016. Python Data Science Handbook: Essential Tools for Working with Data. O’Reilly Media, Inc.

Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.