R

• RStudio (https://www.rstudio.com/products/rstudio/download/), which includes R (https://cloud.r-project.org/)
• tidyverse (e.g. by executing install.packages("tidyverse") from RStudio’s console)

Python

• Anaconda (https://www.continuum.io/downloads) with Python version 3.x
• If you have Windows is it recommended that you install Linux or the VirtualBox machine

Strongly recommended

Bertsimas, O'Hair, Pulleyblank. The Analytics Edge. From the editor or on Amazon. (alternatively, the EdX course)

The Analytics Edge EdX course: https://www.edx.org/course/the-analytics-edge-2 (free) NOTE: the EdX course starts on October 15, but the one on MIT OCW seems to be open: https://ocw.mit.edu/courses/sloan-school-of-management/15-071-the-analytics-edge-spring-2017/index.htm

The ML specialization course: https://www.coursera.org/specializations/machine-learning (free)

Grolemund, Wickham. R for data science. http://r4ds.had.co.nz (free)

The Python Tutorial: https://docs.python.org/3/tutorial/ (free)

Recommended

Kleinberg, Tardos. Algorithm Design. Addison Wesley.

Boyd, Vandenberghe. Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares. Cambridge University Press.

Leskovec, Rajamaran, Ullman. Mining of Massive Datasets. Cambridge University Press. Publicly available at http://infolab.stanford.edu/~ullman/mmdsn.html.