A curated collection of programming platforms, software tools, learning resources, and textbooks recommended for data analytics and programming courses.
Programming Platforms
Google Colab
Code Python in the cloud with no setup required. Google Colab provides free access to GPUs and a collaborative notebook environment ideal for machine learning and data analysis.
Khan Academy SQL
Free, interactive SQL curriculum from Khan Academy. An excellent starting point for students learning database querying and data manipulation fundamentals.
Software Tools
Orange
The best GUI-based free program for machine learning. Orange is Python-based and provides visual programming for data visualization, machine learning, and data mining through an intuitive drag-and-drop interface.
JASP
The best GUI-based free program for statistics. JASP is R-based and offers a user-friendly interface for both classical and Bayesian statistical analysis without requiring any programming knowledge.
Swirl
An interactive environment for learning R programming directly in the R console. Swirl teaches R programming and data science concepts through guided, hands-on exercises.
Learning Resources
R-bloggers
A comprehensive blog aggregator for R tutorials, tips, and news. An excellent community resource for staying current with R programming techniques and best practices.
Introductory Big Data E-Book
A free introductory e-book on Big Data concepts and techniques by Dr. Ryoo's group. Provides a solid foundation for students new to large-scale data analysis.
R vs. Python Comparison
A resource comparing R and Python for data science applications. Helps students understand the strengths of each language and choose the right tool for different analytical tasks.
Textbooks in Python
Think Stats
An introduction to probability and statistics for Python programmers. This free textbook by Allen B. Downey emphasizes simple techniques for exploring real datasets.
Textbooks in R
Modern Dive
Statistical Inference via Data Science. A free, modern textbook for learning R and introductory statistics with a focus on data science workflows and reproducible analysis.
R for Data Science
By Grolemund & Wickham (2016). The essential guide to data science with R, covering data import, tidying, transformation, visualization, and modeling using the tidyverse ecosystem.
Introductory Statistics with Randomization and Simulation
A modern approach to introductory statistics that uses randomization and simulation methods to build intuitive understanding of statistical inference before introducing traditional methods.