This page contains links to our support documents for some of the most popular quantitative, qualitative, and data conversion software at Stanford. We have included guides for getting started with software packages in different operating systems, resources for learning more about the software, tips for data entry, and other useful documents.
- Self-Directed Stata Learning
- Using Stata with FarmShare
- Self-Directed Python Learning (coming soon)
- Self-Directed R Learning (coming soon)
- Data Visualization: Wilke CO. 2019. Fundamentals of Data Visualization.
- Python: VanderPlas J. 2017. Python Data Science Handbook.
- Python: Natural Language Processing with Python.
- Python: spaCy 101: Natural Language Processing in Python.
- Python: scikit-learn Machine Learning in Python.
- R: Kearns GJ. 2010. Introduction to Probability and Statistics Using R.
- R: Wickham H, Grolemund G. 2017. R for Data Science.
- R: Silge J, Robinson D. 2021. Text Mining with R.
- R: James G, Witten D, Hastie T, Tibshirani R. 2021. Introduction to Statistical Learning: With Applications in R, 2nd ed.
- Writing: Mensh B, Kording K. 2017. Ten simple rules for structuring papers.
Our documents are also available in print format in The Velma Denning Room.
Click on a document title to view that document in your browser. Please note that our documents require Adobe Acrobat Reader.
For a list of where you can access the following software, please visit Where to Access Software at Stanford.
View quick instructional videos for selected software.
Other Helpful Guides
Using Excel for Data Manipulation and Statistical Analysis
Comparison of Statistical Software Packages
Using Windows Applications on a Mac
Tips for Data Entry
Writing about Numeric Data
Research Planning Before Data Collection
Research Planning After Data Collection
Tips For Survey Design