Software Licensing Support
- Software at Stanford
- Licensed software available on the Stanford shared servers
- Software offered directly through Software Licensing group
- Stanford's software licensing web store
Safari Books Online catalog
Office of the Vice Provost for Graduate Education
- Funding opportunities in interdisciplinary learning
- Program in interdisciplinary learning
- Stanford graduate summer institute
- Graduate student funding
- VPGE funding opportunities
- VPGE fellowships
Learning R Programming
- R Resources at Stanford Libraries
- R project An R journal and many books are listed under Documentation. From the book list, use your browser’s search box to search for “social science” for the most relevant books. You can also download manuals by clicking on Documentation – Manuals on the website.
- Lynda.com There are online training modules at Lynda.com. Login with your SUNet ID through the Stanford ITS page. Click on the button that says “login to lynda.com.” At lynda.com, enter the search term "r" to find currently available courses.
- Social Science Data & Software The Social Science Resource Center in the Velma Denning Room of Green Library hosts statistical software for evaluation and consultation, including R. The social science data services experts in this office are your best resource. Consult with them to get started or when you encounter problems. They can help you locate information on particular R - related topics. Several computer clusters in the library also include R. SSDS has 3 downloadable worksheets on using R, including a brochure to help you get started (with terminology and tips), and a resource list that includes helpful books available in Stanford libraries.
Consulting Services - run by students for students
- Statistics: The Department of Statistics offers a free drop-in consulting service to members of the University community.
- SMACC: Statistical, Mathematical, and Computational Consulting Consulting services from ICME, the Department of Statistics, Research Computing and IRiSS specializing in:
- Data Science, Machine Learning, Model fitting, Time series, Classification and prediction, Shared computing resources, Network Analysis, Matrix Problems, PDEs and Physical simulation, Discrete Mathematics, Optimization, Distributed Matrix Computations, Distributed Machine Learning and Optimization, High Performance Computing, Linear Algebra, Scientific Computing, FarmShare, Sherlock, GBSC, Proclus
BD2K Guide to the Fundamentals of Data Science
The NIH Big Data to Knowledge program is pleased to announce the BD2K Guide to the Fundamentals of Data Science, a series of online lectures given by experts from across the country covering a range of diverse topics in data science. This course is an introductory overview that assumes no prior knowledge or understanding of data science.
Talks will be archived on their YouTube Channel.