- Statistical & Scientific Computing Infrastructure e.g. programming languages & environments (e.g. R, Julia, Go, Rust, Python)
- Parallel & Distributed Computing - technologies and algorithms
- Data Technologies (e.g. Web API's, text search engines)
- Data Visualization - static, interactive, dynamic, Web-based
- Provenance & Reproducibility - computations and data analysis process
- Data Science pipeline and applications
- XML and Web Technologies for Data Science with R December, 2013 with Deborah Nolan
- Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving March 2015 with Deborah Nolan and several chapter contributors.
- UC Berkeley, Ph.D.