This site was created to accompany the paper
Data science in statistics curricula:
Preparing students to ‘think with data’ (arXiv.org)
by J. Hardin, R. Hoerl, N.J. Horton, and D. Nolan, also with
B. Baumer, O. Hall-Holt, P. Murrell, R. Peng, P. Roback, D. Temple Lang, and M.D. Ward,
to appear in The American Statistician 2015 special issue
“Statistics and the Undergraduate Curriculum”.
Included here are the syllabi and some of the materials for the courses featured
in this paper. Additional curriculum materials, papers and related resources
follow these exemplars.
Exemplars of Teaching Data Science in Statistics
- MTH 292: Data Science, B. Baumer, Smith College.
Syllabus |
Example Assignment - Network Analysis
- STATS 220: Data Technologies, P. Murrell, University of Auckland.
Syllabus | Course Text
Introduction to Data Technologies.
- STAT 133: Concepts in Computing with Data, D. Nolan, University of California, Berkeley
STAT 141: Statistical Computing, D. Temple Lang, University of California, Davis.
Nolan Syllabus |
Temple Lang Syllabus |
Course Projects:
Data Science in R:
A case studies approach to computational reasoning and problem solving |
Data and code:
http://rdatasciencecases.org/
- Data Science Specialization R. Peng, Johns Hopkins University.
Certificate Program
| interactive learning environment:
Swirl: Learn R, in R
- CS125: Computer Science for Mathematicians and Scientists, P. Roback and O. Hall-Holt,
St. Olaf College
Syllabus |
Energy Assignment |
Course Site
- STAT 29000: Introduction to Big Data Analysis, Mark D. Ward, Purdue University.
Syllabus |
Course Web site
Additional Curriculum materials
Last Updated July 5, 2015