The ASA’s most recent curriculum guidelines emphasize the increasing importance of data science, real applications, model diversity, and communication / teamwork in undergraduate education. In an effort to highlight recent efforts inspired by these guidelines, I organized a JSM session titled Doing more with data in and outside the undergraduate classroom. This session featured talks on recent curricular and extra-curricular efforts in this vein, with a particular emphasis on challenging students with real and complex data and data analysis. The speakers discussed how these pedagogical innovations aim to educate and engage the next generation, and help them acquire the statistical and data science skills necessary to succeed in a future of ever-increasing data. I’m posting the slides from this session for those who missed it as well as for those who want to review the resources linked in the slides.

# Computational Thinking and Statistical Thinking: Foundations of Data Science

*by Ani Adhikari and Michael I. Jordan, University of California at Berkeley*

[slideshare id=64747742&doc=adhikarijordanfds-160806003823]

# Learning Communities: An Emerging Platform for Research in Statistics

*by Mark Daniel Ward, Purdue University*

[slideshare id=64747732&doc=wardllc-160806003643]

# The ASA DataFest: Learning by Doing

_by Robert Gould, University of California at Los Angeles_

(See http://www.amstat.org/education/datafest/ if you’re interested in organizing an ASA DataFest at your institution.)

[slideshare id=64747720&doc=goulddatafest-160806003520]

# Statistical Computing as an Introduction to Data Science

*by Colin Rundel, Duke University [GitHub]*

[slideshare id=64747584&doc=rundelstatcomp-160806002304]