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Teaching Precursors to Data Science in Introductory and Second Courses in Statistics

Date/Time: April 28, 2015 3:15 — 4:30 pm
Informal reception to follow at East Street Café at Union Station
Speaker: Nicholas J. Horton, Professor of Statistics, Amherst College
Chair: Steve H. Cohen, Senior Fellow, NORC
Sponsor: WSS Statistics Education Committee

Location: Offices of Mathematica-MPR 1101 1st Street NE, 12th Floor, Washington DC 20002 Once in the building, take the elevators to the 12th floor and inform the secretary that you are attending the WSS seminar. Please call Mathematica's main office number (202 484-9220) if you have trouble finding the building.

By Metro: Take the Red Line to either the NoMa-Gallaudet U (used to be called New York Ave) Station or Union Station. From the NoMa-Gallaudet U Station, follow signs to exit at M Street. Then walk 1 block west on M street and 2 blocks south on 1st Street NE (the building will be on your right). From Union Station, walk north along 1st Street NE for about 4-5 blocks until you reach L Street (the building will be on your left after crossing L street).

By Car: Pay parking is available in the building parking garage, which is located 1 block east of North Capitol on L Street NE.

RSVP: To be placed on the seminar attendance list, please email Carol Joyce Blumberg

Abstract: Statistics students need to develop the capacity to make sense of the staggering amount of information collected in our increasingly data-centered world. Data science is an important part of modern statistics, but our introductory and second statistics courses often neglect this fact. This talk discusses ways to provide a practical foundation for students to learn to “compute with data” as defined by Nolan and Temple Lang (2010), as well as develop “data habits of mind” (Finzer, 2013). We describe how introductory and second courses can integrate two key precursors to data science: the use of reproducible analysis tools and access to large databases. By introducing students to commonplace tools for data management, visualization, and reproducible analysis in data science and applying these to real-world scenarios, we prepare them to think statistically in the era of big data.

POC email: Carol Joyce Blumberg, cblumberg@gmail.com
WebEx: https://mprwebex.mathematica-mpr.com/orion/joinmeeting.do?MK=995818462
Meeting Number: 995-818-462. No password is required.
Audio: For remote access via audio only or if the WebEx connection does not work, call either (609) 945-6996 or (202) 554-7500. Then enter the access code of 995-818-462.


April 28, 2015
Teaching Precursors to Data Science in Introductory and Second Courses in Statistics