Data Science Programs in US Higher Education

A SLIS public lecture with Rong Tang and Watinee Sae-Lim

Starts:  10/8/2015  4:00 PM
Ends: 10/8/2015 5:00 PM
Location: Collaboratory, One Palace Road, Simmons College

In this study — Data Science Programs in US Higher Education: an exploratory content analysis of program descriptions and curricula structures — an exploratory content analysis of 30 randomly selected Data Science programs from eight disciplines revealed significant gaps in the current DS education in the US. The analysis centers on linguistic patterns of program descriptions, curriculum requirement, and DS course focus as pertaining to key skills and domain knowledge. The results show that a range of unique terms were used in individual program descriptions and common terms shared across disciplines. DS programs required varying numbers of credit hours, including practicum and capstone. Most DS courses covered the basic level of analytical skills, yet lacked in upper-level skill coverage. Eight disciplines delivered information skills through their core, and four addressed communication skills. Six disciplines covered visualization skills through their core yet just three in electives. The course offering on mathematics/statistics was weak in iSchools. While core courses in iSchools provided communication and visualization skills, their electives did not attend such skills. Findings have implications for improving the DS education in iSchools and other disciplines.

You can see the full list of lectures and lecturers (with some dates and titles TBA) here:​.