The Information Quality Framework for Evaluating Data Science Programs
15 Pages Posted: 6 Feb 2017 Last revised: 18 Jul 2017
Date Written: February 4, 2017
Designing a new Analytics program requires not only identifying needed courses, but also tying the courses together into a cohesive curriculum with an overriding theme. Such a theme helps determine the proper sequencing of courses and create a coherent linkage between different courses often taught by faculty staff from different domains. It is common to see a program with some courses taught by computer science faculty, other courses taught by faculty and staff from the statistics department, and others from operations research, economics, information systems, marketing or other disciplines. Applying an overriding theme not only helps students organize their learning and course planning, but it also helps the teaching faculty in designing their materials and choosing terminology. The InfoQ framework introduced by Kenett and Shmueli provides a theme that focuses the attention of faculty and students on the important question of the value of data and its analysis with flexibility that accommodates a wide range of data analysis topics. In this chapter we review a number of programs focused on analytics and data science content from an InfoQ perspective. Our goal is to show, with examples, how the InfoQ dimensions are addressed in existing programs and help identify best practices for designing and improving such programs. We base our assessment on information derived from the program’s web site.
Keywords: Decision Science, Information Quality, Educational Framework
JEL Classification: A23, C4, C55, C8, C9, M1, Y1
Suggested Citation: Suggested Citation