A First Course in Data Science. Issue 2 (4th May 2019)

• Record Type: Journal Article
• Authors: Yan, Donghui; Davis, Gary E.
• Is Part Of: Journal of statistics education. Volume 27:Issue 2(2019)
• Page Start: 99
• Page End: 109
• Publication Date: 2019-05-04
• Subjects: Curriculum design -- Data science life cycle -- Data visualization -- Exploratory data analysis; Statistics -- Study and teaching -- Periodicals; Statistics -- Study and teaching; Periodicals; 519.5071
• DOI: 10.1080/10691898.2019.1623136
• Languages: English
• ISSNs: 1069-1898

Abstract

Abstract: Data science is a discipline that provides principles, methodology, and guidelines for the analysis of data for tools, values, or insights. Driven by a huge workforce demand, many academic institutions have started to offer degrees in data science, with many at the graduate, and a few at the undergraduate level. Curricula may differ at different institutions, because of varying levels of faculty expertise, and different disciplines (such as mathematics, computer science, and business) in developing the curriculum. The University of Massachusetts Dartmouth started offering degree programs in data science from Fall 2015, at both the undergraduate and the graduate level. Quite a few articles have been published that deal with graduate data science courses, much less so dealing with undergraduate ones. Our discussion will focus on undergraduate course structure and function, and specifically, a first course in data science. Our design of this course centers around a concept called the data science life cycle . That is, we view tasks or steps in the practice of data science as forming a process, consisting of states that indicate how it comes into life, how different tasks in data science depend on or interact with others until the birth of a data product or a conclusion. Naturally, different pieces of the data science life cycle then form individual parts of the course. Details of each piece are filled up by concepts, techniques, or skills that are popular in industry. Consequently, the design of our course is both "principled" and practical. A significant feature of our course philosophy is that, in line with activity theory, the course is based on the use of tools to transform real data to answer strongly motivated questions related to the data.