Volume 10, Number 03, March 2020

Information Retrieval in Data Science Curricula

  Authors

Duaa Bukhari, Long Island University, USA

  Abstract

Data scientists are very much in demand as companies grapple with the challenge of making valuable discoveries from Big Data. Therefore, academic institutions have started to offer different kind of data science DS programs and they strive to prepare students to be data scientists who are capable to face the challenge of the new age. As an interdisciplinary field, DS programs should represent a combination of subject areas from several disciplines. Consequently, schools that host data science programs are diverse. Until now few studies have investigated data science programs within a particular discipline, such as Business (e.g. Chen et al.). However, there are very few empirical studies that explore DS programs and investigate its curriculum structure across disciplines. This study conducted an exploratory content analysis of 30 United States’ DS programs from a variety of disciplines. The present study seeks to depict the current state of DS education in the U.S. to explore what discipline DS programs covers at the graduate level. The analysis was conducted on course titles and course descriptions. The results show that DS programs required varying numbers of credit hours, including practicum and capstone. Management schools seem to take the lead and the initiative in lunching and hosting DS programs. It can be said that all DS programs requires the basic knowledge of database design, representation, extraction and management. DS programs delivered information skills through their core courses. Results indicates that almost 40 percent of required courses in DS programs is involved information representations, retrieval and programming. Required courses also addressed communication visualization and mathematics skills.

  Keywords

Data Science, Information Retrieval, Curricula, Master’s Programs, DS curriculum

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