Data Science Curriculum Design: A Case Study

Hassan, Ismail Bile; Ghanem, Thanaa M.; Jacobson, David; Jin, Simon; Johnson, Kevin R.; Sulieman, Dalia; Wei, Wei

doi:10.1145/3408877.3432443

Cited by 10 publications

(4 citation statements)

References 5 publications

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“…Business intelligence integrates business analytics and data visualization with data tools and infrastructure to convert business data into actionable information that allows organizations to make data-driven decisions and improve business productivity (Niu et al , 2021). Business intelligence-related courses have been included in a wide range of data science programs (Bile Hassan et al , 2021). Figure 4 focuses on principles 11–15 and their derived keywords.…”

Section: Methodsmentioning

confidence: 99%

Exploring Information Systems (IS) curricula: a semantic analysis approach

Shi,

Hwang,

Chong

et al. 2023

IDD

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Purpose Today’s in-demand skills may not be needed tomorrow. As companies are adopting a new group of technologies, they are in huge need of information technology (IT) professionals who can fill various IT positions with a mixture of technical and problem-solving skills. This study aims to adopt a sematic analysis approach to explore how the US Information Systems (IS) programs meet the challenges of emerging IT topics. Design/methodology/approach This study considers the application of a hybrid semantic analysis approach to the analysis of IS higher education programs in the USA. It proposes a semantic analysis framework and a semantic analysis algorithm to analyze and evaluate the context of the IS programs. To be more specific, the study uses digital transformation as a case study to examine the readiness of the IS programs in the USA to meet the challenges of digital transformation. First, this study developed a knowledge pool of 15 principles and 98 keywords from an extensive literature review on digital transformation. Second, this study collects 4,093 IS courses from 315 IS programs in the USA and 493,216 scientific publication records from the Web of Science Core Collection. Findings Using the knowledge pool and two collected data sets, the semantic analysis algorithm was implemented to compute a semantic similarity score (DxScore) between an IS course’s context and digital transformation. To present the credibility of the research results of this paper, the state ranking using the similarity scores and the state employment ranking were compared. The research results can be used by IS educators in the future in the process of updating the IS curricula. Regarding IT professionals in the industry, the results can provide insights into the training of their current/future employees. Originality/value This study explores the status of the IS programs in the USA by proposing a semantic analysis framework, using digital transformation as a case study to illustrate the application of the proposed semantic analysis framework, and developing a knowledge pool, a corpus and a course information collection.

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Section: Methodsmentioning

confidence: 99%

Exploring Information Systems (IS) curricula: a semantic analysis approach

Shi,

Hwang,

Chong

et al. 2023

IDD

View full text Add to dashboard Cite

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“…In the United States, most of today's DS program courses cover three foundation areas; Mathematics, Statistics, and Computer Science [4]. In addition to the foundation courses, several Data Science curriculum guidelines emphasize Data Science specific courses such as data curation, data visualization, data modeling, and data mining [4][5][6]. In addition, it is strongly recommended to include courses outside of Mathematics, Statistics, and Computer Science to accommodate a wide range of Data Science applications [4][5].…”

Section: Related Workmentioning

confidence: 99%

An Analysis of Curricula for Data Science Undergraduate Programs

Cho¹

2022

J Multimed Inf Syst

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Today, it is imperative to educate students on how to best prepare themselves for the new data driven era of the future.Undergraduate education plays an important role in providing students with more Data Science opportunities and expanding the supply of Data Science talent. This paper surveys and analyzes the curricula of Data Science-related bachelor's degree programs in the United States. The 'required' and 'elective' courses in a curriculum for obtaining a B.S. degree were evaluated by course weight to indicate its necessity. As a result, it was possible to find out which courses were important in Data Science programs and which areas were emphasized for B.S. degrees in Data Science.We found that courses belong to the Data Science area, such as data management, data visualization, and data modeling, were more required for Data Science B.S. degrees in the United States.

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“…The community has been considering how to use data science in teaching computing, or how to teach computing for students whose focus is data science, for several years (see Sullivan, 2013, andRamamurthy, 2016). In the recent conferences, data science education has been a very active topic (see Adams, 2020;Allen, 2021;Blair et al, 2021;Bressoud & Thomas, 2019;Deb et al, 2019;Fekete et al, 2021;Hassan et al, 2021;Karbasian & Johri, 2020;Rampure et al, 2021;Rosenthal & Chung, 2020;Salloum et al, 2021). Another outcome of this activity has been a curriculum document setting out appropriate computing competencies from the ACM Data Science Task Force, co-chaired by Andrea Danyluk and Paul Leidig.…”

Section: The Computing Education Communitymentioning

confidence: 99%

Variations in Data Science Curriculum: A View From Computing Education

Fekete

2021

Harvard Data Science Review

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Contributions of the Berkeley Data Science CurriculumThe wonderful article "Interleaving Computational and Inferential Thinking in an Undergraduate Data Science Curriculum" by Adhikari et al. (this issue) offers a wealth of insights into a seminal university curriculum for teaching data science. The existence of this curriculum at University of California Berkeley, as well as its details, have been inspiring to educators in many institutions as they grapple with the challenges of designing an undergraduate education for the emerging field of data science. In this discussion I aim to show some aspects of this challenge, as it is seen by computer science educators. In particular, I will convey a variety of different curriculum approaches that have value in different settings.To start, though, we need to pay attention to many outstanding aspects of the Berkeley curriculum. Among the most influential features of this innovation is the idea of having a campus-wide focus on data science for undergraduates from diverse intellectual backgrounds. Many of the earliest proposals to teach data science were in the context of master's degrees, which have a deep vocational focus and often are aimed at

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Data Science Curriculum Design: A Case Study

Cited by 10 publications

References 5 publications

Exploring Information Systems (IS) curricula: a semantic analysis approach

Exploring Information Systems (IS) curricula: a semantic analysis approach

An Analysis of Curricula for Data Science Undergraduate Programs

Variations in Data Science Curriculum: A View From Computing Education

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