Insights for Curriculum Development

Karbasian, Habib; Johri, Aditya

doi:10.1145/3328778.3366817

Cited by 15 publications

(16 citation statements)

References 24 publications

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“…The majority of domain-specific research focuses on data science concepts, architectures, practices, application areas, big data, and related fields ( Donoho, 2017 ). These studies provided insights into the use of data science in academia and industry, as well as data science issues and challenges ( Vicario & Coleman, 2019 ; Karbasian & Johri, 2020 ). Previous studies have discussed the use and benefits of data science in various disciplines, ranging from business science to computer sciences, finance to medicine, bioinformatics to natural sciences ( Donoho, 2017 ; Vicario & Coleman, 2019 ).…”

Section: Background and Related Workmentioning

confidence: 99%

“…A number of studies, similar to our current study, have been conducted based on the analysis of posts from Q&A communities to investigate the issues and challenges of data science ( Bagherzadeh & Khatchadourian, 2019 ; Hin, 2020 ; Karbasian & Johri, 2020 ). An empirical study was conducted in which data science-related posts on two Q&A websites, Stack Overflow and Kaggle, were analyzed using the topic modeling approach, and 24 data science-related discussion topics were identified ( Hin, 2020 ).…”

Section: Background and Related Workmentioning

confidence: 99%

“…DSSE seeks solutions to the problems of data scientists from various backgrounds by managing their thousands of posts ( Stack Exchange, 2022 ). With the increase in data science activities in recent years, the DSSE platform has become an important source of information and a useful guide for data scientists ( Karbasian & Johri, 2020 ). All of the posts shared on DSSE can be considered as an important data repository that keeps a history of data scientists’ topics and thoughts.…”

Section: Introductionmentioning

confidence: 99%

“…Analyzing this repository can provide important insights into which tools and technologies data scientists prefer, the reasons for their choices, the types of work environments and motivations they require, and the technical challenges they face ( Karbasian & Johri, 2020 ). From this perspective, researchers have recently conducted many empirical studies using Stack Overflow data to investigate the sub-contexts of software development, such as testing ( Kochhar, 2016 ), security ( Yang et al, 2016 ), mobile development ( Rosen & Shihab, 2016 ), requirements ( Zou et al, 2015 ), concurrency development ( Ahmed & Bagherzadeh, 2018 ), chatbot development ( Abdellatif et al, 2020 ), and IOT development ( Uddin et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…From this perspective, researchers have recently conducted many empirical studies using Stack Overflow data to investigate the sub-contexts of software development, such as testing ( Kochhar, 2016 ), security ( Yang et al, 2016 ), mobile development ( Rosen & Shihab, 2016 ), requirements ( Zou et al, 2015 ), concurrency development ( Ahmed & Bagherzadeh, 2018 ), chatbot development ( Abdellatif et al, 2020 ), and IOT development ( Uddin et al, 2021 ). Despite these valuable efforts, studies exploring the holistic landscape of data science using semantic content analysis based on topic modeling from online Q&A platforms are still limited, with a few exceptions ( Bagherzadeh & Khatchadourian, 2019 ; Hin, 2020 ; Karbasian & Johri, 2020 ). In fact, we believe that analyzing data from Q&A platforms will have significant implications for understanding the current state of data science.…”

Section: Introductionmentioning

confidence: 99%

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What issues are data scientists talking about? Identification of current data science issues using semantic content analysis of Q&A communities

Gürcan¹

2023

PeerJ Computer Science

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Background Because of the growing involvement of communities from various disciplines, data science is constantly evolving and gaining popularity. The growing interest in data science-based services and applications presents numerous challenges for their development. Therefore, data scientists frequently turn to various forums, particularly domain-specific Q&A websites, to solve difficulties. These websites evolve into data science knowledge repositories over time. Analysis of such repositories can provide valuable insights into the applications, topics, trends, and challenges of data science. Methods In this article, we investigated what data scientists are asking by analyzing all posts to date on DSSE, a data science-focused Q&A website. To discover main topics embedded in data science discussions, we used latent Dirichlet allocation (LDA), a probabilistic approach for topic modeling. Results As a result of this analysis, 18 main topics were identified that demonstrate the current interests and issues in data science. We then examined the topics’ popularity and difficulty. In addition, we identified the most commonly used tasks, techniques, and tools in data science. As a result, “Model Training”, “Machine Learning”, and “Neural Networks” emerged as the most prominent topics. Also, “Data Manipulation”, “Coding Errors”, and “Tools” were identified as the most viewed (most popular) topics. On the other hand, the most difficult topics were identified as “Time Series”, “Computer Vision”, and “Recommendation Systems”. Our findings have significant implications for many data science stakeholders who are striving to advance data-driven architectures, concepts, tools, and techniques.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

What issues are data scientists talking about? Identification of current data science issues using semantic content analysis of Q&A communities

Gürcan¹

2023

PeerJ Computer Science

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Artificial intelligence and engineering education

Johri

2020

J of Engineering Edu

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Data Science in Undergraduate Life Science Education: A Need for Instructor Skills Training

et al. 2021

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There is a clear demand for quantitative literacy in the life sciences, necessitating competent instructors in higher education. However, not all instructors are versed in data science skills or research-based teaching practices. We surveyed biological and environmental science instructors (n = 106) about the teaching of data science in higher education, identifying instructor needs and illuminating barriers to instruction. Our results indicate that instructors use, teach, and view data management, analysis, and visualization as important data science skills. Coding, modeling, and reproducibility were less valued by the instructors, although this differed according to institution type and career stage. The greatest barriers were instructor and student background and space in the curriculum. The instructors were most interested in training on how to teach coding and data analysis. Our study provides an important window into how data science is taught in higher education biology programs and how we can best move forward to empower instructors across disciplines.

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Insights for Curriculum Development

Cited by 15 publications

References 24 publications

What issues are data scientists talking about? Identification of current data science issues using semantic content analysis of Q&A communities

What issues are data scientists talking about? Identification of current data science issues using semantic content analysis of Q&A communities

Artificial intelligence and engineering education

Data Science in Undergraduate Life Science Education: A Need for Instructor Skills Training

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