StackOverflow vs Kaggle: A Study of Developer Discussions About Data Science

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“…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 ). In another study, Karbasian & Johri (2020) used a topic modeling-based approach to analyze data science-specific posts from two popular Q&A communities, StackExchange and Reddit.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Researchers have long studied online Q&A platforms such as Stack Overflow ( Ahmad et al, 2018 ), Stack Exchange ( Karbasian & Johri, 2020 ), Quora ( Maity, Sahni & Mukherjee, 2015 ), Kaggle ( Hin, 2020 ), Yahoo ( Zhang et al, 2020 ), and Reddit ( Karbasian & Johri, 2020 ). Stack Overflow, in particular, is an important information and experience sharing platform for all developers from various backgrounds under the umbrella of information technologies, and it has been the subject of numerous studies ( Ahmad et al, 2018 ).…”

Section: Background and Related Workmentioning

confidence: 99%

“…Stack Overflow data has also been used in a number of studies that revealed discussion topics in specific subfields of software development such as testing ( Kochhar, 2016 ), security ( Yang et al, 2016 ), mobile development ( Linares-Vásquez, Dit & Poshyvanyk, 2013 ; Rosen & Shihab, 2016 ), chatbot development ( Abdellatif et al, 2020 ), IOT development ( Uddin et al, 2021 ), and machine learning ( Alshangiti et al, 2019 ). As previously stated, a small number of studies have also been conducted based on the analysis of posts shared on the Q&A platforms to explore the issues and challenges related to data science ( Bagherzadeh & Khatchadourian, 2019 ; Hin, 2020 ; Karbasian & Johri, 2020 ). Our study expands on previous research by analyzing the comprehensive dimensions of posts shared on the DSSE platform, which only includes data science-related posts.…”

Section: Background and Related Workmentioning

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: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 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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“…Due to its effectiveness, ensembling has been a popular research topic (Nilsson, 1965;Hansen & Salamon, 1990;Wolpert, 1992;Krogh & Vedelsby, 1995;Breiman, 1996;Dietterich, 2000;Zhou et al, 2002;Rokach, 2010;Ovadia et al, 2019) as a simple alternative to fully Bayesian methods (Blundell et al, 2015;Gal & Ghahramani, 2016). It is currently the de facto solution for many machine learning applications and Kaggle competitions (Hin, 2020).…”

Section: Introductionmentioning

confidence: 99%

DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation

Rame,

Cord

2021

Preprint

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Deep ensembles perform better than a single network thanks to the diversity among their members. Recent approaches regularize predictions to increase diversity; however, they also drastically decrease individual members' performances. In this paper, we argue that learning strategies for deep ensembles need to tackle the trade-off between ensemble diversity and individual accuracies. Motivated by arguments from information theory and leveraging recent advances in neural estimation of conditional mutual information, we introduce a novel training criterion called DICE: it increases diversity by reducing spurious correlations among features. The main idea is that features extracted from pairs of members should only share information useful for target class prediction without being conditionally redundant. Therefore, besides the classification loss with information bottleneck, we adversarially prevent features from being conditionally predictable from each other. We manage to reduce simultaneous errors while protecting class information. We obtain state-of-the-art accuracy results on CIFAR-10/100: for example, an ensemble of 5 networks trained with DICE matches an ensemble of 7 networks trained independently. We further analyze the consequences on calibration, uncertainty estimation, out-of-distribution detection and online co-distillation.

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StackOverflow vs Kaggle: A Study of Developer Discussions About Data Science

Cited by 2 publications

References 0 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

DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation

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