Data Science in 2020: Computing, Curricula, and Challenges for the Next 10 Years

Schwab-McCoy, Aimee; Baker, Catherine M.; Gasper, Rebecca E.

doi:10.1080/10691898.2020.1851159

Cited by 35 publications

(13 citation statements)

References 29 publications

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“…These include deliberate choices about aspects of the course beyond the programming language that is (often) used: the course website, nature of assignments and projects, and means for teachers and students of giving and receiving feedback, among others. As others have pointed out, this work often takes the form of case studies (for examples of case studies, see Schwab-McCoy et al, 2021). Çetinkaya-Rundel and Ellison's (2021) paper is emblematic of this approach.…”

Section: Prior Research On Data Science Learning That Emphasizes Disc...mentioning

confidence: 99%

A secret agent? K-12 data science learning through the lens of agency

Rosenberg¹,

Jones²

2022

Preprint

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Section: Prior Research On Data Science Learning That Emphasizes Disc...mentioning

confidence: 99%

A secret agent? K-12 data science learning through the lens of agency

Rosenberg¹,

Jones²

2022

Preprint

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“…Woodard and Lee (2021) report on a study where students spoke through their thought process as they performed computational tasks; their results are, somewhat unsurprisingly, that computing is difficult and not intuitive. Schwab-McCoy et al (2021) describe the challenge in front of us to teach computational thinking effectively. Donoghue et al (2021), who describe debugging, and Theobold et al (2021), who discuss the teaching of iteration (a fundamental component of algorithmic thinking), speak to integrating small pieces of computational thinking within the data science curriculum.…”

Section: Novel or Technical Data Science Skills And Habitsmentioning

confidence: 99%

Integrating Computing in the Statistics and Data Science Curriculum: Creative Structures, Novel Skills and Habits, and Ways to Teach Computational Thinking

Horton

Hardin

2021

Journal of Statistics and Data Science Education

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Nolan and Temple Lang (2010) argued for the fundamental role of computing in the statistics curriculum. In the intervening decade the statistics education community has acknowledged that computational skills are as important to statistics and data science practice as mathematics. There remains a notable gap, however, between our intentions and our actions. In this special issue of the Journal of Statistics and Data Science Education we have assembled a collection of papers that (1) suggest creative structures to integrate computing, (2) describe novel data science skills and habits, and (3) propose ways to teach computational thinking. We believe that it is critical for the community to redouble our efforts to embrace sophisticated computing in the statistics and data science curriculum. We hope that these papers provide useful guidance for the community to move these efforts forward.

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“…In 1977, John Tookey (Tukey, 1977) highlighted the critical importance of statistics in the field of data analysis where statistics and data science are interconnected, so that they cannot be separated from each other. So much so, that wherever the word “statistics” is used, “data science” is mentioned and vice versa (Schwab-McCoy et al , 2020). Consequently, recent research in data science has focused primarily on how to use statistical principles as the science that collects, analyzes and understands data (Muthusami and Saritha, 2020).…”

Section: Related Workmentioning

confidence: 99%

Dynamic Distributed and Parallel Machine Learning algorithms for big data mining processing

Djafri

2021

DTA

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PurposeThis work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.Design/methodology/approachIn the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.FindingsThe authors got very satisfactory classification results.Originality/valueDDPML system is specially designed to smoothly handle big data mining classification.

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Data Science in 2020: Computing, Curricula, and Challenges for the Next 10 Years

Cited by 35 publications

References 29 publications

A secret agent? K-12 data science learning through the lens of agency

A secret agent? K-12 data science learning through the lens of agency

Integrating Computing in the Statistics and Data Science Curriculum: Creative Structures, Novel Skills and Habits, and Ways to Teach Computational Thinking

Dynamic Distributed and Parallel Machine Learning algorithms for big data mining processing

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