Accelerating the Advancement of Data Science Education

Dusen, Eric Van; Suen, Anthony; Liang, Alan; Bhatnagar, Amal

doi:10.25080/majora-7ddc1dd1-000

Cited by 4 publications

(6 citation statements)

References 2 publications

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“…[Jor16] discusses this new industry demand for "inferential thinking". Together, computational thinking and inferential thinking have been reimagined by some as the foundation for a new form of cross-disciplinary data science curriculum [AD17] [EVDSLB19]. A key technological feature of these new curricula are digital notebooks that enable users to compose computational narratives that make computing more cognitively digestible to humans [PG15].…”

Section: Computing In Education and Sciencementioning

confidence: 99%

Proceedings of the 19th Python in Science Conference

Varoquaux¹,

Walt²,

Millman

2020

Proceedings of the Python in Science Conference

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SciPy 2020, the 19th annual Python in Science Conference, was held July 6-12, virtually via the conference platform Crowdcast. Due to the COVID-19 pandemic, the SciPy conference was held online. The SciPy Conference brings together a community of researchers, engineers, and programmers dedicated to the advancement of scientific computing through open source Python software.The two main conference themes for 2020 were high performance computing; and, machine learning and data science. Discipline-specific symposia included astronomy and astrophysics; biology and bioinformatics; materials science; earth, ocean, geology, and atmospheric science; and a new symposium dedicated to fostering conversations among maintainers of the open source infrastructure that help power the worlds of scientific discovery and engineering. As was the case in 2019, there were plenary sessions for updates about key scientific software libraries, and three sessions of the ever-popular lightning talks, which this year included SciPy's youngest speaker, Artash Nath, discussing machine learning approaches in exoplanet research.The first conference keynote lecture was delivered by Anne Carpenter, who discussed the history of CellProfiler in the context of developing academic software, current application of the scientific software stack to problems in biology, and future directions for tasks like drug discovery, powered by machine learning. Andrew Chael delivered the second keynote, about the large, interorganizational effort to take the first photograph of black hole M87, and the role of scientific software in that project. This year's diversity plenary was given by Bonny McClain, who delivered an interactive lecture about bias in data, and how to think about measuring what people haven't thought about measuring before.The online format permitted a larger-than-usual number of participants, ultimately attracting 1412 participants from a recordbreaking 57 countries, making this the largest SciPy Conference yet. Participants reported that they enjoyed the broader access to beginner tutorials for popular libraries like PyTorch and xarray -something that would not be possible without having the conference at least partially online. Birds of a Feather (BoF) sessions were organized around the topics of packaging, diversity, Python in education, hardware, and SciPy 2021 with great attendance due to the online format. Sprints that usually gather around tables in conference rooms took the conversation to virtual tables using a ¶ Oracle § Mozilla

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Section: Computing In Education and Sciencementioning

confidence: 99%

Proceedings of the 19th Python in Science Conference

Varoquaux¹,

Walt²,

Millman

2020

Proceedings of the Python in Science Conference

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“…• Data-Science-in-a-Box by Dr. Cetinkaya-Rundel [11] • Foundations in Data Science (Data 8), originating at U.C. Berkeley [33] • DSC101 at Univ. Massachusetts, Dartmouth [37] • Foundations of Data Science & Foundations of Big Data by the European Data Science Academy (EDSA) [18] 3.3.…”

Section: Introductory Course Topicsmentioning

confidence: 99%

Evaluation of EDISON's data science competency framework through a comparative literature analysis

Schmitt¹,

Clark²,

Kinnaird³

et al. 2023

FoDS

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<p style='text-indent:20px;'>During the emergence of Data Science as a distinct discipline, discussions of what exactly constitutes Data Science have been a source of contention, with no clear resolution. These disagreements have been exacerbated by the lack of a clear single disciplinary 'parent.' Many early efforts at defining curricula and courses exist, with the EDISON Project's Data Science Framework (EDISON-DSF) from the European Union being the most complete. The EDISON-DSF includes both a Data Science Body of Knowledge (DS-BoK) and Competency Framework (CF-DS). This paper takes a critical look at how EDISON's CF-DS compares to recent work and other published curricular or course materials. We identify areas of strong agreement and disagreement with the framework. Results from the literature analysis provide strong insights into what topics the broader community see as belonging in (or not in) Data Science, both at curricular and course levels. This analysis can provide important guidance for groups working to formalize the discipline and any college or university looking to build their own undergraduate Data Science degree or programs.</p>

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“…Data science offers actionable insights by mining structured and unstructured data using statistical and computational tools and methods to identify patterns. It is a growing field impacting various sectors, genres, and disciplines, and therefore places the spotlight on data science education (DSE) (Van Dusen et al, 2019). DSE is an umbrella term used to describe learning programs meant to equip data scientists with data science competencies and skills mainly from computer science, mathematics, statistics, engineering, psychology, and the domain of interest.…”

Section: Introductionmentioning

confidence: 99%

“…There is therefore a growing call for standardising DSE (Heinemann et al, 2018;Irizarry, 2020), especially in the field of curriculum design (Chen, 2020;Finzer, 2013;Song & Zhu, 2016). For instance, several academic workshops (panel sessions) and conferences have been hosted with the intent to discuss data science curriculum design (i.e., Danyluk et al, 2019;Howe et al, 2017;Mikroyannidis, Domingue, Phethean, et al, 2018;Oh et al, 2019;Van Dusen et al, 2019). However, these are still developing opportunities that might introduce some beneficial recommendations to improve DSE.…”

Section: Introductionmentioning

confidence: 99%

Data Science Education – A Scoping Review

2023

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Aim/Purpose: This study aimed to evaluate the extant research on data science education (DSE) to identify the existing gaps, opportunities, and challenges, and make recommendations for current and future DSE. Background: There has been an increase in the number of data science programs especially because of the increased appreciation of data as a multidisciplinary strategic resource. This has resulted in a greater need for skills in data science to extract meaningful insights from data. However, the data science programs are not enough to meet the demand for data science skills. While there is growth in data science programs, they appear more as a rebranding of existing engineering, computer science, mathematics, and statistics programs. Methodology: A scoping review was adopted for the period 2010–2021 using six scholarly multidisciplinary databases: Google Scholar, IEEE Xplore, ACM Digital Library, ScienceDirect, Scopus, and the AIS Basket of eight journals. The study was narrowed down to 91 research articles and adopted a classification coding framework and correlation analysis for analysis. Contribution: We theoretically contribute to the growing body of knowledge about the need to scale up data science through multidisciplinary pedagogies and disciplines as the demand grows. This paves the way for future research to understand which programs can provide current and future data scientists the skills and competencies relevant to societal needs. Findings: The key results revealed the limited emphasis on DSE, especially in non-STEM (Science, Technology, Engineering, and Mathematics) disciplines. In addition, the results identified the need to find a suitable pedagogy or a set of pedagogies because of the multidisciplinary nature of DSE. Further, there is currently no existing framework to guide the design and development of DSE at various education levels, leading to sometimes inadequate programs. The study also noted the importance of various stakeholders who can contribute towards DSE and thus create opportunities in the DSE ecosystem. Most of the research studies reviewed were case studies that presented more STEM programs as compared to non-STEM. Recommendations for Practitioners: We recommend CRoss Industry Standard Process for Data Mining (CRISP-DM) as a framework to adopt collaborative pedagogies to teach data science. This research implies that it is important for academia, policymakers, and data science content developers to work closely with organizations to understand their needs. Recommendation for Researchers: We recommend future research into programs that can provide current and future data scientists the skills and competencies relevant to societal needs and how interdisciplinarity within these programs can be integrated. Impact on Society: Data science expertise is essential for tackling societal issues and generating beneficial effects. The main problem is that data is diverse and always changing, necessitating ongoing (up)skilling. Academic institutions must therefore stay current with new advances, changing data, and organizational requirements. Industry experts might share views based on their practical knowledge. The DSE ecosystem can be shaped by collaborating with numerous stakeholders and being aware of each stakeholder’s function in order to advance data science internationally. Future Research: The study found that there are a number of research opportunities that can be explored to improve the implementation of DSE, for instance, how can CRISP-DM be integrated into collaborative pedagogies to provide a fully comprehensive data science curriculum?

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Accelerating the Advancement of Data Science Education

Cited by 4 publications

References 2 publications

Proceedings of the 19th Python in Science Conference

Proceedings of the 19th Python in Science Conference

Evaluation of EDISON's data science competency framework through a comparative literature analysis

Data Science Education – A Scoping Review

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