The Data Science Education Dilemma

Finzer, William

doi:10.5070/t572013891

Cited by 78 publications

(43 citation statements)

References 8 publications

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“…Although practicing statisticians seem to largely agree that the lion's share of the time spent on many projects is devoted to data cleaning and manipulation (or data wrangling, as it is often called (Kandel et al, 2011)), the motivation for adding these skills to the statistics curriculum is not simply convenience, nor should a lack of skills or interest on the part of instructors stand in the way. Finzer (2013) describes a "data habit of mind...that grows out of working with data." (This is not to be confused with "statistical thinking" as articulated by Chance (2002), which contains no mention of computing.)…”

Section: Background and Related Workmentioning

confidence: 99%

“…Breiman et al (2001) articulated the distinction between "statistical data models" and "algorithmic models" that in many ways characterizes the relationship between statistics and machine learning, viewing the former as being far more limited than the latter. And while machine learning and data mining have traditionally been subfields of computer science, Finzer (2013) notes that data science does not have a natural home within traditional departments, belonging exclusively to neither mathematics, statistics, or computer science. Indeed, in Cleveland ( 2001)'s seminal action plan for data science, he saw data science as a "partnership" between statisticians (i.e.…”

Section: Background and Related Workmentioning

confidence: 99%

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A Data Science Course for Undergraduates: Thinking With Data

Baumer

2015

The American Statistician

108

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Data science is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in a particular application domain for the purpose of extracting meaningful information from the increasingly sophisticated array of data available in many settings. These data tend to be non-traditional, in the sense that they are often live, large, complex, and/or messy. A first course in statistics at the undergraduate level typically introduces students to a variety of techniques to analyze small, neat, and clean data sets.However, whether they pursue more formal training in statistics or not, many of these students will end up working with data that are considerably more complex, and will need facility with statistical computing techniques. More importantly, these students require a framework for thinking structurally about data. We describe an undergraduate course in a liberal arts environment that provides students with the tools necessary to apply data science. The course emphasizes modern, practical, and useful skills that cover the full data analysis spectrum, from asking an interesting question to acquiring, managing, manipulating, processing, querying, analyzing, and visualizing data, as well communicating findings in written, graphical, and oral forms.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

A Data Science Course for Undergraduates: Thinking With Data

Baumer

2015

The American Statistician

108

View full text Add to dashboard Cite

show abstract

“…To be effective, students need to develop data habits of mind (Finzer 2013). They need to be able to think creatively about data and understand conceptions of "data tidying" (Wickham 2014).…”

Section: Data-related Skillsmentioning

confidence: 99%

Challenges and Opportunities for Statistics and Statistical Education: Looking Back, Looking Forward

Horton¹

2015

The American Statistician

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The 175th anniversary of the ASA provides an opportunity to look back into the past and peer into the future. What led our forebears to found the association? What commonalities do we still see? What insights might we glean from their experiences and observations? I will use the anniversary as a chance to reflect on where we are now and where we are headed in terms of statistical education amidst the growth of data science. Statistics is the science of learning from data. By fostering more multivariable thinking, building data-related skills, and developing simulation-based problem solving, we can help to ensure that statisticians are fully engaged in data science and the analysis of the abundance of data now available to us.

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“…The strong interest of governments in Science, Technology, Engineering, and Mathematics (STEM) fields, both individually and collectively, as critical for national economic growth and competitive advantage (e.g., Engler, 2012; Office of the Chief Scientist, 2013), has put increasing pressure on education systems to provide graduates prepared to work in these fields. At the same time, the STEMrelated fields of Big Data and Data Science have emerged (e.g., Finzer, 2013;François & Monteiro, 2018;Ridgway et al, 2018). Together these movements impact on the compulsory years of schooling in preparing students to be able to take advantage of STEM careers in the future, careers that are likely to be influenced by the power of data and statistics to drive change and innovation (Watson et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

Characterizing Student Experience of Variation Within a Stem Context: Improving Catapults

Watson

Fitzallen

Wright

et al. 2022

SERJ

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STEM learning experiences at the school level provide both opportunities and challenges for exploring students’ understanding of statistical concepts. This report focuses on data handling and informal inference embedded in a STEM context, that is, of testing, adjusting, and retesting catapults. In particular, the learning goal was for Grade 4 (aged 9–10 years) students to build on their developing understanding of variation while learning about the science topic of force as demonstrated by two configurations of catapults causing ping pong balls to be launched different distances. This report focuses on the students’ experiences of variation that were associated with the activity from a structural perspective during implementation. The analysis, employing various aspects of the Structure of Observed Learning Outcomes, points to the potential contribution of multimodal functioning in identifying and characterizing understanding of variation in a new context. The activity took place with 50 students in two classes with data collected from student workbooks. Results suggest that meaningful engagement with context can provide support for developing understanding of the concept of variation.

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The Data Science Education Dilemma

Cited by 78 publications

References 8 publications

A Data Science Course for Undergraduates: Thinking With Data

A Data Science Course for Undergraduates: Thinking With Data

Challenges and Opportunities for Statistics and Statistical Education: Looking Back, Looking Forward

Characterizing Student Experience of Variation Within a Stem Context: Improving Catapults

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