Statistics Practicum: Placing 'Practice' at the Center of Data Science Education

Kolaczyk, Eric D.; Wright, Haviland; Yajima, Masanao

doi:10.1162/99608f92.2d65fc70

Cited by 15 publications

(9 citation statements)

References 0 publications

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“…An important thread throughout our curriculum, starting with STA130, is engagement with real problems. Similar to Kolaczyk et al (2021), this experience is not delayed until a capstone. The efforts required for this are substantial, and well recognized.…”

Section: Real Problems: the Need For Experiential Learningmentioning

confidence: 91%

The Building Blocks of Statistical Education in the Data Science Ecosystem

Gibbs

Taback

2021

Harvard Data Science Review

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Section: Real Problems: the Need For Experiential Learningmentioning

confidence: 91%

The Building Blocks of Statistical Education in the Data Science Ecosystem

Gibbs

Taback

2021

Harvard Data Science Review

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“…The history of statistics education for undergraduate and graduate social science students includes a long list of pedagogical reforms and improvements (Carter et al, 2017;Crooks et al, 2019;Fillebrown, 1994;Hazan et al, 2018;Immekus, 2019;Kolaczyk et al, 2021;Prodromou & Dunne, 2017). Since many social science students experience some degree of anxiety about learning statistics, frequently as a result of a bad experience of learning mathematics at school, several methods have been proposed to better engage students with statistics.…”

Section: Data Science and Statistical Educationmentioning

confidence: 99%

“…Since many social science students experience some degree of anxiety about learning statistics, frequently as a result of a bad experience of learning mathematics at school, several methods have been proposed to better engage students with statistics. These methods are based on using real-life data that are relevant to the students (Fillebrown, 1994;Neumann et al, 2013;Prodromou & Dunne, 2017;Wiberg, 2009), problem-based learning (Buckley et al, 2015), project-based learning (Fillebrown, 1994), flipped classrooms (Immekus, 2019), real-life practicum (Kolaczyk et al, 2021), and hands-on activities (Hancock & Rummerfield, 2020;Pfaff & Weinberg, 2009).…”

Section: Data Science and Statistical Educationmentioning

confidence: 99%

Machine Learning for Non-Majors: A White Box Approach

Mike

Hazzan

2022

SERJ

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Data science is a new field of research, with growing interest in recent years, that focuses on extracting knowledge and value from data. New data science education programs, which are being launched at a growing rate, are designed for multiple levels, beginning with elementary school pupils. Machine learning is an important element of data science that requires an extensive background in mathematics. While it is possible to teach the principles of machine learning as a black box, it might be difficult to improve algorithm performance without a white box understanding of the underlaying learning algorithms. In this paper, we suggest pedagogical methods to support white box understanding of machine learning algorithms for learners who lack the needed graduate level of mathematics, particularly high school computer science pupils.

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“…As many argued before (e.g., Blei & Smyth, 2017), data science aims to solve realworld problems and address domain-specific questions, thus making experiential learning a pillar of any comprehensive educational program. Whether the students are engaged in capstone projects at the end of their education, or practicum courses are at the center of their programs (Kolaczyk et al, 2021), the opportunity to design meaningful experiences should be a dominant discussion point in any conversation of data science education.…”

Section: Experiential Learningmentioning

confidence: 99%