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Culturally Relevant Data in Teaching Statistics and Data Science Courses
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Travis Weiland; Immanuel Williams – Journal of Statistics and Data Science Education, 2024
In this article, we consider how to make data more meaningful to students through the choice of data and the activities we use them in drawing upon students lived experiences more in the teaching of statistics and data science courses. In translating scholarship around culturally relevant pedagogy from the fields of education and mathematics…
Descriptors: Undergraduate Students, Predominantly White Institutions, Statistics Education, Culturally Relevant Education
Constructing a Course on Classification Methods for Undergraduate Non-Stem Students: Striving to Reach Knowledge Discovery
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Anna Khalemsky; Roy Gelbard; Yelena Stukalin – Journal of Statistics and Data Science Education, 2025
Classification, a fundamental data analytics task, has widespread applications across various academic disciplines, such as marketing, finance, sociology, psychology, education, and public health. Its versatility enables researchers to explore diverse research questions and extract valuable insights from data. Therefore, it is crucial to extend…
Descriptors: Classification, Undergraduate Students, Undergraduate Study, Data Science
Students' Experience and Perspective of a Data Science Program in a Two-Year College
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Mary Glantz; Jennifer Johnson; Marilyn Macy; Juan J. Nunez; Rachel Saidi; Camilo Velez – Journal of Statistics and Data Science Education, 2023
Two-year colleges provide the opportunity for students of all ages to try new subjects, change careers, upskill, or begin exploring higher education, at affordable rates. Many might begin their exploration by taking a course at a local two-year college. Currently, not many of these institutions in the U.S. offer data science courses. This article…
Descriptors: Two Year Colleges, Data Science, Two Year College Students, Student Experience
Obtaining and Applying Public Data for Training Students in Technical Statistical Writing: Case Studies with Data from U.S. Geological Survey and General Ecological Literature
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Barb Bennie; Richard A. Erickson – Journal of Statistics and Data Science Education, 2024
Effective undergraduate statistical education requires training using real-world data. Textbook datasets seldom match the complexities and messiness of real-world data and finding these datasets can be challenging for educators. Consulting and industrial datasets often have nondisclosure agreements. Academic datasets often require subject area…
Descriptors: Undergraduate Students, Statistics Education, Data Science, Earth Science
Open Case Studies: Statistics and Data Science Education through Real-World Applications
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Carrie Wright; Qier Meng; Michael R. Breshock; Lyla Atta; Margaret A. Taub; Leah R. Jager; John Muschelli; Stephanie C. Hicks – Journal of Statistics and Data Science Education, 2024
With unprecedented and growing interest in data science education, there are limited educator materials that provide meaningful opportunities for learners to practice "statistical thinking," as defined by Wild and Pfannkuch, with messy data addressing real-world challenges. As a solution, Nolan and Speed advocated for bringing…
Descriptors: Statistics, Statistics Education, Open Educational Resources, Case Method (Teaching Technique)
Writing Workshops to Foster Written Communication Skills in Statistics Graduate Students
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Hildreth, Laura A.; Miley, Michelle; Strickland, Erin; Swisher, Jacob – Journal of Statistics and Data Science Education, 2023
Being able to communicate effectively is an essential skill for statisticians and data scientists. Despite this, communication skills are not frequently taught or emphasized in statistics and data science courses. In this article, we describe a series of four workshops that were developed to enhance the written communication skills of statistics…
Descriptors: Writing Workshops, Writing Skills, Communication Skills, Statistics Education
Neglected, Acknowledged, or Targeted: A Conceptual Framing of Variability, Data Analysis, and Domain Consequences
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Zachary del Rosario – Journal of Statistics and Data Science Education, 2024
Variability is underemphasized in domains such as engineering. Statistics and data science education research offers a variety of frameworks for understanding variability, but new frameworks for domain applications are necessary. This study investigated the professional practices of working engineers to develop such a framework. The Neglected,…
Descriptors: Foreign Countries, Engineering Education, Engineering, Technical Occupations
Studying the Opportunities Provided by an Applied High School Mathematics Course: Explorations in Data Science
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Jo Boaler; Kira Conte; Ken Cor; Jack A. Dieckmann; Tanya LaMar; Jesse Ramirez; Megan Selbach-Allen – Journal of Statistics and Data Science Education, 2025
This article reports on a multi-method study of a high school course in data science, finding that students who take data science take more mathematics courses than those who do not, there are more under-represented students in data science than is typical for other advanced mathematics courses; that the students who take data science are more…
Descriptors: Mathematics Instruction, Opportunities, High School Students, Data Science
Framework for Accessible and Inclusive Teaching Materials for Statistics and Data Science Courses
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Dogucu, Mine; Johnson, Alicia A.; Ott, Miles – Journal of Statistics and Data Science Education, 2023
Despite rapid growth in the data science workforce, people of color, women, those with disabilities, and others remain underrepresented in, underserved by, and sometimes excluded from the field. This pattern prevents equal opportunities for individuals, while also creating products and policies that perpetuate inequality. Thus, it is critical…
Descriptors: Access to Information, Inclusion, Instructional Materials, Statistics Education
Teaching for Large-Scale Reproducibility Verification
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Vilhuber, Lars; Son, Hyuk Harry; Welch, Meredith; Wasser, David N.; Darisse, Michael – Journal of Statistics and Data Science Education, 2022
We describe a unique environment in which undergraduate students from various STEM and social science disciplines are trained in data provenance and reproducible methods, and then apply that knowledge to real, conditionally accepted manuscripts and associated replication packages. We describe in detail the recruitment, training, and regular…
Descriptors: Statistics Education, Data Science, STEM Education, Social Sciences
Fostering the Development of Earth Data Science Skills in a Diverse Community of Online Learners: A Case Study of the Earth Data Science Corps
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Nathan A. Quarderer; Leah Wasser; Anne U. Gold; Patricia Montaño; Lauren Herwehe; Katherine Halama; Emily Biggane; Jessica Logan; David Parr; Sylvia Brady; James Sanovia; Charles Jason Tinant; Elisha Yellow Thunder; Justina White Eyes; LaShell Poor Bear/Bagola; Madison Phelps; Trey Orion Phelps; Brett Alberts; Michela Johnson; Nathan Korinek; William Travis; Naomi Jacquez; Kaiea Rohlehr; Emily Ward; Elsa Culler; R. Chelsea Nagy; Jennifer Balch – Journal of Statistics and Data Science Education, 2025
Today's data-driven world requires earth and environmental scientists to have skills at the intersection of domain and data science. These skills are imperative to harness information contained in a growing volume of complex data to solve the world's most pressing environmental challenges. Despite the importance of these skills, Earth and…
Descriptors: Electronic Learning, Earth Science, Environmental Education, Science Education