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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
What Is Missing in Missing Data Handling? An Evaluation of Missingness in and Potential Remedies for Doctoral Dissertations and Subsequent Publications That Use NHANES Data
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Hairui Yu; Suzanne E. Perumean-Chaney; Kathryn A. Kaiser – Journal of Statistics and Data Science Education, 2024
Missing data can significantly influence results of epidemiological studies. The National Health and Nutrition Examination Survey (NHANES) is a popular epidemiological dataset. We examined recent practices related to the prevalence and the reporting of the amount of missing data, the underlying mechanisms, and the methods used for handling missing…
Descriptors: Statistics Education, Data Science, Data Use, Research Problems
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
Teaching Experiential Data Analytics Using an Election Simulation
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Bay Arinze – Journal of Statistics and Data Science Education, 2023
Data Analytics has grown dramatically in importance and in the level of business deployments in recent years. It is used across most functional areas and applications, some of the latter including market campaigns, detecting fraud, determining credit, identifying assembly line defects, health services and many others. Indeed, the realm of…
Descriptors: Data Analysis, Elections, Simulation, Statistics Education
Active-Learning Class Activities and Shiny Applications for Teaching Support Vector Classifiers
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Qing Wang; Xizhen Cai – Journal of Statistics and Data Science Education, 2024
Support vector classifiers are one of the most popular linear classification techniques for binary classification. Different from some commonly seen model fitting criteria in statistics, such as the ordinary least squares criterion and the maximum likelihood method, its algorithm depends on an optimization problem under constraints, which is…
Descriptors: Active Learning, Class Activities, Classification, Artificial Intelligence
Opinionated Practices for Teaching Reproducibility: Motivation, Guided Instruction and Practice
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Ostblom, Joel; Timbers, Tiffany – Journal of Statistics and Data Science Education, 2022
In the data science courses at the University of British Columbia, we define data science as the study, development and practice of reproducible and auditable processes to obtain insight from data. While reproducibility is core to our definition, most data science learners enter the field with other aspects of data science in mind, for example…
Descriptors: Statistics Education, Data Science, Teaching Methods, Replication (Evaluation)
Personalized Education through Individualized Pathways and Resources to Adaptive Control Theory-Inspired Scientific Education (iPRACTISE): Proof-of-Concept Studies for Designing and Evaluating Personalized Education
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Sy-Miin Chow; Jungmin Lee; Jonathan Park; Prabhani Kuruppumullage Don; Tracey Hammel; Michael N. Hallquist; Eric A. Nord; Zita Oravecz; Heather L. Perry; Lawrence M. Lesser; Dennis K. Pearl – Journal of Statistics and Data Science Education, 2024
Personalized educational interventions have been shown to facilitate successful and inclusive statistics, mathematics, and data science (SMDS) in higher education through timely and targeted reduction of heterogeneous training disparities caused by years of cumulative, structural challenges in contemporary educational systems. However, the burden…
Descriptors: Individualized Instruction, Instructional Design, Science Education, Higher 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
LUSTRE: An Online Data Management and Student Project Resource
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Towse, John; Davies, Rob; Ball, Ellie; James, Rebecca; Gooding, Ben; Ivory, Matthew – Journal of Statistics and Data Science Education, 2022
We advocate for greater emphasis in training students about data management, within the context of supporting experience in reproducible workflows. We introduce the "L"ancaster "U"niversity "ST"atistics "RE"sources (LUSTRE) package, used to manage student research project data in psychology and build…
Descriptors: Data Analysis, Information Management, Open Source Technology, Data Science
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