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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
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Victoria Woodard – Journal of Statistics and Data Science Education, 2023
In many collegiate level statistics courses, the focus of the learning outcomes is often on the analysis of data after it has been collected. Students are provided with clean data sets from previous studies to practice statistical analysis, but receive little to no application as to the amount of time and effort that goes in to collecting good…
Descriptors: Research Design, Data Collection, Statistics Education, Active Learning
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Mayer, Benjamin; Kuemmel, Anja; Meule, Marianne; Muche, Rainer – Journal of Statistics and Data Science Education, 2023
Teaching practical skills is of particular interest in the study of human medicine. With regard to medical statistics this means the use of statistical software, which may be effectively taught by a flipped classroom approach. As a pilot study, we designed and implemented an elective course on medical statistics that focused on hands-on data…
Descriptors: Computer Software, Medicine, Statistics, Flipped Classroom
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Vance, Eric A. – Journal of Statistics and Data Science Education, 2021
Data science is collaborative and its students should learn teamwork and collaboration. Yet it can be a challenge to fit the teaching of such skills into the data science curriculum. Team-Based Learning (TBL) is a pedagogical strategy that can help educators teach data science better by flipping the classroom to employ small-group collaborative…
Descriptors: Cooperative Learning, Data Analysis, Statistics Education, Flipped Classroom
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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)
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Polak, Julia; Cook, Dianne – Journal of Statistics and Data Science Education, 2021
Kaggle is a data modeling competition service, where participants compete to build a model with lower predictive error than other participants. Several years ago they released a simplified service that is ideal for instructors to run competitions in a classroom setting. This article describes the results of an experiment to determine if…
Descriptors: Artificial Intelligence, Data Analysis, Models, Competition
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Schwab-McCoy, Aimee; Baker, Catherine M.; Gasper, Rebecca E. – Journal of Statistics and Data Science Education, 2021
In the past 10 years, new data science courses and programs have proliferated at the collegiate level. As faculty and administrators enter the race to provide data science training and attract new students, the road map for teaching data science remains elusive. In 2019, 69 college and university faculty teaching data science courses and…
Descriptors: Statistics Education, Higher Education, College Students, Teaching Methods