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Direct linkBahar Memarian; Tenzin Doleck – Education and Information Technologies, 2024
The development of data science curricula has gained attention in academia and industry. Yet, less is known about the pedagogical practices and tools employed in data science education. Through a systematic literature review, we summarize prior pedagogical practices and tools used in data science initiatives at the higher education level.…
Descriptors: Data Science, Teaching Methods, Literature Reviews, Curriculum Development
Yim Register – ProQuest LLC, 2024
The field of Data Science has seen rapid growth over the past two decades, with a high demand for people with skills in data analytics, programming, statistics, and ability to visualize, predict from, and otherwise make sense of data. Alongside the rise of various artificial intelligence (AI) and machine learning (ML) applications, we have also…
Descriptors: Artificial Intelligence, Ethics, Algorithms, Data Science
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
Chen, Huan; Wang, Ye; Li, You; Lee, Yugyung; Petri, Alexis; Cha, Teryn – Education and Information Technologies, 2023
Artificial intelligence (AI) has been widely adopted in higher education. However, the current research on AI in higher education is limited lacking both breadth and depth. The present study fills the research gap by exploring faculty members' perception on teaching AI and data science related courses facilitated by an open experiential AI…
Descriptors: College Faculty, Computer Science Education, Control Groups, Data Science
Aparajita Jaiswal – ProQuest LLC, 2022
The discipline of data science has gained substantial attention recently. This is mainly attributed to the technological advancement that led to an exponential increase in computing power and has made the generation and recording of enormous amounts of data possible on an everyday basis. It has become crucial for industries to wrangle, curate, and…
Descriptors: Data Science, Skill Development, Undergraduate Students, Science Process Skills
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
Rethlefsen, Melissa L.; Norton, Hannah F.; Meyer, Sarah L.; MacWilkinson, Katherine A.; Smith, Plato L.; Ye, Hao – Journal of Statistics and Data Science Education, 2022
Research Reproducibility: Educating for Reproducibility, Pathways to Research Integrity was an interdisciplinary, conference hosted virtually by the University of Florida in December 2020. This event brought together educators, researchers, students, policy makers, and industry representatives from across the globe to explore best practices,…
Descriptors: Interdisciplinary Approach, Educational Research, Replication (Evaluation), Integrity
