Authors:
- Offers a pioneering, comprehensive approach: conceptual, technological, activity-based, interdisciplinary
- Develops pedagogical guidelines using elements from cognitive and social psychology research
- Focuses on teaching on a variety of levels—from and for academia as well as professionals in relevant industry
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Table of contents (20 chapters)
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Front Matter
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Overview of Data Science and Data Science Education
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Front Matter
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Opportunities and Challenges of Data Science Education
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Front Matter
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Teaching Professional Aspects of Data Science
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Front Matter
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Machine Learning Education
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Front Matter
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About this book
Data science is a new field that touches on almost every domain of our lives, and thus it is taught in a variety of environments. Accordingly, the book is suitable for teachers and lecturers in all educational frameworks: K-12, academia and industry.
This book aims at closing a significant gap in the literature on the pedagogy of data science. While there are many articles and white papers dealing with the curriculum of data science (i.e., what to teach?), the pedagogical aspect of the field (i.e., how to teach?) is almost neglected. At the same time, the importance of the pedagogical aspects of data science increases as more and more programs are currently open to a variety of people.
This book provides a variety of pedagogical discussions and specific teaching methods and frameworks, as well as includes exercises, and guidelines related to many data science concepts (e.g., data thinking and the data science workflow), main machine learning algorithms and concepts (e.g., KNN, SVM, Neural Networks, performance metrics, confusion matrix, and biases) and data science professional topics (e.g., ethics, skills and research approach).
Professor Orit Hazzan is a faculty member at the Technion’s Department of Education in Science and Technology since October 2000. Her research focuses on computer science, software engineering and data science education. Within this framework, she studies the cognitive and social processes on the individual, the team and the organization levels, in all kinds of organizations.
Dr. Koby Mike is a Ph.D. graduate from the Technion's Department of Education in Science and Technology under the supervision of Professor Orit Hazzan. He continued his post-doc research on data science education at the Bar-Ilan University, and obtained a B.Sc. and an M.Sc. in Electrical Engineering from Tel Aviv University.
Authors and Affiliations
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Department of Education in Science and Technology, Technion—Israel Institute of Technology, Haifa, Israel
Orit Hazzan
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Department of Education in Science and Technology, Technion—Israel Instutite of Technology, Haifa, Israel
Koby Mike
About the authors
Dr. Koby Mike is a Ph.D. graduate from the Technion's Department of Education in Science and Technology under the supervision of Professor Orit Hazzan. He continued his a post-doc research on data science education at the Bar-Ilan University, and retains B.Sc. and an M.Sc. in Electrical Engineering from Tel Aviv University. After two decades of professional career is the Israeli hi-tech industry, he returned to academia for his doctoral studies on data science education. As part of is research, Koby developed and taught several data science programs for high school students, high school computer science teachers, and graduate students and researchers in social sciences and digital humanities.
Bibliographic Information
Book Title: Guide to Teaching Data Science
Book Subtitle: An Interdisciplinary Approach
Authors: Orit Hazzan, Koby Mike
DOI: https://doi.org/10.1007/978-3-031-24758-3
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-24757-6Published: 21 March 2023
Softcover ISBN: 978-3-031-24760-6Published: 22 March 2024
eBook ISBN: 978-3-031-24758-3Published: 20 March 2023
Edition Number: 1
Number of Pages: XXVII, 321
Number of Illustrations: 13 b/w illustrations, 30 illustrations in colour
Topics: Data Structures and Information Theory, Artificial Intelligence, Education, general, Statistics, general, Computers and Education, Algorithms