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Guide to Teaching Data Science An Interdisciplinary Approach

Title
Guide to Teaching Data Science [electronic resource] : An Interdisciplinary Approach / by Orit Hazzan, Koby Mike.
ISBN
9783031247583
Edition
1st ed. 2023.
Publication
Cham : Springer International Publishing : Imprint: Springer, 2023.
Physical Description
1 online resource (XXVII, 321 p.) 43 illus., 30 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
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.
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
March 21, 2023
Contents
Part I: Overview of Data Science and Data Science Education
Chapter 1. Introduction
Chapter 2. What is data science
Chapter 3. Introduction to data science education
Chapter 4. Data science thinking
Part II: Challenges of Data Science Education
Chapter 5. The pedagogical challenge of data science education
Chapter 6. Data science education and the variety of learners
Chapter 7. The interdisciplinarity challenge
Chapter 8. Data science skills
Part III: Data science Teaching frameworks
Chapter 9. Teacher Preparation - the Method for Teaching Data Science course
Chapter 10. Data Science for Social Science
Chapter 11. Conclusion.
Also listed under
Mike, Koby. author.
SpringerLink (Online service)
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