Guide to teaching data science : an interdisciplinary approach / Orit Hazzan, Koby Mike.
Hazzan, Orit, 1962- author.; Mike, Koby, author.
2023
QA76.27
Available Online

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Title Guide to teaching data science : an interdisciplinary approach / Orit Hazzan, Koby Mike.
Author Hazzan, Orit, 1962- author.
ISBN 9783031247583
3031247582
3031247574
9783031247576
DOI https://doi.org/10.1007/978-3-031-24758-3
Published Cham, Switzerland : Springer, [2023]
Language English
Description 1 online resource (xxvii, 321 pages) : illustrations (black and white, and colour).
Other Standard Identifiers 10.1007/978-3-031-24758-3 doi
Call Number QA76.27
Dewey Decimal Classification 004.071
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)."-- Provided by publisher.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Historical Data Professor Orit Hazzan is a faculty member at the Technions 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.
Source of Description Description based on print version record and online resource (ProQuest Ebook Central, viewed March 31, 2023).
Added Author Mike, Koby, author.
Available in Other Form Guide to Teaching Data Science
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Record Appears in Online Resources > Ebooks
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1 Introduction-What is This Guide About?
Part I Overview of Data Science and Data Science Education
2 What is Data Science?
3 Data Science Thinking
4 The Birth of a New Discipline: Data Science Education

Part II Opportunities and Challenges of Data Science Education. 5 Opportunities in Data Science Education
6 The Interdisciplinarity Challenge
7 The Variety of Data Science Learners
8 Data Science as a Research Method
9 The Pedagogical Chasm in Data Science Education
Part III Teaching Professional Aspects of Data Science. 10 The Data Science Workflow
11 Professional Skills and Soft Skills in Data Science
12 Social and Ethical Issues of Data Science
Part IV Machine Learning Education. 13 The Pedagogical Challenge of Macine Learning Education
14 Core concepts of Macine Learning
15 Macine Learning Algoritms
16 Teaching Methods for Macine Learning
Part V Frameworks for Teaching Data Science. 17 Data Science for Managers and Policymakers
18 Data Science Teacher Preparation: The "Method for Teaching Data Science" Course
19 Data Science for Social Science and Digital Humanities Research
20 Data Science for Research on Human Aspects of Science and Engineering

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