Guide to Teaching Data Science : An Interdisciplinary Approach

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ABOUT THE 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.


TABLE OF CONTENTS

  1. Introduction—What is This Guide About?

    • Orit Hazzan, Koby Mike
    Pages 1-15
  2. Overview of Data Science and Data Science Education

    1. Front Matter

      Pages 17-17
    2. What is Data Science?

      • Orit Hazzan, Koby Mike
      Pages 19-34
    3. Data Science Thinking

      • Orit Hazzan, Koby Mike
      Pages 35-57
  3. Opportunities and Challenges of Data Science Education

    1. Front Matter

      Pages 73-73
    2. Opportunities in Data Science Education

      • Orit Hazzan, Koby Mike
      Pages 75-83
    3. The Interdisciplinarity Challenge

      • Orit Hazzan, Koby Mike
      Pages 85-99
    4. The Variety of Data Science Learners

      • Orit Hazzan, Koby Mike
      Pages 101-120
    5. Data Science as a Research Method

      • Orit Hazzan, Koby Mike
      Pages 121-135
    6. The Pedagogical Chasm in Data Science Education

      • Orit Hazzan, Koby Mike
      Pages 137-148
  4. Teaching Professional Aspects of Data Science

    1. Front Matter

      Pages 149-149
    2. The Data Science Workflow

      • Orit Hazzan, Koby Mike
      Pages 151-163
    3. Professional Skills and Soft Skills in Data Science

      • Orit Hazzan, Koby Mike
      Pages 165-178
    4. Social and Ethical Issues of Data Science

      • Orit Hazzan, Koby Mike
      Pages 179-195
  5. Machine Learning Education

    1. Front Matter

      Pages 197-197
    2. Core Concepts of Machine Learning

      • Orit Hazzan, Koby Mike
      Pages 209-224
    3. Machine Learning Algorithms

      • Orit Hazzan, Koby Mike
      Pages 225-234

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