Guide to Teaching Data Science : An Interdisciplinary Approach / by Orit Hazzan, Koby Mike.

Format:

Book

Author/Creator:

Hazzan, Orit., Author.

Mike, Koby., Author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Language:

English

Subjects (All):

Artificial intelligence-Data processing.

Teaching.

Quantitative research.

Education-Data processing.

Algorithms.

Data Science.

Didactics and Teaching Methodology.

Data Analysis and Big Data.

Computers and Education.

Local Subjects:

Data Science.

Didactics and Teaching Methodology.

Data Analysis and Big Data.

Computers and Education.

Algorithms.

Physical Description:

1 online resource (XXVII, 321 pages) : 43 illustrations, 30 illustrations in color.

Edition:

1st ed. 2023.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2023.

System Details:

text file PDF

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 (id est, what to teach?), the pedagogical aspect of the field (id est, 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.

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.

Other Format:

Printed edition:

ISBN:

978-3-031-24758-3

9783031247583

Access Restriction:

Restricted for use by site license.