Guide to Teaching Data Science: An Interdisciplinary Approach

✍ Scribed by Orit Hazzan, Koby Mike

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 330
• Category: Library

✦ Synopsis

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

Prologue
Contents
List of Figures
List of Tables
List of Exercises
1 Introduction—What is This Guide About?
1.1 Introduction
1.2 Motivation for Writing This Guide
1.3 Pedagogical Principles and Guidelines for Teaching Data Science
1.4 The Structure of the Guide to Teaching Data Science
1.4.1 The Five Parts of the Guide
1.4.2 The Chapters of the Guide
1.5 How to Use This Guide?
1.5.1 Data Science Instructors in Academia
1.5.2 K-12 Teachers
1.5.3 Instructors of the Methods of Teaching Data Science (MTDS) Course
1.6 Learning Environments for Data Science
1.6.1 Textual Programing Environments for Data Science
1.6.2 Visual Programing Environments for Data Science
1.7 Conclusion
Reference
Part I Overview of Data Science and Data Science Education
2 What is Data Science?
2.1 The Interdisciplinary Development of Data Science
2.1.1 The Origins of Data Science in Statistics
2.1.2 The Origins of Data Science in Computer Science
2.1.3 The Origins of Data Science in Application Domains: The Case of Business Analytics
2.2 Data Science as a Science
2.3 Data Science as a Research Method
2.3.1 Exploratory Data Analysis
2.3.2 Machine Learning as a Research Method
2.4 Data Science as a Discipline
2.5 Data Science as a Workflow
2.6 Data Science as a Profession
2.7 Conclusion
References
3 Data Science Thinking
3.1 Introduction
3.2 Data Thinking and the Thinking Skills Associated with Its Components
3.2.1 Computational Thinking
3.2.2 Statistical Thinking
3.2.3 Mathematical Thinking
3.2.4 Application Domain Thinking
3.2.5 Data Thinking
3.3 Thinking About Data Science Thinking
3.4 Conclusion
References
4 The Birth of a New Discipline: Data Science Education
4.1 Introduction
4.2 Undergraduate Data Science Curricula Initiatives
4.2.1 Strengthening Data Science Education Through Collaboration, 2015
4.2.2 Curriculum Guidelines for Undergraduate Programs in Data Science, 2016
4.2.3 The EDISON Data Science Framework, 2017
4.2.4 Envisioning the Data Science Discipline, 2018
4.2.5 Computing Competencies for Undergraduate Data Science Curricula, 2017–2021
4.3 Data Science Curriculum for K-12
4.4 Meta-Analysis of Data Science Curricula
4.5 Conclusion
References
Part II Opportunities and Challenges of Data Science Education
5 Opportunities in Data Science Education
5.1 Introduction
5.2 Teaching STEM in a Real-World Context
5.3 Teaching STEM with Real-World Data
5.4 Bridging Gender Gaps in STEM Education
5.5 Teaching Twenty-First Century Skills
5.6 Interdisciplinary Pedagogy
5.7 Professional Development for Teachers
5.8 Conclusion
References
6 The Interdisciplinarity Challenge
6.1 Introduction
6.2 The Interdisciplinary Structure of Data Science
6.3 Is Data Science More About Computer Science or More About Statistics?
6.4 Integrating the Application Domain
6.4.1 Data Science Pedagogical Content Knowledge (PCK)
6.4.2 Developing Interdisciplinary Programs
6.4.3 Integrating the Application Domain into Courses in Computer Science, Mathematics, and Statistics
6.4.4 Mentoring Interdisciplinary Projects
6.5 Conclusion
References
7 The Variety of Data Science Learners
7.1 Introduction
7.2 Data Science for K-12 Pupils
7.3 Data Science for High School Computer Science Pupils
7.4 Data Science for Undergraduate Students
7.5 Data Science for Graduate Students
7.6 Data Science for Researchers
7.7 Data Science for Data Science Educators
7.8 Data Science for Professional Practitioners in the Industry
7.9 Data Science for Policy Makers
7.10 Data Science for Users
7.11 Data Science for the General Public
7.12 Activities on Learning Environments for Data Science
7.13 Conclusion
References
8 Data Science as a Research Method
8.1 Introduction
8.2 Data Science as a Research Method
8.2.1 Data Science Research as a Grounded Theory
8.2.2 The Application Domain Knowledge in Data Science Research
8.3 Research Skills
8.3.1 Cognitive Skills: Awareness of the Importance of Model Assessment—Explainability and Evaluation
8.3.2 Organizational Skills: Understanding the Field of the Organization
8.3.3 Technological Skills: Data Visualization
8.4 Pedagogical Challenges of Teaching Research Skills
8.5 Conclusion
References
9 The Pedagogical Chasm in Data Science Education
9.1 The Diffusion of Innovation Theory
9.2 The Crossing the Chasm Theory
9.3 The Data Science Curriculum Case Study from the Diffusion of Innovation Perspective
9.3.1 The Story of the New Program
9.3.2 The Teachers’ Perspective
9.4 The Pedagogical Chasm
9.5 Conclusion
References
Part III Teaching Professional Aspects of Data Science
10 The Data Science Workflow
10.1 Data Workflow
10.2 Data Collection
10.3 Data Preparation
10.4 Exploratory Data Analysis
10.5 Modeling
10.5.1 Data Quantity, Quality, and Coverage
10.5.2 Feature Engineering
10.6 Communication and Action
10.7 Conclusion
References
11 Professional Skills and Soft Skills in Data Science
11.1 Introduction
11.2 Professional Skills
11.2.1 Cognitive Skills: Thinking on Different Levels of Abstraction
11.2.2 Organizational Skills: Storytelling
11.2.3 Technological Skills: Programming for Data Science
11.3 Soft Skills
11.3.1 Cognitive Skills: Learning
11.3.2 Organizational Skills: Teamwork and Collaboration
11.3.3 Technological Skills: Debugging Data and Models
11.4 Teaching Notes
11.5 Conclusion
References
12 Social and Ethical Issues of Data Science
12.1 Introduction
12.2 Data Science Ethics
12.3 Methods of Teaching Social Aspects of Data Science
12.3.1 Teaching Principles
12.3.2 Kinds of Activities
12.4 Conclusion
References
Part IV Machine Learning Education
13 The Pedagogical Challenge of Machine Learning Education
13.1 Introduction
13.2 Black Box and White Box Understandings
13.3 Teaching ML to a Variety of Populations
13.3.1 Machine Learning for Data Science Majors and Allied Majors
13.3.2 Machine Learning for Non-major Students
13.3.3 Machine Learning for ML Users
13.4 Framework Remarks for ML Education
13.4.1 Statistical Thinking
13.4.2 Interdisciplinary Projects
13.4.3 The Application Domain Knowledge
13.5 Conclusion
References
14 Core Concepts of Machine Learning
14.1 Introduction
14.2 Types of Machine Learning
14.3 Machine Learning Parameters and Hyperparameters
14.4 Model Training, Testing, and Validation
14.5 Machine Learning Performance Indicators
14.6 Bias and Variance
14.7 Model Complexity
14.8 Overfitting and Underfitting
14.9 Loss Function Optimization and the Gradient Descent Algorithm
14.10 Regularization
14.11 Conclusion
References
15 Machine Learning Algorithms
15.1 Introduction
15.2 K-nearest Neighbors
15.3 Decision Trees
15.4 Perceptron
15.5 Linear Regression
15.6 Logistic Regression
15.7 Neural Networks
15.8 Conclusion
References
16 Teaching Methods for Machine Learning
16.1 Introduction
16.2 Visualization
16.3 Hand-On Tasks
16.3.1 Hands-On Task for the KNN Algorithm
16.3.2 Hands-On Task for the Perceptron Algorithm
16.3.3 Hands-On Task for the Gradient Descent Algorithm
16.3.4 Hands-On Task for Neural Networks
16.4 Programming Tasks
16.5 Project-Based Learning
16.6 Conclusion
References
Part V Frameworks for Teaching Data Science
17 Data Science for Managers and Policymakers
17.1 Introduction
17.2 Workshop for Policymakers in National Education Systems
17.2.1 Workshop Rationale and Content
17.2.2 Workshop Schedule
17.2.3 Group Work Products
17.2.4 Workshop Wrap-Up
17.3 Conclusion
References
18 Data Science Teacher Preparation: The “Method for Teaching Data Science” Course
18.1 Introduction
18.2 The MTDS Course Environment
18.3 The MTDS Course Design
18.4 The Learning Targets and Structure of the MTDS Course
18.5 Grading Policy and Submissions
18.6 Teaching Principles of the MTDS Course
18.7 Lesson Descriptions
18.7.1 Lesson 6
18.7.2 Mid-Semester Questionnaire
18.7.3 Lesson 7
18.8 Conclusion
References
19 Data Science for Social Science and Digital Humanities Research
19.1 Introduction
19.2 Relevance of Data Science for Social Science and Digital Humanities Researchers
19.3 Data Science Bootcamps for Researchers in Social Sciences and Digital Humanities
19.3.1 Applicants and Participants of Two 2020 Bootcamps for Researchers in Social Sciences and Digital Humanities
19.3.2 The Design and Curriculum of the Data Science for Social Science and Digital Humanities Researchers Bootcamp
19.4 Data Science for Psychological Sciences
19.4.1 The Computer Science for Psychological Science Course
19.4.2 The Data Science for Psychology Science Course
19.5 Data Science for Social Sciences and Digital Humanities, from a Motivation Theory Perspective
19.5.1 The Self-determination Theory
19.5.2 Gender Perspective
19.6 Conclusion
References
20 Data Science for Research on Human Aspects of Science and Engineering
20.1 Introduction
20.2 Examples of Research Topics Related to Human Aspects of Science and Engineering that Can Use Data Science Methods
20.3 Workshop on Data Science Research on Human Aspects of Science and Engineering
20.3.1 Workshop Rationale
20.3.2 Workshop Contents
20.3.3 Target Audience
20.3.4 Workshop Framework (in Terms of weeks)—A Proposal
20.3.5 Prerequisites
20.3.6 Workshop Requirements and Assessment
20.3.7 Workshop Schedule and Detailed Contents
20.3.8 Literature (For the Workshop)
20.4 Conclusion
Epilogue
Index