Data Science for Business and Decision Making: An Introductory Text for Students and Practitioners

✍ Scribed by Seyed Ali Fallahchay (Author)

• Publisher: Arcler Press
• Year: 2020
• Tongue: English
• Leaves: 218
• Category: Library

✦ Synopsis

This book explores the principles underpinning data science. It considers the how and why of modern data science. The book goes further than existing books by applying data to decision making. Not only is the book useful for undergraduates, but it can also help business owners in improving their decision making. Using real life examples, this book explores the possibilities and limitations of an information-based decision making framework.

✦ Table of Contents

Cover
Title Page
Copyright
ABOUT THE AUTHOR
TABLE OF CONTENTS
List of Figures
List of Abbreviations
Preface
Chapter 1 Introduction to Data Science
1.1. The Scientific Method And Processes
1.2. Knowledge Extraction Using Algorithms
1.3. Insights Into Structured And Unstructured Data
1.4. Data Mining And Big Data
1.5. Use Of Hardware And Software Systems
Chapter 1: Summary
Chapter 2 Peripatetic And Amalgamated Uses of Methodologies
2.1. Statistical Components In Data Science
2.2. Analytical Pathways For Business Data
2.3. Machine Learning (Ml) As A New Pathway
2.4. The Use Of Data-Driven Science
2.5. Empirical, Theoretical, And Computational Underpinnings
Chapter 2: Summary
Chapter 3 The Changing Face of Data Science
3.1. Introduction Of Information Technology
3.2. The Data Deluge
3.3. Database Management Techniques
3.4. Distributed And Parallel Systems
3.5. Business Analytics (BA), Intelligence, And Predictive Modeling
Chapter 3: Summary
Chapter 4 Statistical Applications of Data Science
4.1. Public Sector Uses of Data Science
4.2. Data as a Competitive Advantage
4.3. Data Engineering Practices
4.4. Applied Data Science
4.5. Predictive and Explanatory Theories of Data Science
Chapter 4: Summary
Chapter 5 The Future of Data Science
5.1. Increased Usage of Open Science
5.2. Co-Production And Co-Consumption of Data Science
5.3. Better Reproducibility of Data Science
5.4. Transparency In The Production And Use of Data Science
5.5. Changing Research Paradigms In Academia
Chapter 5: Summary
Chapter 6 The Data Science Curriculum
6.1. Advanced Probability And Statistical Techniques
6.2. Software Packages Such As Microsoft Excel And Python
6.3. Social Statistics And Social Enterprise
6.4. Computational Competence For Business Leaders
6.5. The Language Of Data Science
Chapter 6: Summary
Chapter 7 Ethical Considerations in Data Science
7.1. Data Protection And Privacy
7.2. Informed Consent And Primary Usage
7.3. Data Storage And Security
7.4. Data Quality Controls
7.5. Business Secrets And Political Interference
Chapter 7: Summary
Chapter 8 How Data Science Supports Business Decision-Making
8.1. Opening Up The Perspective Of The Decision Maker
8.2. Properly Evaluating Feasible Options
8.3. Justification Of Decisions
8.4. Maintaining Records Of Decision Rationale
8.5. Less Subjectivity And More Objectivity In Decision-Making
Chapter 8: Summary
Concluding Remarks
Bibliography
Index
Back Cover