Data Science for Business and Decision Making : an introductory Text for Students and Practitioners /

Data Science for Business and Decision Making : an introductory Text for Students and Practitioners /

Saved in:
Bibliographic Details
Main Author: Fallahchay, Seyed Ali
Format: Electronic eBook
Language:English
Published: [Ashland] : Arcler Press, 2020.
Subjects:
Business > Data processing.
Machine learning.
Big data.
Cloud computing.
Business intelligence.
Online Access:CONNECT
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

Similar Items