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Essentials of data science and analytics : statistical tools, machine learning, and R-statistical software overview / Amar Sahay.
- Format:
- Book
- Author/Creator:
- Sahay, Amar, author.
- Series:
- Quantitative approaches to decision making collection.
- Quantitative approaches to decision making collection
- Language:
- English
- Subjects (All):
- Business--Data processing.
- Business.
- Data mining.
- Decision making--Computer programs.
- Decision making.
- R (Computer program language).
- Physical Description:
- 1 online resource (xix, 460 pages) : illustrations.
- Edition:
- First edition.
- Place of Publication:
- New York : Business Expert Press, 2021.
- Summary:
- The book is intended for supply chain professionals, as well as for graduate and advanced undergraduate students. Practitioners will obtain valuable new insights and examples of implementable frameworks and methods for managing their supply chain functions and organizations. Students will develop an understanding of real-world approaches for supply chain planning, decision support, and many other key activities.
- Contents:
- Cover
- Half-Title
- Title
- Copyright
- Dedication
- Description
- Contents
- Preface
- Acknowledgments
- Part I: Data Science, Analytics, and Business Analytics
- Chapter 1: Data Science and Its Scope
- Chapter 2: Data Science, Analytics, and Business Analytics (BA)
- Chapter 3: Business Analytics, Business Intelligence, and Their Relation to Data Science
- Part II: Understanding Data andData Analysis Applications
- Chapter 4: Understanding Data, Data Types, and Data-Related Terms
- Chapter 5: Data Analysis Tools for Data Science and Analytics: Data Analysis Using Excel
- Part III: Data Visualization andStatistics for Data Science
- Chapter 6: Basic Statistical Concepts for Data Science
- Chapter 7: Descriptive Analytics_Visualizing Data Using Graphs and Charts
- Chapter 8: Numerical Methods for Data Science Applications
- Chapter 9: Applications of Probability in Data Science
- Chapter 10: Discrete Probability Distributions Applications in Data Science
- Chapter 11: Sampling and Sampling Distributions: Central Limit Theorem
- Chapter 12: Estimation, Confidence Intervals, Hypothesis Testing
- Part IV: Introduction to MachineLearning and R-statisticalProgramming Software
- Chapter 13: Basics of MachLearning (ML)
- Chapter 14: R Statistical Programing Software for Data Science
- Online References
- Additional Readings
- About the Author
- Index
- Adpage
- Backcover.
- Notes:
- Includes bibliographical references and index.
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 9781631573460
- 9781803162072
- 1803162074
- OCLC:
- 1257076800
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