Essentials of data science and analytics: statistical tools, machine learning, and r-statistical software overview
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York
Business Expert Press
2021
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xix, 460 Seiten Illustrationen, Diagramme |
| ISBN: | 9781631573453 |
Internformat
MARC
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Datensatz im Suchindex
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| adam_text | Contents Preface........................ xiii Acknowledgments......................................................................................... xxi Part I Data Science, Analytics, and Business Analytics............1 Chapter 1 Data Science and Its Scope.....................................................3 Chapter 2 Data Science, Analytics, and Business Chapter 3 Business Analytics, Business Intelligence, and Analytics (BA)....................................... Their Relation to Data Science.............................. Part II 19 39 Understanding Data and Data Analysis Applications........................................................................ 49 Chapter 4 Understanding Data, Data Types, and Data-Related Terms............................. Chapter 5 51 Data Analysis Tools for Data Science and Analytics: Data Analysis Using Excel................................ 67 Part III Data Visualization and Statistics for Data Science................................................................................135 Chapter 6 Basic Statistical Concepts for Data Science......................137 Chapter 7 Descriptive Analytics_Visualizing Data Using Graphs and Charts............................................ 155 Chapter 8 Numerical Methods for Data Science Applications........ 199 Chapter 9 Applications of Probability in Data Science.................... 255 Chapter 10 Discrete Probability Distributions Applications in Data Science...................................................................301 Chapter 11 Sampling and Sampling Distributions: Central Limit
Theorem...................................................................351 Chapter 12 Estimation, Confidence Intervals, Hypothesis Testing............................................................ 381
xii CONTENTS Part IV Introduction to Machine Learning and R-statistical Programming Software...................................................421 Chapter 13 Basics of MachLearning (ML)......................................... 423 Chapter 14 R Statistical Programing Software for Data Science.......437 Online Rrferences........................................................................................443 Additional Reading.t................................................................................... 445 About the Author........................................................................................449 Index.......................................................................................................... 451
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| adam_txt |
Contents Preface. xiii Acknowledgments. xxi Part I Data Science, Analytics, and Business Analytics.1 Chapter 1 Data Science and Its Scope.3 Chapter 2 Data Science, Analytics, and Business Chapter 3 Business Analytics, Business Intelligence, and Analytics (BA). Their Relation to Data Science. Part II 19 39 Understanding Data and Data Analysis Applications. 49 Chapter 4 Understanding Data, Data Types, and Data-Related Terms. Chapter 5 51 Data Analysis Tools for Data Science and Analytics: Data Analysis Using Excel. 67 Part III Data Visualization and Statistics for Data Science.135 Chapter 6 Basic Statistical Concepts for Data Science.137 Chapter 7 Descriptive Analytics_Visualizing Data Using Graphs and Charts. 155 Chapter 8 Numerical Methods for Data Science Applications. 199 Chapter 9 Applications of Probability in Data Science. 255 Chapter 10 Discrete Probability Distributions Applications in Data Science.301 Chapter 11 Sampling and Sampling Distributions: Central Limit
Theorem.351 Chapter 12 Estimation, Confidence Intervals, Hypothesis Testing. 381
xii CONTENTS Part IV Introduction to Machine Learning and R-statistical Programming Software.421 Chapter 13 Basics of MachLearning (ML). 423 Chapter 14 R Statistical Programing Software for Data Science.437 Online Rrferences.443 Additional Reading.t. 445 About the Author.449 Index. 451 |
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| title | Essentials of data science and analytics statistical tools, machine learning, and r-statistical software overview |
| title_auth | Essentials of data science and analytics statistical tools, machine learning, and r-statistical software overview |
| title_exact_search | Essentials of data science and analytics statistical tools, machine learning, and r-statistical software overview |
| title_exact_search_txtP | Essentials of data science and analytics statistical tools, machine learning, and r-statistical software overview |
| title_full | Essentials of data science and analytics statistical tools, machine learning, and r-statistical software overview Sahay Amar |
| title_fullStr | Essentials of data science and analytics statistical tools, machine learning, and r-statistical software overview Sahay Amar |
| title_full_unstemmed | Essentials of data science and analytics statistical tools, machine learning, and r-statistical software overview Sahay Amar |
| title_short | Essentials of data science and analytics |
| title_sort | essentials of data science and analytics statistical tools machine learning and r statistical software overview |
| title_sub | statistical tools, machine learning, and r-statistical software overview |
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