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Essentials of data science and analytics : statistical tools, machine learning, and R-statistical software overview / Amar Sahay.

By: Material type: TextTextSeries: Quantitative approaches to decision making collectionPublisher: New York, New York (222 East 46th Street, New York, NY 10017) : Business Expert Press, [(c)2021.]Edition: First editionDescription: 1 online resource (xix, 460 pages) : illustrations (some color)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781631573460
Subject(s): Genre/Form: Additional physical formats: Print version:: No titleLOC classification:
  • HF5548.2
Online resources: Available additional physical forms:
Contents:
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 and data 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 and statistics 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 applicationsin data science ; Chapter 11. Sampling and sampling distributions: central limit theorem ; Chapter 12. Estimation, confidence intervals, hypothesis testing -- Part IV. Introduction to machine learning and R-statistical programming software. Chapter 13. Basics of MachLearning (ML) ; Chapter 14. R statistical programing software for data science.
Abstract: Data science and analytics have emerged as the most desired fields in driving business decisions. Using the techniques and methods of data science, decision makers can uncover hidden patterns in their data, develop algorithms and models that help improve processes and make key business decisions.Data science is a data driven decision making approach that uses several different areas and disciplines with a purpose of extracting insights and knowledge from structured and unstructured data. The algorithms and models of data science along with machine learning and predictive modeling are widely used in solving business problems and predicting future outcomes. This book combines the key concepts of data science and analytics to help you gain a practical understanding of these fields. The four different sections of the book are divided into chapters that explain the core of data science. Given the booming interest in data science, this book is timely and informative.
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Item type Current library Collection Call number URL Status Date due Barcode
Online Book (LOGIN USING YOUR MY CIU LOGIN AND PASSWORD) Online Book (LOGIN USING YOUR MY CIU LOGIN AND PASSWORD) G. Allen Fleece Library ONLINE HF5548.2 (Browse shelf(Opens below)) Link to resource Available BEP9781631573460
Online Book (LOGIN USING YOUR MY CIU LOGIN AND PASSWORD) Online Book (LOGIN USING YOUR MY CIU LOGIN AND PASSWORD) G. Allen Fleece Library Non-fiction HF5548.2 (Browse shelf(Opens below)) Link to resource Available 9781631573460

Includes bibliographies and index.

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 and data 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 and statistics 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 applicationsin data science ; Chapter 11. Sampling and sampling distributions: central limit theorem ; Chapter 12. Estimation, confidence intervals, hypothesis testing -- Part IV. Introduction to machine learning and R-statistical programming software. Chapter 13. Basics of MachLearning (ML) ; Chapter 14. R statistical programing software for data science.

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Data science and analytics have emerged as the most desired fields in driving business decisions. Using the techniques and methods of data science, decision makers can uncover hidden patterns in their data, develop algorithms and models that help improve processes and make key business decisions.Data science is a data driven decision making approach that uses several different areas and disciplines with a purpose of extracting insights and knowledge from structured and unstructured data. The algorithms and models of data science along with machine learning and predictive modeling are widely used in solving business problems and predicting future outcomes. This book combines the key concepts of data science and analytics to help you gain a practical understanding of these fields. The four different sections of the book are divided into chapters that explain the core of data science. Given the booming interest in data science, this book is timely and informative.

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