Essentials of Data Science and Analytics , livre ebook

Essentials of Data Science and Analytics
Essentials of Data Science and Analytics
Statistical Tools, Machine Learning, and R-Statistical Software Overview
Amar Sahay
Essentials of Data Science and Analytics: Statistical Tools, Machine Learning, and R-Statistical Software Overview
Copyright © Business Expert Press, LLC, 2021.
Cover design by Charlene Kronstedt
Interior design by Exeter Premedia Services Private Ltd., Chennai, India
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means—electronic, mechanical, photocopy, recording, or any other except for brief quotations, not to exceed 400 words, without the prior permission of the publisher.
First published in 2021 by
Business Expert Press, LLC
222 East 46th Street, New York, NY 10017
www.businessexpertpress.com
ISBN-13: 978-1-63157-345-3 (paperback)
ISBN-13: 978-1-63157-346-0 (e-book)
Business Expert Press Quantitative Approaches to Decision Making Collection
Collection ISSN: 2163-9515 (print)
Collection ISSN: 2163-9582 (electronic)
First edition: 2021
10 9 8 7 6 5 4 3 2 1
To Priyanka Nicole, Our Love and Joy
Description
This text provides a comprehensive overview of Data Science. With continued advancement in storage and computing technologies, data science has emerged as one of the most desired fields in driving business decisions. Data science employs techniques and methods from many other fields such as statistics, mathematics, computer science, and information science. Besides the methods and theories drawn from several fields, data science uses visualization techniques using specially designed big data software and statistical programming language, such as R programming, and Python. Data science has wide applications in the areas of Machine Learning (ML) and Artificial Intelligence (AI). The book has four parts divided into different chapters. These chapters explain the core of data science. Part I of the book introduces the field of data science, different disciplines it comprises of, and the scope with future outlook and career prospects. This section also explains analytics, business analytics, and business intelligence and their similarities and differences with data science. Since the data is at the core of data science, Part II is devoted to explaining the data, big data, and other features of data. One full chapter is devoted to data analysis, creating visuals, pivot table, and other applications using Excel with Office 365. Part III explains the statistics behind data science. It uses several chapters to explain the statistics and its importance, numerical and data visualization tools and methods, probability, and probability distribution applications in data science. Other chapters in the Part III are sampling, estimation, and hypothesis testing. All these are integral part of data science applications. Part IV of the book provides the basics of Machine Learning (ML) and R-statistical software. Data science has wide applications in the areas of Machine Learning (ML) and Artificial Intelligence (AI) and R-statistical software is widely used by data science professionals. The book also outlines a brief history, the body of knowledge, skills, and education requirements for data scientist and data science professionals. Some statistics on job growth and prospects are also summarized. A career in data science is ranked at the third best job in America for 2020 by Glassdoor and was ranked the number one best job from 2016 to 2019. 29
Primary Audience
The book is appropriate for majors in data science, analytics, business, statistics and data analysis majors, graduate students in business, MBAs, professional MBAs, and working people in business and industry who are interested in learning and applying data science in making effective business decisions. Data science is a vast area and the tools of data science are proven to be effective in making timely business decisions and predicting the future outcomes in this current competitive business environment.
The book is designed with a wide variety of audience in mind. It takes a unique approach of presenting the body of knowledge and integrating such knowledge to different areas of data science, analytics, and predictive modeling. The importance and applications of data science tools in analyzing and solving different problems is emphasized throughout the book. It takes a simple yet unique learner-centered approach in teaching data science and predictive, knowledge, and skills requires as well as the tools. The students in Information Systems interested in data science will also find the book to be useful.
Scope
This book may be used as a suggested reading for professionals in interested in data science and can also be used as a real-world applications text in data science analytics, and business intelligence.
Because of its subject matter and content, the book may also be adopted as a suggested reading in undergraduate and graduate data science, data analytics, statistics, data analysis courses, and MBA, and professional MBA courses. The businesses are now data-driven where the decisions are made using real data both collected over time and current real-time data. Data analytics is now an integral part of businesses and a number of companies rely on data, analytics, and business intelligence, and machine learning and artificial intelligence (AI) applications in making effective and timely business decisions. The professionals involved in data science and analytics, big data, visual analytics, information systems and business intelligence, business and data analytics will find this book useful.
Keywords
data science; data analytics; business analytics; business intelligence; data analysis; decision making; descriptive analytics; predictive analytics; prescriptive analytics; statistical analysis; quantitative techniques; data mining; predictive modeling; regression analysis; modeling; time-series forecasting; optimization; simulation; machine learning; neural networks; artificial intelligence
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 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 Applications in 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
Online References
Additional Readings
About the Author
Index
Preface
This book is about Data Science, one of the fastest growing fields with applications in almost all disciplines. The book provides a comprehensive overview of data science.

Data science is a data-driven decision making approach that uses several different areas, methods, algorithms, models, and disciplines with a purpose of extracting insights and knowledge from structured and unstructured data. These insights are helpful in applying algorithms and models to make decisions. The models in data science are used in predictive analytics to predict future outcomes. Machine learning and artificial intelligence (AI) are major application areas of data science.
Data science is a multidisciplinary field that provides the knowledge and skills to understand, process, and visualize data in the initial stages followed by applications of statistics, modeling, mathematics, and technology to address and solve analytically complex problems using structured and unstructured data. At the core of data science is data. It is about using this data in creative and effective ways to help businesses in making data-driven business decisions. Data science is about extracting knowledge and insights from data. Businesses and processes today are run using data. The amount of data collected now is in massive scale and is usually referred as the age of Big Data . The rapid advancement in technology is making it possible to collect, store, and process volumes of data rapidly. It is about using this data effectively using visualization, statistical analysis, and modeling tools that can help businesses driving business decisions.
The knowledge of statistics in data science is as important as the applications of computer science. Companies now collect massive amounts of data from exabytes to zettabytes, which are both structured and unstructured. The advancement in technology and the computing capabilities have made it possible to process and analyze this huge data with smarter storage spaces.
Data science is a multidisciplinary field that involves the ability to understand, process, and visualize data in the initial stages followed by applications of statistics, modeling, mathematics, and technology to address and solve analytically complex problems using