Practical Business Analytics Using R and Python: Solve Business Problems Using a

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This book illustrates how data can be useful in solving business problems. It explores various analytics techniques for using data to discover hidden patterns and relationships, predict future outcomes, optimize efficiency and improve the performance of organizations. You'll learn how to analyze data by applying concepts of statistics, probability theory, and linear algebra. In this new edition, both R and Python are used to demonstrate these analyses. Practical Business Analytics Using R and Python also features new chapters covering databases, SQL, Neural networks, Text Analytics, and Natural Language Processing.Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models using statistical machine learning and discusses concepts like regression, classification, and neural networks. Part three covers two of the most popular unsupervised learning techniques, clustering and association mining, as well as text mining and natural language processing (NLP). The book concludes with an overview of big data analytics, R and Python essentials for analytics including libraries such as pandas and NumPy.Upon completing this book, you will understand how to improve business outcomes by leveraging R and Python for data analytics.What You Will LearnMaster the mathematical foundations required for business analyticsUnderstand various analytics models and data mining techniques such as regression, supervised machine learning algorithms for modeling, unsupervised modeling techniques, and how to choose the correct algorithm for analysis in any given taskUse R and Python to develop descriptive models, predictive models, and optimize modelsInterpret and recommend actions based on analytical model outcomesWho This Book Is ForSoftware professionals and developers, managers, and executives who want to understand and learn the fundamentals of analytics using R and Python.

FORMATPaperback LANGUAGEEnglish CONDITIONBrand New Back Cover

This updated book builds on the basic foundations established in the first edition, illustrating how data can predict future outcomes, optimize the efficiency and performance of organizations, and detect relevant patterns and relationships. It will also teach you to analyze and understand data by applying concepts of probability theory, statistics, and machine learning. This new edition also uses Python coding examples in addition to R. It also features new chapters, including coverage of SQL, NLP, and optimization models in R and Python. Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models, statistical learning models, and machine learning models. It discusses concepts like regression, classification, and neural networks. Part three covers the two popular unsupervised learning techniques used in text mining and natural language processing (NLP). It explains how to utilize clustering techniques and mine text to discover insights. The book concludes with an overview of big data analytics, R and Python for analytics, and explores Jupyter Notebook, pandas, and NumPy python libraries used in data analytics. You will: Understand various analytics models, data mining, and machine learning algorithms for modeling, and how to choose which to use for a given task Use tools and techniques to develop descriptive models, predictive models, and optimization models Interpret and recommend actions based on analytical output

Author Biography

Dr. Umesh Hodeghatta Rao is an engineer, a scientist, and an educator. He is currently a faculty member at Northeastern University, MA, USA, specializing in data analytics, AI, machine learning, deep learning, natural language processing (NLP), and cyber security.  He has more than 25 years of work experience in technical and senior management positions at AT&T Bell Laboratories, Cisco Systems, McAfee, and Wipro. He was also a faculty member at Kent State University, Kent, Ohio, USA and Xavier Institute of Management, Bhubaneswar, India. He has his master's degree in Electrical and Computer Engineering (ECE) from Oklahoma State University, USA and a Ph.D. from the Indian Institute of Technology (IIT), Kharagpur. His research interest is applying AI Machine Learning to strengthen an organization's information security based on his expertise on Information Security and Machine Learning. As a Chief Data Scientist, he is helping business leaders to make informed decisions and recommendations linked to the organization's strategy and financial goals, reflecting an awareness of external dynamics based on a data-driven approach.He has published many journal articles in international journals and conference proceedings.  In addition, he has authored books titled "Business Analytics Using R: A Practical Approach" and "The InfoSec Handbook: An Introduction to Information Security" published by Springer Apress, USA. Furthermore, Dr. Hodeghatta has contributed his services to many professional organizations and regulatory bodies. He was an Executive Committee member of IEEE Computer Society (India); Academic advisory member for the Information and Security Audit Association (ISACA), USA; IT advisor for the government of India; Technical Advisory Member of the International Neural Network Society (INNS) India; Advisory member of Task Force on Business Intelligence & Knowledge Management; He is listed in Who's Who in the World in theyear 2012, 2013, 2014, 2015 and 2016. He is also a senior member of the IEEE, USA.  Umesha Nayak is a director and principal consultant of MUSA Software Engineering Pvt. Ltd. which focuses on systems/process/management consulting.  He has 33 years experience, of which 12 years are in providing consulting to IT / manufacturing and other organizations from across the globe. He is a Master of Science in Software Systems; Master of Arts in Economics; CAIIB; Certified Information Systems Auditor (CISA), and Certified Risk and Information Systems Control (CRISC) professional from ISACA, US; PGDFM; Certified Ethical Hacker from EC Council; Certified Lead Auditor for many of the standards; Certified Coach among others.  He has worked extensively in banking, software development, product design and development, project management, program management, information technology audits, information application audits, quality assurance, coaching, product reliability, human resource management, and consultancy.  He was Vice President and Corporate Executive Council member at Polaris Software Lab, Chennai prior to his current assignment.  He has also held various roles like Head of Quality, Head of SEPG and Head of Strategic Practice Unit – Risks & Treasury at Polaris Software Lab.  He started his journey with computers in 1981 with ICL mainframes and continued further with minis and PCs.  He was one of the founding members of the information systems auditing in the banking industry in India.  He has effectively guided many organizations through successful ISO 9001/ISO 27001/CMMI and other certifications and process/product improvements. He has co-authored the open access book The InfoSec Handbook: An Introduction to Information Security, published by Apress.

Table of Contents

Section 1: Introduction to Analytics.- Chapter 1: Business Analytics Revolution.- Chapter 2: Foundations of Business Analytics.- Chapter 3: Structured Query Language (SQL) Analytics.- Chapter 4: Business Analytics Process.- Chapter 5: Exploratory Data Analysis (EDA).- Chapter 6: Evaluating Analytics Model Performance.- Section II: Supervised Learning and Predictive Analytics.- Chapter 7: Simple Linear Regressions.- Chapter 8: Multiple Linear Regressions.- Chapter 9: Classification.- Chapter 10: Neural Networks.- Chapter 11: Logistic Regression.- Section III: Time Series Models.- Chapter 12: Time Series – Forecasting.- Section IV: Unsupervised Model and Text Mining.- Chapter 13: Cluster Analysis.- Chapter 14: Relationship Data Mining.- Chapter 15: Mining Text and Text Analytics.- Chapter 16: Big Data and Big Data Analytics.- Section V: Business Analytics Tools.- Chapter 17: R programming for Analytics.- Chapter 18: Python Programming for Analytics.

Feature

Explains the theory, tools, and techniques of business analytics with case studies that are easy to understand It covers unsupervised learning techniques, text mining, and natural language processing Explains regression, classification, time series, and optimization problems using both R and Python

Details ISBN1484287533 Author Umesha Nayak Short Title Business Analytics Using R and Python Language English Edition 2nd ISBN-10 1484287533 ISBN-13 9781484287538 Format Paperback Publisher APress Imprint APress Place of Publication Berkley Country of Publication United States Subtitle Solve Business Problems Using a Data-driven Approach Year 2023 Illustrations 372 Illustrations, color; 180 Illustrations, black and white; XXV, 706 p. 552 illus., 372 illus. in color. Publication Date 2023-04-04 AU Release Date 2023-04-04 NZ Release Date 2023-04-04 US Release Date 2023-04-04 UK Release Date 2023-04-04 Pages 706 Edition Description 2nd ed. Replaces 9781484225134 Alternative 9781484299401 DEWEY 005.133 Audience Professional & Vocational We've got this

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