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  • © 2019

Essentials of Business Analytics

An Introduction to the Methodology and its Applications

  • Offers a comprehensive introductory approach to business analytics that includes an emphasis on big data handling, applications in different verticals and case studies
  • Highlights big data handling, applications of analytics in different verticals, and real life case studies
  • Includes exercises for each chapter and downloadable use cases for students and professionals to practice and test the analytics tools
  • Request lecturer material: sn.pub/lecturer-material

Part of the book series: International Series in Operations Research & Management Science (ISOR, volume 264)

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Table of contents (30 chapters)

  1. Front Matter

    Pages i-xvi
  2. Introduction

    • Sridhar Seshadri
    Pages 1-15
  3. Tools

    1. Front Matter

      Pages 17-17
    2. Data Collection

      • Sudhir Voleti
      Pages 19-39
    3. Data Management—Relational Database Systems (RDBMS)

      • Hemanth Kumar Dasararaju, Peeyush Taori
      Pages 41-69
    4. Big Data Management

      • Peeyush Taori, Hemanth Kumar Dasararaju
      Pages 71-109
    5. Data Visualization

      • John F. Tripp
      Pages 111-135
    6. Statistical Methods: Basic Inferences

      • Vishnuprasad Nagadevara
      Pages 137-178
    7. Statistical Methods: Regression Analysis

      • Bhimasankaram Pochiraju, Hema Sri Sai Kollipara
      Pages 179-245
    8. Advanced Regression Analysis

      • Vishnuprasad Nagadevara
      Pages 247-281
    9. Text Analytics

      • Sudhir Voleti
      Pages 283-301
  4. Modeling Methods

    1. Front Matter

      Pages 303-303
    2. Simulation

      • Sumit Kunnumkal
      Pages 305-336
    3. Introduction to Optimization

      • Milind G. Sohoni
      Pages 337-380
    4. Forecasting Analytics

      • Konstantinos I. Nikolopoulos, Dimitrios D. Thomakos
      Pages 381-420
    5. Count Data Regression

      • Thriyambakam Krishnan
      Pages 421-438
    6. Survival Analysis

      • Thriyambakam Krishnan
      Pages 439-458
    7. Machine Learning (Unsupervised)

      • Shailesh Kumar
      Pages 459-505
    8. Machine Learning (Supervised)

      • Shailesh Kumar
      Pages 507-568
    9. Deep Learning

      • Manish Gupta
      Pages 569-595

About this book

This comprehensive edited volume is the first of its kind, designed to serve as a textbook for long-duration business analytics programs. It can also be used as a guide to the field by practitioners. The book has contributions from experts in top universities and industry. The editors have taken extreme care to ensure continuity across the chapters.

The material is organized into three parts: A) Tools, B) Models and C) Applications. In Part A, the tools used by business analysts are described in detail. In Part B, these tools are applied to construct models used to solve business problems. Part C contains detailed applications in various functional areas of business and several case studies. Supporting material can be found in the appendices that develop the pre-requisites for the main text.

Every chapter has a business orientation. Typically, each chapter begins with the description of business problems that are transformed into data questions; and methodology is developed to solve these questions. Data analysis is conducted using widely used software, the output and results are clearly explained at each stage of development. These are finally transformed into a business solution. The companion website provides examples, data sets and sample code for each chapter.


    





Editors and Affiliations

  • Applied Statistics and Computing Lab, Indian School of Business, Hyderabad, India

    Bhimasankaram Pochiraju

  • Gies College of Business, University of Illinois at Urbana Champaign, Champaign, USA

    Sridhar Seshadri

About the editors

Bhimasankaram Pochiraju obtained his Ph.D. in Statistics from the Indian Statistical Institute. He was the Clinical Professor of Statistics, Executive Director of Applied Statistics and Computing Lab and Faculty Director, Certificate Programme in Business Analytics at the Indian School of Business. He was formerly Professor of Statistics and Head, SQC & OR Division at the Indian Statistical Institute. He co-authored a text book and a research monograph in Linear Algebra. His areas of research interest include Analytics, Causal Inference and Linear Algebra. 

Sridhar Seshadri obtained his PhD at the University of California, Berkeley after graduating from the Indian Institute of Technology, Madras, India and the Indian Institute of Management, Ahmedabad, India. He is currently Professor and Area Leader of IS, Operations Management and Supply Chain Management areas at the Geis College of Business. He has also been a faculty member at The Indian School of Business, The University of Texas at Austin, New York University and the Administrative Staff College of India. During his teaching career, he was awarded the Stern School of Business Teaching Excellence Award (1998) and recognized as the Stern School of Business Undergraduate Teacher of the Year in 1997. His current research includes Analytics, Pricing and Revenue Optimization and Risk Management in supply chains.


  

Bibliographic Information

Buy it now

Buying options

eBook USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access