An Introduction to Business Analytics - Ger Koole

[Pages:78]An Introduction to Business Analytics

Copyright c 2019 Ger Koole All rights reserved

MG books, Amsterdam ISBN 978 90 820179 3 9 Cover design: Ingrid Brandenburg & Luciano Picozzi

An Introduction to Business Analytics Ger Koole

MG books Amsterdam

Preface

Books on Business Analytics (BA) typically fall into two categories: managerial books without any technical details, and very technical books, written for BA majors who already have a background in advanced mathematics or computer science. This book tries to fill the gap by discussing BA techniques at a level appropriate for readers with a less technical background. This makes it suitable for many different audiences, especially managers who want to better understand the work of their data scientists, or people who want to learn the basics of BA and do their first BA projects themselves.

The full range of BA-related topics is covered: from the many different techniques to an overview of managerial aspects; from comparisons of the usefulness of different techniques in different situations to their historical context. While working with this book, you will also learn appropriate tooling, especially R and a bit of Excel. There are exercises to sharpen your skills and test your understanding.

Because this book contains a large variety of topics, I sought advice from many experts. I am especially indebted to Sandjai Bhulai, Bram Gorissen, Jeroen van Kasteren, Diederik Roijers and Qingchen Wang for their feedback on scientific issues and Peggy Curley for editing.

Business Analytics is a young field in full development, which uses aspects from various fields of science. Although I tried to integrate the knowledge from many fields, it is unavoidable that the content will be biased based on my background and experience. Please do not hesitate to send me an email if you have any ideas or comments to share. I sincerely hope that reading this book is a rewarding experience.

All chapters can be read independently, but I advise to read Chapter 1 first to understand the connections between the chapters. The index at the end can be helpful for unknown terms and abbreviations.

Ger Koole

Amsterdam/Peymeinade, 2016?2019

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Contents

Preface

i

Contents

v

1 Introduction

1

1.1 What is business analytics? . . . . . . . . . . . . . . . . . . . . 1

1.2 Historical overview . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.3 Non-technical overview . . . . . . . . . . . . . . . . . . . . . . 7

1.4 Tooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.5 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

1.6 Additional reading . . . . . . . . . . . . . . . . . . . . . . . . . 14

2 Going on a Tour with R

17

2.1 Getting started . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.2 Learning R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.3 Libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.4 Data structures . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.5 Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.6 Simulation and hypothesis testing . . . . . . . . . . . . . . . . 22

2.7 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.8 Regression and deep learning . . . . . . . . . . . . . . . . . . . 25

2.9 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.10 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.11 Additional reading . . . . . . . . . . . . . . . . . . . . . . . . . 29

3 Variability

31

3.1 Summarizing data . . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.2 Probability theory and the binomial distribution . . . . . . . . 34

3.3 Other distributions and the central limit theorem . . . . . . . . 41

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3.4 Parameter estimation . . . . . . . . . . . . . . . . . . . . . . . . 49 3.5 Additional reading . . . . . . . . . . . . . . . . . . . . . . . . . 53

4 Machine Learning

55

4.1 Data preparation . . . . . . . . . . . . . . . . . . . . . . . . . . 56

4.2 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

4.3 Linear regression . . . . . . . . . . . . . . . . . . . . . . . . . . 59

4.4 Nonlinear prediction . . . . . . . . . . . . . . . . . . . . . . . . 64

4.5 Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

4.6 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

4.7 Additional reading . . . . . . . . . . . . . . . . . . . . . . . . . 76

5 Simulation

77

5.1 Monte Carlo simulation . . . . . . . . . . . . . . . . . . . . . . 77

5.2 Discrete-event simulation . . . . . . . . . . . . . . . . . . . . . 80

5.3 Additional reading . . . . . . . . . . . . . . . . . . . . . . . . . 84

6 Linear Optimization

85

6.1 Problem formulation . . . . . . . . . . . . . . . . . . . . . . . . 85

6.2 LO in Excel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

6.3 Example LO problems . . . . . . . . . . . . . . . . . . . . . . . 92

6.4 Integer problems . . . . . . . . . . . . . . . . . . . . . . . . . . 95

6.5 Example ILO problems . . . . . . . . . . . . . . . . . . . . . . . 98

6.6 Modeling tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

6.7 Modeling tricks . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

6.8 Additional reading . . . . . . . . . . . . . . . . . . . . . . . . . 106

7 Combinatorial Optimization

107

7.1 The shortest path problem . . . . . . . . . . . . . . . . . . . . . 107

7.2 The maximum flow problem . . . . . . . . . . . . . . . . . . . 111

7.3 The traveling salesman problem . . . . . . . . . . . . . . . . . 112

7.4 Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

7.5 Additional reading . . . . . . . . . . . . . . . . . . . . . . . . . 116

8 Simulation Optimization

117

8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

8.2 Comparing scenarios . . . . . . . . . . . . . . . . . . . . . . . . 119

8.3 Ranking and selection . . . . . . . . . . . . . . . . . . . . . . . 119

8.4 Local search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

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