Introduction to Data Analytics for Business and Economics
Using Python for Data Preprocessing, Visualization, and Modeling
Preface
This book stems from years of experience teaching introductory and advanced data analytics courses across different institutions. There is no shortage of excellent books in this space: theoretical handbooks like An Introduction to Statistical Learning build the statistical foundations rigorously, and applied books like Hands-On Machine Learning cover the modeling toolkit in depth. What none of them focus on is what business and economics students actually need most: the tools and methods to turn a messy, real-world business problem into a proper data analytics project. That is what this book tries to do differently. There is no lengthy mathematical derivation here, but plenty of hands-on code, and the practical tips and tricks that make the difference between a model that works on a slide and one that survives contact with a real business problem.
Who this book is for
This book offers a gentle introduction to Python, so no prior programming experience is required to start. That said, a basic introductory programming or Python course would benefit the reader, since the programming covered here is aimed squarely at what you need for data analytics, not the general fundamentals of programming as such. A basic statistics course is similarly useful, since not every statistical concept is re-derived from first principles. In practice, this makes the book a good fit for undergraduate or Bachelor-level teaching once students have already had some exposure to programming and statistics, even a light one. It is also the primary teaching material for the Data Analytics course at the University of Namur (UNamur), though it is written to be useful to any reader with the right background, regardless of institution.
What this book covers
The book walks through a full data analytics pipeline, from raw, messy data to a working, evaluated predictive model: Python fundamentals, data understanding, data exploration, data preparation, model evaluation, and modeling. Each chapter pairs a concept with runnable code, so the code is never a black box you’re asked to accept on faith.
How to read it
Chapters build on each other and are meant to be read in order. Each one comes with a companion Jupyter notebook so you can run the code yourself, not just read it. These notebooks aren’t published on this website; they live in the notebooks_student/ folder of the book’s GitHub repository, linked from the sidebar on every page of this site. If you want to run the code locally, the Installation Guide appendix walks through setting up everything you need.
A note on generative AI
Parts of this book’s text, code examples, and figures were drafted, polished, or generated with the help of a generative AI assistant, always under the direct guidance of, and with final review by, the author. Every explanation, dataset, line of code, and figure has been checked for accuracy before publication. Chapter 1 discusses this shift in writing and coding practice in a bit more detail.
License
This book is licensed under the CC BY-NC-SA 4.0 license.
