
Business Analytics with Python
Essential Skills for Business Students
- English
- ePUB (mobile friendly)
- Available on iOS & Android
About this book
Your essential textbook for mastering business analytics through Python.
Business Analytics with Python by Bowei Chen and Gerhard Kling is the definitive guide for upper-level undergraduate and postgraduate students studying business, management or finance. Designed to support analytics modules that prioritize practical application, this textbook introduces students to data-driven decision-making through Python, without assuming a background in computer science. It aligns with course outcomes by integrating statistical, mathematical and machine learning techniques into a unified business context.
This textbook takes a holistic approach to business analytics, exploring how Python can be used to interpret and solve real-world problems. From foundational coding skills to the implementation of supervised and unsupervised machine learning methods, students learn how to translate data into insight across key business functions. Through industry-relevant case studies, including customer churn analysis, fraud detection and sales forecasting, learners build confidence in applying analytics to real organizational challenges.
Pedagogical features include:
- A running case study that reinforces practical learning across chapters
- Clear learning objectives and chapter summaries to track progress
- Step-by-step exercises and coding activities to build analytical fluency
- Examples grounded in real business applications for immediate relevance
Whether preparing for exams or building analytical capability for a future career, this textbook equips students with the tools to turn business data into strategic advantage.
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Information
Table of contents
- About the Authors
- Preface
- Acknowledgements
- Walkthrough of Features and Online Resources
- PART ONE Introduction and Preliminaries
- 1 Introduction
- 2 Mathematical Foundations of Business Analytics
- 3 Getting Started with Python
- 4 Data Wrangling
- 5 Data Visualization
- PART TWO Methods and Techniques
- 6 Linear Regression
- 7 Logistic Regression
- 8 Neural Networks
- 9 K-Nearest Neighbours
- 10 Naïve Bayes
- 11 Tree-Based Methods
- 12 Support Vector Machines
- 13 Principal Component Analysis
- 14 Cluster Analysis
- PART THREE Applications and Tools
- 15 Modelling Supply Chains: Use Cases
- 16 User Interfaces and Web Applications
- Answers to Exercises
- Bibliography
- Index