Business Analytics with Python (Paperback)

Bowei Chen

ISBN 10: 1398617172 / ISBN 13: 9781398617179

Published by Kogan Page Ltd, London, 2025

About this Item

Paperback. Data-driven decision-making is a fundamental component of business success. Use this textbook to help you learn and understand the core knowledge and techniques needed for analysing business data with Python programming. Business Analytics with Python is ideal for students taking upper level undergraduate and postgraduate modules on analytics as part of their business, management or finance degrees. It assumes no prior knowledge or experience in computer science, instead presenting the technical aspects of the subject in an accessible, introductory way for students. This book takes a holistic approach to business analytics, covering not only Python as well as mathematical and statistical concepts, essential machine learning methods and their applications. Features include: - Chapters covering preliminaries, as well as supervised and unsupervised machine learning techniques - A running case study to help students apply their knowledge in practice. - Real-life examples demonstrating the use of business analytics for tasks such as customer churn prediction, credit card fraud detection, and sales forecasting. - Practical exercises and activities, learning objectives, and chapter summaries to support learning. Learn how to use Python programming techniques to analyze business data with this introductory textbook for business students. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9781398617179

Bibliographic Details

• Title: Business Analytics with Python (Paperback)
• Publisher: Kogan Page Ltd, London
• Publication Date: 2025
• Language: English
• ISBN 10: 1398617172
• ISBN 13: 9781398617179
• Binding: Paperback
• Condition: new

About this title

Synopsis

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.

About the Authors

Bowei Chen is an Associate Professor of Marketing Analytics and Data Science at the Adam Smith Business School, University of Glasgow. He is also the Programme Director of the MSc in Finance and Management and an ESRC IAA Reviewer.

Gerhard Kling is a Professor in Finance at the University of Aberdeen. He has worked in higher education for over 18 years (SOAS, University of Southampton, UWE, Utrecht University). His current interests focus on machine learning (ML), artificial intelligence (AI), and their applications in FinTech and Green Finance.