Business Analytics with Python
Kogan Page Ltd (Verlag)
978-1-3986-1717-9 (ISBN)
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.
Bowei Chen is an Associate Professor in Marketing Analytics and Data Science at the Adam Smith Business School, University of Glasgow, UK. He is the Programme Director of the MSc in Business Analytics. Gerhard Kling is Professor in Finance at the University of Aberdeen, UK. He has worked in higher education (SOAS, University of Southampton, UWE, Utrecht University) and consulting (McKinsey).
Section - ONE: Introduction and preliminaries;
Chapter - 01: Introduction;
Chapter - 02: Mathematical foundations of business analytics;
Chapter - 03: Getting started with python;
Chapter - 04: Data wrangling;
Chapter - 05: Data visualization;
Section - TWO: Methods and techniques;
Chapter - 06: Linear regression;
Chapter - 07: Logistic regression;
Chapter - 08: Neural networks;
Chapter - 09: K-nearest neighbours;
Chapter - 10: Naïve bayes;
Chapter - 11: Tree-based methods;
Chapter - 12: Support vector machines;
Chapter - 13: Principal component analysis;
Chapter - 14: Cluster analysis;
Section - THREE: Applications and tools;
Chapter - 15: Modelling supply chains – use cases;
Chapter - 16: User interfaces and web applications;
Chapter - 17: Answers to exercises;
| Erscheinungsdatum | 27.02.2025 |
|---|---|
| Verlagsort | London |
| Sprache | englisch |
| Maße | 170 x 240 mm |
| Gewicht | 694 g |
| Themenwelt | Schulbuch / Wörterbuch ► Lexikon / Chroniken |
| Informatik ► Datenbanken ► Data Warehouse / Data Mining | |
| Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik | |
| ISBN-10 | 1-3986-1717-2 / 1398617172 |
| ISBN-13 | 978-1-3986-1717-9 / 9781398617179 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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