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Business Analytics with Python
Essential Skills for Business Students
Learn how to use Python programming techniques to analyze business data with this introductory textbook for business students.
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EAN: 9781398617179
Edition: 1
Published:
Format: 240 x 170
408 pages
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About the 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.
About the authors
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).
Table of contents
- 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;
Reviews
Professor Mairi Maclean, Professor of International Business, School of Management, University of Bath, UK
For business school students learning about business analytics, and business professionals seeking practical guidance concerning the skills and techniques that business analytics require, this comprehensive volume is quite simply a must-read. It goes beyond mere theory to illuminate the practical uses of business analytics applicable to real-world situations. This volume is thoughtful, intelligent, well-written and curated, yet readily accessible, providing clear explanations and pertinent insights, while ensuring that the mathematical content remains at a level that readers can understand. I have found it to be invaluable and illuminating, making business analytics intelligible to the interested reader who may not yet be familiar with all that business analytics has to offer.
Nedko Krastev, Founder and CEO, 365 Data Science
Business Analytics with Python stands out as a rare resource that successfully bridges the gap between theoretical concepts and real-world applications-something few tools manage to accomplish today. Its step-by-step approach and meticulously crafted examples ensure that readers don't just learn about business analytics in the abstract, but actually gain the skills to apply these methods and techniques in practice. The hands-on guidance throughout makes advanced data analysis accessible, even to those without a strong quantitative background. In an era where actionable analytics skills are increasingly essential, this book serves as both a solid educational foundation and a practical reference, empowering students and professionals alike to confidently solve complex business problems with Python.
Yufei Huang, Associate Professor in Operations Management, Trinity Business School, Trinity College Dublin
This book is a clear and practical blueprint for incorporating machine learning insights into business operational decisions. Drawing on my experience in operations management and business analytics, I appreciate how the authors seamlessly blend fundamental Python skills, advanced modelling techniques, and actionable business strategies. Their guidance empowers readers to streamline processes, improve efficiency, and translate predictive insights into tangible, real-world results. This is a great resource for anyone serious about leveraging machine learning to drive smarter, more impactful data-driven decisions.
Wei Zhou, Professor of Information & Operations Management, ESCP Business School
This is a must-read for professionals and students looking to harness the power of Python in solving real-world business challenges. The book masterfully bridges the gap between programming fundamentals and practical applications in business analytics, providing readers with step-by-step guidance and hands-on examples. With its clear explanations, accessible approach, and industry-relevant use cases, this book empowers readers to confidently use Python to derive insights, make data-driven decisions and drive strategic outcomes. Whether you're a beginner or looking to enhance your existing skills, this is an invaluable resource for staying competitive in today's data-driven world.
Yu Zheng, Associate Professor of Fintech, Southwestern University of Finance and Economics, China, and CEO of Inboc Technologies
In the FinTech industry, data fuels innovation and informed decision-making. This book equips students and professionals with the essential tools to analyze financial and business data, optimize operations, and uncover actionable insights through predictive analytics. It is an invaluable resource for those striving to excel in this fast-paced and highly competitive field.
- For business school students learning about business analytics, and business professionals seeking practical guidance concerning the skills and techniques that business analytics require, this comprehensive volume is quite simply a must-read. It goes beyond mere theory to illuminate the practical uses of business analytics applicable to real-world situations. This volume is thoughtful, intelligent, well-written and curated, yet readily accessible, providing clear explanations and pertinent insights, while ensuring that the mathematical content remains at a level that readers can understand. I have found it to be invaluable and illuminating, making business analytics intelligible to the interested reader who may not yet be familiar with all that business analytics has to offer.
- Professor Mairi Maclean, Professor of International Business, School of Management, University of Bath, UK
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