Business Analytics with Python: Essential Skills for Business Students

by Bowei Chen, Gerhard Kling

Overview

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.

Product Details

ISBN-13:	9781398617278
Publisher:	Kogan Page, Ltd.
Publication date:	03/03/2025
Sold by:	Barnes & Noble
Format:	eBook
Pages:	408
File size:	10 MB

About the Author

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).

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.

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;

Editorial Reviews

"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."

Nedko Krastev

"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

"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."

Yufei Huang

"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."

Wei Zhou

"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."

Yu Zheng