
Business Analytics with Python
Essential Skills for Business Students
Kogan Page Ltd (Publisher)
1st Edition
Published on 3. March 2025
Book
Paperback/Softback
440 pages
978-1-3986-1717-9 (ISBN)
Description
Data-driven decision-making is a fundamental component of business success. Use this textbook to learn the core knowledge and techniques for analyzing business data with Python programming.
Business Analytics with Python
assumes no prior knowledge or experience in computer science, presenting the technical aspects of the subject in an accessible, introductory way for students on business courses. It features chapters on linear regression, neural networks and cluster analysis, with a running case study that enables students to apply their knowledge. Students will also benefit from real-life examples to show how business analysis has been used for such tasks as customer churn prediction, credit card fraud detection and sales forecasting.
This book presents a holistic approach to business analytics: in addition to Python, it covers mathematical and statistical concepts, essential machine learning methods and their applications.
Business Analytics with Python
comes complete with practical exercises and activities, learning objectives and chapter summaries as well as self-test quizzes. It is supported by online resources that include lecturer PowerPoint slides, study guides, sample code and datasets and interactive worksheets.
This textbook is ideal for students taking upper level undergraduate and postgraduate modules on analytics as part of their business, management or finance degrees.
Reviews / Votes
"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 * "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, Founder and CEO, 365 Data Science * "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, Associate Professor in Operations Management, Trinity Business School, Trinity College Dublin * "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, Professor of Information & Operations Management, ESCP Business School * "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, Associate Professor of Fintech, Southwestern University of Finance and Economics, China, and CEO of Inboc Technologies *More details
Language
English
Place of publication
London
United Kingdom
Product notice
Paperback (trade)
Dimensions
Height: 244 mm
Width: 170 mm
Thickness: 22 mm
Weight
705 gr
ISBN-13
978-1-3986-1717-9 (9781398617179)
Schweitzer Classification
Other editions
Additional editions

Book
03/2025
1st Edition
Kogan Page Ltd
€193.20
Shipment within 10-20 days
Persons
Author
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.
Content
- Section - ONE: Introduction and Preliminaries;
-
- Chapter - 01: Introduction;
- Chapter - 02: Getting started with Python;
- Chapter - 03: Data wrangling;
- Chapter - 04: Review of mathematics;
- Chapter - 05: Data visualisation with Python;
- Section - TWO: Methods and Techniques
-
- Chapter - 06: Linear Regression;
- Chapter - 07: Logistic Regression;
- Chapter - 08: Neural Networks;
- Chapter - 09: K-Nearest Neighbours;
- Chapter - 10: Naive Bayes;
- Chapter - 11: Tree-based Methods;
- Chapter - 12: Kernel Machines;
- Chapter - 13: Principal Component Analysis;
- Chapter - 14: Cluster Analysis;
- Section - THREE: Applications and Tools;
-
- Chapter - 15: Business Analytics Case Studies;
- Chapter - 16: Machine Learning Web Tools