What do you think?
Rate this book
Business Analytics with Python: Essential Skills for Business Students
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 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.
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 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.
408 pages, Paperback
Published March 25, 2025
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 of 1 review
February 9, 2026
Thanks to Kogan Page Ltd and Netgalley for this eARC.
Chen and Kling’s Business Analytics with Python is that rare textbook‑style guide that manages to be technically grounded and genuinely readable. Rather than overwhelming students with abstract theory or drowning them in code snippets, the authors build a bridge between business reasoning and computational practice, making the case that analytics isn’t just a technical skill—it’s a way of thinking.
This book’s greatest strength is its pedagogical clarity. Chen and Kling understand that many business students approach Python with a mix of curiosity and apprehension, so they structure the material to demystify rather than intimidate. Concepts like data wrangling, visualization, regression, and machine learning are introduced with a steady hand, each chapter layering complexity without losing sight of the “why” behind the “how.” The authors consistently tie techniques back to real business questions—forecasting demand, segmenting customers, evaluating risk—so the learning feels purposeful rather than procedural.
What sets this book apart from other analytics primers is its emphasis on interpretation. The authors don’t treat models as black boxes or Python as a magic wand. Instead, they foreground the importance of assumptions, data quality, and the messy realities of organizational decision-making. This makes the text especially valuable for students who will eventually need to explain their analyses to non‑technical stakeholders. The tone is pragmatic, not evangelistic: analytics is powerful, but only when paired with judgment.
The writing is clean, direct, and refreshingly free of jargon. Examples are well‑chosen and never feel like filler. The progression from foundational Python skills to more advanced analytics mirrors the way students actually learn—incrementally, with plenty of room to revisit earlier concepts. Even the coding exercises feel intentional, reinforcing not just syntax but analytical reasoning.
Where this book excels:
- Bridging disciplines: It speaks fluently to both business logic and computational thinking.
- Real-world grounding: Case studies and examples reflect the ambiguity of real data rather than sanitized textbook scenarios.
- Ethical awareness: The authors acknowledge the risks of misinterpretation, bias, and overconfidence—topics too often glossed over in analytics texts.
Business Analytics with Python is an excellent companion for business students who want to build analytical fluency without getting lost in technical weeds. It’s practical, thoughtful, and structured to build confidence. More importantly, it teaches students to think like analysts, not just code like them.
Chen and Kling’s Business Analytics with Python is that rare textbook‑style guide that manages to be technically grounded and genuinely readable. Rather than overwhelming students with abstract theory or drowning them in code snippets, the authors build a bridge between business reasoning and computational practice, making the case that analytics isn’t just a technical skill—it’s a way of thinking.
This book’s greatest strength is its pedagogical clarity. Chen and Kling understand that many business students approach Python with a mix of curiosity and apprehension, so they structure the material to demystify rather than intimidate. Concepts like data wrangling, visualization, regression, and machine learning are introduced with a steady hand, each chapter layering complexity without losing sight of the “why” behind the “how.” The authors consistently tie techniques back to real business questions—forecasting demand, segmenting customers, evaluating risk—so the learning feels purposeful rather than procedural.
What sets this book apart from other analytics primers is its emphasis on interpretation. The authors don’t treat models as black boxes or Python as a magic wand. Instead, they foreground the importance of assumptions, data quality, and the messy realities of organizational decision-making. This makes the text especially valuable for students who will eventually need to explain their analyses to non‑technical stakeholders. The tone is pragmatic, not evangelistic: analytics is powerful, but only when paired with judgment.
The writing is clean, direct, and refreshingly free of jargon. Examples are well‑chosen and never feel like filler. The progression from foundational Python skills to more advanced analytics mirrors the way students actually learn—incrementally, with plenty of room to revisit earlier concepts. Even the coding exercises feel intentional, reinforcing not just syntax but analytical reasoning.
Where this book excels:
- Bridging disciplines: It speaks fluently to both business logic and computational thinking.
- Real-world grounding: Case studies and examples reflect the ambiguity of real data rather than sanitized textbook scenarios.
- Ethical awareness: The authors acknowledge the risks of misinterpretation, bias, and overconfidence—topics too often glossed over in analytics texts.
Business Analytics with Python is an excellent companion for business students who want to build analytical fluency without getting lost in technical weeds. It’s practical, thoughtful, and structured to build confidence. More importantly, it teaches students to think like analysts, not just code like them.
Displaying 1 of 1 review