Kogan Page Ltd Paperback English
Business Analytics with Python
Essential Skills for Business Students
By Bowei Chen
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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.
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.