- Assumes no prior knowledge and presents an accessible approach to business analytics, including Python programming and mathematical and statistical concepts
- Includes chapters on linear regression, neural networks and cluster analysis, and covers essential machine learning methods and their applications
- Features a running case study throughout the book with related questions to enable students to apply their knowledge of the key topics
- Is supported by learning features such as practical exercises and activities, learning objectives and chapter summaries as well as self-test quizzes and real-world examples
- Online resources: lecturer PowerPoint slides, study guides and datasets
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.
Key features at a glance
Temporarily Unavailable
EAN: 9781398617179
Edition: 1
Published:
Format: 240x170
408 pages
About the book
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.
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).
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