Product details:
| ISBN13: | 9781398617179 |
| ISBN10: | 1398617172 |
| Binding: | Paperback |
| No. of pages: | 440 pages |
| Size: | 240x170 mm |
| Weight: | 666 g |
| Language: | English |
| 700 |
Category:
Business Analytics with Python
Essential Skills for Business Students
Edition number: 1
Publisher: Kogan Page
Date of Publication: 3 March 2025
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Short description:
Learn how to use Python programming techniques to analyze business data with this introductory textbook for business students.
Long 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.
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.
