Business Analytics with Python
eBook - ePub

Business Analytics with Python

Essential Skills for Business Students

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Business Analytics with Python

Essential Skills for Business Students

About this 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.

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Yes, you can access Business Analytics with Python by Bowei Chen,Gerhard Kling in PDF and/or ePUB format, as well as other popular books in Computer Science & Information Management. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. About the Authors
  2. Preface
  3. Acknowledgements
  4. Walkthrough of Features and Online Resources
  5. PART ONE Introduction and Preliminaries
  6. 1 Introduction
  7. 2 Mathematical Foundations of Business Analytics
  8. 3 Getting Started with Python
  9. 4 Data Wrangling
  10. 5 Data Visualization
  11. PART TWO Methods and Techniques
  12. 6 Linear Regression
  13. 7 Logistic Regression
  14. 8 Neural Networks
  15. 9 K-Nearest Neighbours
  16. 10 Naïve Bayes
  17. 11 Tree-Based Methods
  18. 12 Support Vector Machines
  19. 13 Principal Component Analysis
  20. 14 Cluster Analysis
  21. PART THREE Applications and Tools
  22. 15 Modelling Supply Chains: Use Cases
  23. 16 User Interfaces and Web Applications
  24. Answers to Exercises
  25. Bibliography
  26. Index