Business Analytics with Python

Learn how to use Python programming techniques to analyze business data with this introductory textbook 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 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.

Online resources: lecturer PowerPoint slides, study guides, sample code and datasets and interactive worksheets

Auteur
Gerhard Kling is a Professor in Finance at the University of Aberdeen. He has worked in higher education for over 18 years (SOAS, University of Southampton, UWE, Utrecht University). His current interests focus on machine learning (ML), artificial intelligence (AI), and their applications in FinTech and Green Finance.

Contenu

• Section - ONE: Introduction and Preliminaries;
• • Chapter - 01: Introduction;
  • Chapter - 02: Getting started with Python;
  • Chapter - 03: Data wrangling;
  • Chapter - 04: Review of mathematics;
  • Chapter - 05: Data visualisation with Python;
• Section - TWO: Methods and Techniques
• • Chapter - 06: Linear Regression;
  • Chapter - 07: Logistic Regression;
  • Chapter - 08: Neural Networks;
  • Chapter - 09: K-Nearest Neighbours;
  • Chapter - 10: Naive Bayes;
  • Chapter - 11: Tree-based Methods;
  • Chapter - 12: Kernel Machines;
  • Chapter - 13: Principal Component Analysis;
  • Chapter - 14: Cluster Analysis;
• Section - THREE: Applications and Tools;
• • Chapter - 15: Business Analytics Case Studies;
  • Chapter - 16: Machine Learning Web Tools