Chen / Kling Business Analytics with Python

Essential Skills for Business Students
1. Auflage 2025
ISBN: 978-1-3986-1727-8
Verlag: Kogan Page
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 408 Seiten, Web PDF

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.

Autoren/Hrsg.

Chen, Bowei

Kling, Gerhard

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Wirtschaftswissenschaften Betriebswirtschaft Management Wissensmanagement
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie

Inhaltsverzeichnis

Section - ONE: Introduction and preliminaries; Chapter - 01: Introduction; Chapter - 02: Mathematical foundations of business analytics; Chapter - 03: Getting started with python; Chapter - 04: Data wrangling; Chapter - 05: Data visualization; Section - TWO: Methods and techniques; Chapter - 06: Linear regression; Chapter - 07: Logistic regression; Chapter - 08: Neural networks; Chapter - 09: K-nearest neighbours; Chapter - 10: Naïve bayes; Chapter - 11: Tree-based methods; Chapter - 12: Support vector machines; Chapter - 13: Principal component analysis; Chapter - 14: Cluster analysis; Section - THREE: Applications and tools; Chapter - 15: Modelling supply chains - use cases; Chapter - 16: User interfaces and web applications; Chapter - 17: Answers to exercises;

Über Autor(innen)

Kling, Gerhard
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.

Chen, Bowei
Bowei Chen is an Associate Professor of Marketing Analytics and Data Science at the Adam Smith Business School, University of Glasgow. He is also the Programme Director of the MSc in Finance and Management and an ESRC IAA Reviewer.

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).