E-Book, Englisch, 408 Seiten, Web PDF
Chen / Kling Business Analytics with Python
1. Auflage 2025
ISBN: 978-1-3986-1727-8
Verlag: Kogan Page
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
Essential Skills for Business Students
E-Book, Englisch, 408 Seiten, Web PDF
ISBN: 978-1-3986-1727-8
Verlag: Kogan Page
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
Your essential textbook for mastering business analytics through Python. Business Analytics with Python by Bowei Chen and Gerhard Kling is the definitive guide for upper-level undergraduate and postgraduate students studying business, management or finance. Designed to support analytics modules that prioritize practical application, this textbook introduces students to data-driven decision-making through Python, without assuming a background in computer science. It aligns with course outcomes by integrating statistical, mathematical and machine learning techniques into a unified business context. This textbook takes a holistic approach to business analytics, exploring how Python can be used to interpret and solve real-world problems. From foundational coding skills to the implementation of supervised and unsupervised machine learning methods, students learn how to translate data into insight across key business functions. Through industry-relevant case studies, including customer churn analysis, fraud detection and sales forecasting, learners build confidence in applying analytics to real organizational challenges. Pedagogical features include: - A running case study that reinforces practical learning across chapters - Clear learning objectives and chapter summaries to track progress - Step-by-step exercises and coding activities to build analytical fluency - Examples grounded in real business applications for immediate relevance Whether preparing for exams or building analytical capability for a future career, this textbook equips students with the tools to turn business data into strategic advantage.
Autoren/Hrsg.
Fachgebiete
- Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
- Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
- Wirtschaftswissenschaften Betriebswirtschaft Management Wissensmanagement
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
Weitere Infos & Material
- 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;




