Sinossi
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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Informazioni sull?autore
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).
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Paperback. Condizione: New. 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. Codice articolo LU-9781398617179
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