Business Analytics With Python Essential Skills For Business Students True PDF (Chen, Bowei;Kling, Gerhard;)

[Farid-Khan]

2025 | English | 1398617288 | True PDF | 408 pages | 33.60 MB 9781398617278

Catergory: Nonfiction, Computers, Database Management, Programming, Programming Languages, Reference & Language, Reference

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.

Contents of Download:

? Business Analytics With Python.pdf (Chen, Bowei;Kling, Gerhard (2025) (33.6 MB)

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