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Foundations of Programming, Statistics, and Machine Learning for Business Analytics
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Foundations of Programming, Statistics, and Machine Learning for Business Analytics

  • Ram Gopal - University of Warwick, Warwick Business School, UK
  • Dan Philps - University of Warwick, Warwick Business School, UK
  • Tillman Weyde - City, University of London, UK
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April 2023 | 336 pages | SAGE Publications Ltd

Business Analysts and Data Scientists are in huge demand, as global companies seek to digitally transform themselves and leverage their data resources to realize competitive advantage.

This book covers all the fundamentals, from statistics to programming to business applications, to equip you with the solid foundational knowledge needed to progress in business analytics. 

Assuming no prior knowledge of programming or statistics, this book takes a simple step-by-step approach which makes potentially intimidating topics easy to understand, by keeping Maths to a minimum and including examples of business analytics in practice.

Key features:

·       Introduces programming fundamentals using R and Python

·       Covers data structures, data management and manipulation and data visualization

·       Includes interactive coding notebooks so that you can build up your programming skills progressively

Suitable as an essential text for undergraduate and postgraduate students studying Business Analytics or as pre-reading for students studying Data Science.

Ram Gopal is Pro-Dean and Professor of Information Systems at the University of Warwick.

Daniel Philps is an Artificial Intelligence Researcher and Head of Rothko Investment Strategies.

Tillman Weyde is Senior Lecturer at City, University of London.

 
Chapter 1: Introduction To Programming And Statistics
 
Chapter 2: Summarizing And Visualizing Data
 
Chapter 3: Summarizing And Visualizing Data
 
Chapter 4: Programming Fundamentals
 
Chapter 5: Programming Fundamentals
 
Chapter 6: Distributions
 
Chapter 7: Statistical Testing – Concepts and Strategy
 
Chapter 8: Statistical Testing – Concepts and Strategy
 
Chapter 9: Nonparametric Tests
 
Chapter 10: Reality Check
 
Chapter 11: Fundamentals of Estimation
 
Chapter 12: Linear Models
 
Chapter 13: General Linear Models
 
Chapter 14: Regression Diagnostics And Structure
 
Chapter 15: Timeseries And Forecasting
 
Chapter 16: Introduction To Machine Learning
 
Chapter 17: Model Selection And Cross Validation
 
Chapter 18: Regression Models In Machine Learning
 
Chapter 19: Classification Models And Evaluation
 
Chapter 20: Automated Machine Learning

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Paperback
ISBN: 9781529620917
£44.99

Hardcover
ISBN: 9781529620900
£130.00