Foundations of Programming, Statistics, and Machine Learning for Business Analytics
eBook - ePub

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

  1. 512 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

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

About this book

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.

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Yes, you can access Foundations of Programming, Statistics, and Machine Learning for Business Analytics by Ram Gopal,Dan Philps,Tillman Weyde in PDF and/or ePUB format, as well as other popular books in Commerce & Commerce Général. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Cover
  2. Half Title
  3. Publisher Note
  4. Title Page
  5. Copyright Page
  6. Contents
  7. About the Authors
  8. Acknowledgements
  9. Online Resources
  10. About this book
  11. 1 Introduction to Programming and Statistics
  12. 2 Summarizing and Visualizing Data
  13. 3 Managing and Preparing Data
  14. 4 Programming Fundamentals
  15. 5 Random Variables, Probability, and Distributions
  16. 6 Distributions
  17. 7 Statistical Testing –Concepts and Strategy
  18. 8 Statistical Tests
  19. 9 Nonparametric Tests
  20. 10 Reality Check
  21. 11 Fundamentals of Estimation
  22. 12 Estimation of Linear Models
  23. 13 General Linear Models
  24. 14 Regression Diagnostics and Structure
  25. 15 Timeseries and Forecasting
  26. 16 Introduction to Machine Learning
  27. 17 Model Selection and Cross-validation
  28. 18 Regression Models in Machine Learning
  29. 19 Classification Models and Evaluation
  30. 20 Automated Machine Learning
  31. Index