
Foundations of Programming, Statistics, and Machine Learning for Business Analytics
- 512 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
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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Information
Table of contents
- Cover
- Half Title
- Publisher Note
- Title Page
- Copyright Page
- Contents
- About the Authors
- Acknowledgements
- Online Resources
- About this book
- 1 Introduction to Programming and Statistics
- 2 Summarizing and Visualizing Data
- 3 Managing and Preparing Data
- 4 Programming Fundamentals
- 5 Random Variables, Probability, and Distributions
- 6 Distributions
- 7 Statistical Testing –Concepts and Strategy
- 8 Statistical Tests
- 9 Nonparametric Tests
- 10 Reality Check
- 11 Fundamentals of Estimation
- 12 Estimation of Linear Models
- 13 General Linear Models
- 14 Regression Diagnostics and Structure
- 15 Timeseries and Forecasting
- 16 Introduction to Machine Learning
- 17 Model Selection and Cross-validation
- 18 Regression Models in Machine Learning
- 19 Classification Models and Evaluation
- 20 Automated Machine Learning
- Index