Description
Book SynopsisBusiness 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.
Table of ContentsChapter 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
