This book is ideal for those who are well-versed in writing code and have a basic understanding of statistics, but have limited experience in implementing predictive models and machine learning techniques for analyzing real world data. The most challenging part of solving industrial business problems is the practical and hands-on knowledge of building and deploying advanced predictive models and machine learning algorithms.
Applied Analytics through Case Studies Using SAS and R is your answer to solving these business problems by sharpening your analytical skills.
What You'll Learn
- Understand analytics and basic data concepts
- Use an analytical approach to solve Industrial business problems
- Build predictive model with machine learning techniques
- Create and apply analytical strategies
Who This Book Is For
Data scientists, developers, statisticians, engineers, and research students with a great theoretical understanding of data and statistics who would like to enhance their skills by getting practical exposure in data modeling.
|Edition description:||1st ed.|
|Product dimensions:||7.00(w) x 9.90(h) x 0.80(d)|
About the Author
Table of Contents
Applied Analytics through Case Studies Using SAS and R
Chapter 1: Role of Analytics in Various Industries
Chapter 2: Banking Case Study with Analytical Solutions
Chapter 3: Retail Case Study with Analytical Solutions
Chapter 4: Telecommunication Case Study with Analytical Solutions
Chapter 5: Healthcare Case Study with Analytical Solutions
Chapter 6: Airline Case Study with Analytical Solutions
Chapter 7: FMCG Case Study with Analytical Solutions