Introduction to R for business intelligence : learn how to leverage the power of R for business intelligence

Key Features

• Use this easy-to-follow guide to leverage the power of R analytics and make your business data more insightful.
• This highly practical guide teaches you how to develop dashboards that help you make informed decisions using R.
• Learn the A to Z of working with data for Business Intelligence with the help of this comprehensive guide.

Book Description

Explore the world of Business Intelligence through the eyes of an analyst working in a successful and growing company. Learn R through use cases supporting different functions within that company. This book provides data-driven and analytically focused approaches to help you answer questions in operations, marketing, and finance.

In Part 1, you will learn about extracting data from different sources, cleaning that data, and exploring its structure. In Part 2, you will explore predictive models and cluster analysis for Business Intelligence and analyze financial times series. Finally, in Part 3, you will learn to communicate results with sharp visualizations and interactive, web-based dashboards.

After completing the use cases, you will be able to work with business data in the R programming environment and realize how data science helps make informed decisions and develops business strategy. Along the way, you will find helpful tips about R and Business Intelligence.

What you will learn

• Extract, clean, and transform data
• Validate the quality of the data and variables in datasets
• Learn exploratory data analysis
• Build regression models
• Implement popular data-mining algorithms
• Visualize results using popular graphs
• Publish the results as a dashboard through Interactive Web Application frameworks

About the Author

Jay Gendron is an associate data scientist working with Booz Allen Hamilton. He has worked in the fields of machine learning, data analysis, and statistics for over a decade, and believes that good questions and compelling visualization make analytics accessible to decision makers. Jay is a business leader, entrepreneurial employee, artist, and author. He has a B.S.M.E. in mechanical engineering, an M.S. in management of technology, an M.S. in operations research, and graduate certificates for chief information officer and IT program management.

Jay is a lifelong learner—a member of the first cohort to earn the 10-course specialization in data science by Johns Hopkins University on Coursera. He is an award-winning speaker who has presented internationally and provides pro bono data science expertise to numerous not-for-profit organizations to improve their operational insights. Connect with Jay Gendron at https://www.linkedin.com/in/jaygendron, visit http://jgendron.github.io/, or Twitter @jaygendron.

Table of Contents

1. Extract, Transform, and Load
2. Data Cleaning
3. Exploratory Data Analysis
4. Linear Regression for Business
5. Data Mining with Cluster Analysis
6. Time Series Analysis
7. Visualizing the Data's Story
8. Web Dashboards with Shiny
9. References
10. Other Helpful R Functions
11. R Packages Used in the Book
12. R Code for Supporting Market Segment Business Case Calculations