Chapter 1: Introduction Chapter Goal: To understand what is R, why use R, statistics in data mining and data science No of pages 15 Sub -Topics 1. What is R? 2. High Level and Low Level Language 3. What is Statistics? 4. What is Data Science? 5. What is Data Mining? 6. What is Text Mining? 7. Three Types of Analytics 8. Big Data 9. Why R? 10. Conclusion
Chapter 2: Getting Started Chapter Goal: To set up the computer for R Programming No of pages: 15 Sub - Topics 1. What is R and RStudio? 2. Installation of R and RStudio 3. Integrated Development Environment 4. RStudio - The IDE for R. 5. Conclusion Chapter 3: Basic Syntax Chapter Goal: To learn R programming basics No of pages : 30 Sub - Topics: 1. Writing in R Console 2. Using Code Editor 3. Variables and Data Types 4. Vectors 5. Lists 6. Data Frame 7. Logical Statements 8. Loops 9. Functions 10. Conclusion
Chapter 4: Descriptive Statistics Chapter Goal: To learn Descriptive Statistics in R No of pages: 20 Sub - Topics: 1. Reading Data Files 2. Mean, Median, Min, Max, ... 3. Percentile, Standard Deviations 4. The Summary() and Str() functions 5. Distributions 6. Conclusion

Chapter 5: Data Visualizations Chapter Goal: To learn Data Visualizations in R No of pages: 20 Sub - Topics: 1. What is Data Visualizations? 2. Bar Chart, Histogram 3. Line Chart, Pie Chart 4. Scatterplot and Box Plot 5. Scatterplot Matrix 6. Decision Trees 7. Conclusion

Chapter 6: Inferential Statistics and Regressions Chapter Goal: To learn inferential statistics and regressions in R No of pages: 20 Sub - Topics: 1. Correlations 2. T Test, Chi Square, ANOVA 3. Non Parametric Test 4. Linear Regressions 5. Multiple Linear Regressions