The objective of this course is to be able to use R for the purpose of quantitative analysis for decision making. Hence the first two units would introduce us to important commands of R and units 3, 4 and 5 would focus on the use of R in linear and non-linear analysis.
Unit 1 breaks the inertia by helping us to learn R and its environment. We start with Installing R, the R Studio and R packages. We also learn the basics of R, its data structures and the means of reading data into R. We will also plot a few graphs using R in the last module of this unit.
In Unit 2 we begin writing functions in R, followed by control statements such as the “if and else”, switch, if else and a few compound tests. We gain momentum with writing loops in R (for, while, and controlling loops). We then try out a few applications using the functions and loops.
As mentioned earlier we begin our data manipulation and analysis from Unit 3 with group manipulation and data reshaping. A little bit of manipulating strings is also done. Then begins the tryst with basic statistics using the R Studio (Summaries, Correlation, t-tests and ANOVA)
We then move on to working with the linear predictive models in Unit 4 by performing the simple and multiple regression models using R, the GLM, followed by the logit regression, model diagnostics based on residuals and we also work a little on, cross validations and bootstrapping.
We hit the summit in Unit 5 with the non-linear models using Non-Linear least square, Splines, Generalized Additive Models, Decision trees and Random forests. We also work on some Time Series models with Autoregressive moving averages, VAR and GARCH. Clustering algorithms such as the K means, PAM and Hierarchical Clustering are worked on before we close this course.
This course on R programming is a judicious balance of knowledge and skill which calls for as much practice as possible along-with theoretical learning.
