Course Content

1. R programming Concept

• An Introduction to R
• Introduction to the R language
• Programming statistical graphics
• Programming with R
• Simulation
• Computational linear algebra
• Numerical optimization

2. More Advanced Concepts Of R Programming

• Vectors
• Matrices
• Factors
• Data Frames
• Lists
• R Graphics
• R built in functions and R packages.

3. Machine Learning using R

• Naive Bayes
• Recommender
• K-means
• time series

4. Various case study using R

5. Data Manipulation Techniques

• Data in R
• Reading and Writing Data
• R and Databases
• Dates
• Factors
• Subscripting
• Character Manipulation
• Data Aggregation
• Reshaping Data

6. Statistical Applications using R programming

• Basics
• The R Environment
• Probability and distributions
• Descriptive statistics and graphics
• One- and two-sample tests
• Regression and correlation
• Analysis of variance and the Kruskal–Wallis test
• Tabular data
• Power and the computation of sample size
• Advanced data handling
• Multiple Regression
• Linear models
• Logistic regression
• Survival analysis
• Rates and Poisson regression
• Nonlinear curve fitting

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