R Programming (Statistical Analysis)
Course Content:
Probability & Statistics
- Introduction to Statistics – Descriptive Statistics, Summary Statistics Basic probability theory
- Statistical Concepts ( uni-variate and bi-variate sampling, distributions, re-sampling, statistical Inference, prediction error)
- Probability Distribution (Continuous and discrete-Normal, Bernoulli, Binomial, Negative Binomial, Geometric and Poisson distribution)
- Bayes’ Theorem
- Central Limit Theorem
- Data Exploration & preparation Concepts of Correlation
- Regression
- Covariance
- Outliers etc.
R Programming
- Introduction & Installation of R
- R Basics
- Finding Help
- Code Editors for R
- Command Packages
- Manipulating and Processing Data in R
- Reading and Getting Data into R
- Exporting Data from R
- Data Objects- Data Types & Data Structure
- Viewing Named Objects
- Structure of Data Items
- Manipulating and Processing Data in R ( Creating, Accessing, Sorting Data Frames, Extracting, Combining, Merging, Reshaping Data Frames)
- Control Structures, Functions in R (numeric, character, statistical)
- Working with objects
- Viewing Objects within Objects
- Constructing Data Objects
- Building R Packages
- Running and Manipulating Packages
- Non Parametric Tests – ANOVA
- Chi – Square
- T – Test
- U – Test
- Introduction to Graphical Analysis
- Using Plots (Box Plots Scatter Plot, Pie Charts, Bar Charts, Line Chart)
- Plotting variables
- Designing Special Plots
- Simple Liner Regression
- Multiple Regression
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