Data Science Statistics and Analytics
Training in Chennai
Module 1
- Overview
- History of R
- Advantages and disadvantages
- Downloading and installing
- How to find documentation
Module 2
- Introduction
- Using the R console
- Getting help
- Learning about the environment
- Writing and executing scripts
- Saving your work
Module 3
- Data Structures and Variables
- Variables and assignment
- Data types-Indexing, subsetting
- Viewing data and summaries
- Functions
- Naming conventions
- Objects
- Models
- Graphics
Module 4
- Control Flow
- Truth testing
- Branching
- Looping
- Vectorized calculations
Module 5
- Functions
- Parameters
- Return values
- Variable scope
- Exception Handling
Module 6
- Getting Data into the R environment
- Builtin data
- Reading local data
- Web data
Module 7
- Overview of Statistics in R
- Introduction to R Graphics
- Model notation
Module 8
- Descriptive statistics
- Continuous data
- Scatter plot,Box plot
- Categorical data
- Mosaic plot
- Correlation
Module 9
- Inferential statistics
- T-test and non-parametric equivalents
- Chi-squared test, logistic regression
- Distribution testing
- Power testing
Module 10
- Linear Regression
- Linear models
- Regression plots
- ANOVA
Module 11
- Other Topics
- Classification
- Clustering
- Time series
- Dimensionality reduction
- Machine Learning
Module 12
- Object Oriented R
- Generic functions
- S3/S4 classes
Module 13
- Installing Packages
- Finding resources
- Installing resources
Module 14
- More about Graphics
- Labels
- Exporting graphics
Module 15
- Sophisticated Graphics in R
- Lattice
- GGplot2
- Interactive graphics
- Animated GIF
- rGGobi
Module 16
- R for Mapping and GIS
- Choropleth maps
- Layers