Data Science With R

R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing.

The R language is widely used among statisticians and data miners for developing statistical software and data analysis and R is rapidly becoming the leading language in data science and statistics

The course aims to give an understanding of Data Science. This course will help the student to build Exploratory models, predictive models, gain practical experience in the application area of Data Science.

• Introductory R language fundamentals and basic syntax
• What R is and how it’s used to perform data analysis
• Make use of R loop functions and debugging tools
• Understand critical programming language concepts
• Become familiar with the major R data structures
• Create your own visualizations using R

Curriculum:

• Setting up the python environment for R
• R Installation
• R Studio Installation
• Introduction to R
• Introduction to R Programming
• The Data types in R
• Basic Data Structures in R
• Loops
• Functions
• Introduction to dplyr and Data.table
• Simple Line Plots
• Simple Scatter Plots
• Customizing Plot Legends
• Text and Annotation
• Introduction to ggplot2 and Esquisse
• Introduction to Statistics
• Simulating Random Variables
• Random Number Generation
• Statistics Functions
• Continuous Random Variables
• Statistical Tests
• Introduction to Machine Learning
• Different types of machine learning
• Machine Learning Applications
• Introducing mlr and Caret
• Introduction to  mlr and Caret
• Lab: Exploring Handwritten Digits
• Introduction to Linear Regression
• Simple Linear Regression
• Multiple Linear Regression
• Hands-on Exercise
• Introduction to Classification
• An Overview of Classification
• Logistic Regression
• Multiple Logistic Regression
• Bayes’ Theorem for Classification
• Linear Discriminant Analysis
• Quadratic Discriminant Analysis
• KNN
• Hands-on Exercises
• Acquiring & Preparing Data
• Content Acquisition Overview
• Working with RCrawler
• Acquiring data
• Data Cleaning & Wrangling
• Missing Values and Outlier
• Hands-on Exercises
• Feature Engineering
• What is Feature Engineering?
• Why Feature Engineering?
• How to apply Feature Engineering?
• Discussions on various scenarios
• Hands-on Exercises
• Resampling Methods
• Cross-Validation
• Leave-One-Out Cross-Validation
• K fold Cross-Validation
• Bias-Variance Trade-Off for k-Fold Cross-Validation
• The Bootstrap
• Hands-on Exercises
• Model Selection and Regularization
• Best Subset Selection
• Stepwise Selection
• Choosing the Optimal Model
• Ridge Regression
• And more.