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Data Science with R

  /    /  Data Science with R

Data Science with R

Data Science

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

The intended audience for this course:

  • Application Developers
  • DevOps Engineers
  • Architects
  • System Engineers
  • Technical Managers
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
  • Introducing Data frame
  • Data Indexing and Selection
  • Data Indexing and Selection
  • Combining Datasets: Merge and Join
  • Aggregation and Grouping
Introduction to ggplot2 and Esquisse
  • Simple Line Plots
  • Simple Scatter Plots
  • Customizing Plot Legends
  • Text and Annotation
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
  • The Lasso
  • Selecting the Tuning Parameter
  • Principal Components Regression
  • Regression in High Dimensions
  • Hands-On Exercises

Participants should preferably have some hands-on experience in R Programming

Course Information


3 Days / 4 Days

Mode of Delivery

Instructor led/Virtual

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