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Data Science with R

Learn how to build Exploratory models, predictive models, gain practical experience in the application area of Data Science using R


3 Days


Intermediate Level

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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
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The intended audience for this course:

  • Application Developers
  • DevOps Engineers
  • Architects
  • System Engineers
  • Technical Managers
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  • R Installation
  • R Studio Installation
  • Introduction to R
  • The Data types in R
  • Basic Data Structures in R
  • Loops
  • Functions
  • Simple Line Plots
  • Simple Scatter Plots
  • Customizing Plot Legends
  • Text and Annotation
  • Simulating Random Variables
  • Random Number Generation
  • Statistics Functions
  • Continuous Random Variables
  • Statistical Tests
  • Different types of machine learning
  • Machine Learning Applications
  • Introduction to  mlr and Caret
  • Lab: Exploring Handwritten Digits
  • Simple Linear Regression
  • Multiple Linear Regression
  • Hands-on Exercise
  • An Overview of Classification
  • Logistic Regression
  • Multiple Logistic Regression
  • Bayes’ Theorem for Classification
  • Linear Discriminant Analysis
  • Quadratic Discriminant Analysis
  • KNN
  • Hands-on Exercises
  • Content Acquisition Overview
  • Working with RCrawler
  • Acquiring data
  • Data Cleaning & Wrangling
  • Missing Values and Outlier
  • Hands-on Exercises
  • What is Feature Engineering?
  • Why Feature Engineering?
  • How to apply Feature Engineering?
  • Discussions on various scenarios
  • Hands-on Exercises
  • 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
  • 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
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Participants should preferably have some hands-on experience in R Programming


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