Data Science with R

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

Duration

3 Days

Level

Intermediate Level

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As per your team needs

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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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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
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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Topics

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Data Science with R

Duration

Level

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