Data Science with R

About Course

This Data Science Training program provides comprehensive coverage of Data Science and Statistics, along with hands-on learning of leading analytical tool R. This training lets you gain expertise in Machine Learning Algorithms. It helps in data analysis and their related methods to understand and analyze Big Data.

Who can learn?

This Training is designed for the IT Beginners/professionals and Developers who want to make their career as Data Scientist, Business Analysts and Analysts Managers

Prerequisites

Knowledge of understanding basic programming concepts and basic statistical knowledge will be added advantage.

DATA SCIENCE with R Programming

R Programming

Course Introduction

• How to get help in the course
• Vectors
  • Vector Basics
  • Vector Operations
  • Vector Indexing and Slicing
  • Vector Exercise

How to install the Software

Development Environment Overview

Introduction to R Basics

Arithmetic in R

Variables

R Basic Data Types

Comparison Operators

• Data Frames

• Lists
• Data Input and Output with R
  • CSV Files
  • Excel Files
  • SQL with R
  • Web Scraping with R

• R Programming Basics

• Logical Operators
• If, else, and else if Statements
• While Loops
• For Loops
• Functions

• Advanced R Programming
  • Built-in R Features
  • Apply Functions
  • Math Functions with R
  • Regular Expressions
  • Dates and Timestamps

• Data Manipulation with R

• Charting in R
• Data Visualization With R
  • Overview of ggplot2
  • Histograms
  • Scatterplots
  • Barplots
  • Boxplots
  • 2 Variable Plotting
  • Coordinates and Faceting
  • Themes

• Interactive Visualizations with Plotly

• Capstone Data Project

Business Statistics

Introduction to Analytics

• Analytics Industry Overview
• Application of Analytics & Challenges to Analytics

Data Understanding

• Data Types
• Summarizing Techniques
• Five Number Summary
• Histograms & Ogives
• Box Plots
• Scatter Diagram
• Frequency Tables and Distribution
• Cumulative Distributions

Measure of Central Tendency,

Dispersion and its importance

• Understanding Range
• Inter Quartile Range
• Variance
• Standard Deviation

Probability and Probability Distribution

• Introduction to Probability
• Types of Probability
• Probability Rules
• Probability Distribution
• Random Variables
  • Discrete Random Variable
  • Continuous Random Variable

• Discrete Distributions

• Continuous Distribution

Sampling and Sampling Distribution

• Introduction to Sampling
• Random Sampling & Non Random Sampling
• Sampling Techniques
  • Stratified Sampling Method
  • Cluster Sampling Method

• Sampling Distribution
• Central Limit Theorem
• Standard Error Concept

Statistical Inference

• Estimation
  • Introduction
  • Point Estimates and Interval Estimates
  • Calculating Interval Estimates using ‘Z’ table
  • Introduction to ‘t’ distribution
  • Degrees Of Freedom
  • Calculating Interval Estimates using ‘t’ table
  • Confidence Intervals with t & z distributions
  • Determining Sample Size in Estimation

Hypotheses Testing

• Introduction
• Testing Procedure
• Testing Hypotheses
• One Sample Test & Two Sample Tests

• Z test
• t test
• One Tail & Two Tail Test
• Dependent & Independent Samples

• Concept of p-value
• ANOVA
  • Introduction
  • F distribution
  • One way ANOVA
  • Two Way ANOVA

• CHISQ Test
• Some Non Parametric Tests
• Man-Whitney U Test

• Wilcoxon Test
• Kruskal Wallis Test

Simple Regression & Correlation

• Introduction
• Dependent and Independent Variables
• Correlation Analysis
  • Spearman Correlation
  • Pearson Correlation

• Estimation in Regression
• Least Squared Method
• Standard Error Of Line
• Finding Regression Equation
• Hypotheses Testing for estimates
• Limitations & Errors in Simple Regression Analysis^\
• Multiple Regression analysis: Introduction
• Multicollinearity
• Fitting the model
• Regression Assumptions
• Residual Analysis for Regression Assumptions
• Transformation Of Variables

Logistic Regression

• Understanding Logistics Regression
• Difference between linear and logistics regression
• Odds Ratio
• Logit Model
• Building Models
• ROC concept
• Model Fitting
• Evaluation of goodness of fit
• Model Suitability

Cluster Analysis

• Introduction to Cluster Analysis
• Clustering Algorithm
• Hierarchical Clustering Procedure

• Agglomerative Clustering Technique
• Non Hierarchical Procedure
• K-means

• Evaluation of Clustering Results
• Application

Factor Analysis

• Definition and examples
• Factor Analysis
• Communality
• Rotation Of Factors
• Implementation
• Evaluation

MACHINE LEARNING USING R

• Introduction to Machine Learning
• Linear Regression
• Project : Linear Regression
• Logistic Regression
• Project : Logistic Regression
• K Nearest Neighbors
• Project : K Nearest Neighbors
• Decision Trees and Random Forests
• Project : Decision Trees and Random Forests
• Support Vector Machines
• Project : Support Vector Machines
• K – Means Clustering
• Project : K – Means Clustering
• Natural Language Processing
• Project : Natural Language Processing
• Neural Nets
• Project : Neural Nets