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R

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R, an essential tool for used by Facebook, LinkedIn and Google, is fast becoming the analytics tool for SMEs to MNCs. New packages get added to R quite regularly and we focus on enabling a trainee to self-develop skills in R.

Syllabus

Lesson 01 – Introduction To Business Analytics

Need Of Business Analytics

Types Of Business Analytics

Application Of Business Analytics - Case Study

Business Intelligence (BI)

Data Science

Lesson 02 – Introduction To R

Lesson 03 – R Programming

Introduction

Operators In R

Conditional Statements In R

Loops In R

R Functions

Lesson 04 – R Data Structure

Lesson 05 – Apply Functions

Lesson 06 – Data Visualization

Heat Maps

Introduction

Graphics In R

Bar Chart

Pie Charts

Histograms

Kernel Density

Histograms And A Density Plot

Line Charts

Lesson 07 – Introduction To Statistics

Basics Of Statistics

Correlation

Lesson 08 – Hypothesis Testing I

Power Of Test

Hypothesis

Null Hypothesis

Types Of Errors

Decision Making

Level Of Significance

Confidence Coefficient

Beta Risk

Lesson 09 – Hypothesis Testing II

Introduction To ANOVA Test

Chi-Square Test

Decision Rules

Three Types Of Hypothesis Tests

Testing Null Hypothesis

T-Test

Z-Test

Parametric Tests

Introduction

F-Ratio Test

Lesson 10 – Regression Analysis

Correlation

Types Regression Analysis

Introduction To Regression Analysis

Introduction

Non-Linear Regression

Lesson 11 – Classification

Support Vector Machines

Nearest Neighbor Classifiers

Bayesian

Model A Decision Tree

Decision Trees In Data Mining

Basic Algorithm For A Decision

Classification Rules Of Trees

Decision Tree

Data Preparation Issues

Classification Process

Lesson 12 – Clustering

DBSCAN Clustering

Hierarchical Clustering

K-Means Clustering

Lesson 13 – Association

Ordering Items

Association Rule

Association Rules

Apriori Algorithm