Business analytics with R Introduction

Business analytics with R course will introduce you to the concept of data mining, business analytics, data manipulation, data visualization, exploratory data analysis, sentiment analysis and the concepts using open source R – Programming language & many other. R is a providing excellent capability to the analytics professionals open source programming environment. It much easier to pick up as compared to java like languages, it is high level programming language making.

Prerequisites:

• You don’t need to have a programming back ground to do
• Basic knowledge on R – Programming from scratch.
• It’s good to have basic understanding of statistics.

BUSINESS ANALYTICS WITH R ONLINE COURSE CONTENT

1. INTRODUCTION OF BUSINESS ANALYTICS AND R

• Introduction to Business Analytics with Applications
• Career opportunities
• Installation Procedure of RCmdr & RStudio interface

2. R PROGRAMMING

• Basic Math operations.
• Advanced Control Statements, Data Structures,&  Loop
• Built in Function.

3. STATISTICS

• Statistical Measures.
• Probability Distribution and its types.
• Creating Summary statistics.

4. IMPORTING DATA FROM DIFFERENT SOURCES

• CSV, Text, Excel, MySQL Data base
• Statistical Tools
• Web Scraping
• Packages
• GUI

5. DATA MANIPULATION IN R

• Apply Family, DPLYR Package.
• String Manipulation.
• Data Reshaping

6. DATA VISUALIZATION IN R

• Data Visualization Introduction
• Difference between Multivariate graphs & Bivariate , Univariate
• Different types of Graphs

7. DATA EXPLORATION AND PREPARATION

• EDA (Exploratory Data Analysis)
• Cross tabulation.
• Anomaly detection & Graphical analysis

8. DATA MINING WITH MACHINE LEARNING

• Introduction to Machine Learning, Data Mining.
• Clustering & its Application.
• Clustering Techniques.

9. ASSOCIATION RULE MINING AND SENTIMENT ANALYSIS

• Association Rule Mining & its Algorithm.
• Sentiment Analysis.
• Mining data from Twitter.

10. PREDICTIVE MODELLING TECHNIQUES

• Hypothesis Testing.
• T-Test , Z-Test
• Chi Square Test

11. REGRESSION MODELLING IN R

• Regression Introduction
• Linear Regression
• Logistic Regression

12. DECISION TREE AND RANDOM FOREST

• Decision Tree & its terminology
• Building Decision Trees
• Decision Tree splitting method
• Random Forest Introduction
• Party package