Data Science With R

Data Science With R Course Curriculum

This R programming course in Singapore forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science.

By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc

• Introduction to Data Science Methologies

• Data Types

• Introduction to Data Science Tools

• Statistics

• Approach to Business Problems

• An overview of Analytics

• Models and Algorithms

• Importance of Data Science

• Data Science as a Strategic Asset

• R, Python, WEKA, RapidMiner

Overview Of R

Introduction to R

• Data Structures and Manipulation in R

• Installing R on Various Operating Systems

• IDEs for R

• Steps in R Initiation

• Installing an R Package

Overview Of Business Analytics

• Introduction to Business Analytics

• Analytics Technology and Resources

• Need of Business Analytics

• Types and Features of Business Analytics

• Descriptive and Predictive Analytics

• Business Decisions

• Analytical Tools

• Data Science as a Strategic Asset

Data Visualization And Manipulation

• Introduction

• Types of Graphics

• Save Graphics to a File

• Graphics in R

• Create a word cloud

• Exporting Graphs in EStudio

Hypothesis Testing

• Need of Hypothesis Testing in Businesses

• Chances of Errors in Sampling

• Level of Significance

• Types of Statistical Hypothesis Tests

• Test Statistic

• Types of Errors

• Use Normal and Student Probability Distribution Functions

• Objectives of Null Hypothesis Test

• Use Chi-Squared Test Statistics

Logistic Regression Analysis

• Introduction to Regression Analysis and Usage

• Types of Regression Analysis

• Interaction Regression Model

• Correlation

• Logit Function

• Lift charts

• Decile Analysis

Cluster Analysis Classification Models

• Introduction to Cluster Techniques

• Examples of Classification

• Classification Process – Model Construction

• Data Preparation Issues

• Basic Algorithm for a Decision Tree

• Decision Trees in Data Mining

• Naive Bayes Classifier

• Clustering Models

• Use Cases of Clustering

• DBSCAN Clustering Algorithm

• Distance Methodologies

• Hierarchical and Non-Hierarchical Procedure

• K-Means clustering