Data Science With R

Data Science With R Course Curriculum

data science with r course singapore

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

SkillsFuture Claimable Course

An Overview of R and Business Analytics

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