Data Science with R Programming

R Programming Course in Singapore

Why Should I Learn Data Science with r programming course in Singapore from Inspizone?

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

What are the course objectives?

The Data Science with R programming course has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies.
Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R.
Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing.

What you will learn in this data science course?

This data science training course will enable you to:
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  • Gain a foundational understanding of business analytics
  • Install R, R-studio, and workspace setup, and learn about the various R packages
  • Master R programming and understand how various statements are executed in R
  • Gain in-depth understanding of data structure used in R and learn to import/export data in R
  • Define, understand and use the various application functions and DPYR functions
  • Understand and use the various graphics in R for data visualization
  • Gain a basic understanding of various statistical concepts
  • Understand and use hypothesis testing method to drive business decisions
  • Understand and use linear, non-linear regression models, and classification techniques for data analysis
  • Learn and use the various association rules and Apriori algorithm
  • Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering

Who should take this Online Data Science Training Course?

There is an increasing demand for skilled data scientists across all industries, making this data science certification course well-suited for participants at all levels of experience. We recommend this Data Science training particularly for the following professionals:

  • IT professionals looking for a career switch into data science and analytics
  • Software developers looking for a career switch into data science and analytics
  • Professionals working in data and business analytics
  • Graduates looking to build a career in analytics and data science
  • Anyone with a genuine interest in the data science field
  • Experienced professionals who would like to harness data science in their fields

Prerequisites: There are no prerequisites for this data science online training course. If you are new in the field of data science, this is the best course to start with.

Duration: 3 Days

Venue: 10 Anson Road, 26-08A International Plaza, Singapore 079903

  Data Science Certification Course Outline:

Lesson 01 - Introduction to Business Analytics

  • Overview
  • Business Decisions and Analytics
  • Types of Business Analytics
  • Applications of Business Analytics
  • Data Science Overview

Lesson 02 - Introduction to R Programming

  • Overview
  • Importance of R
  • Data Types and Variables in R
  • Operators in R
  • Conditional Statements in R
  • Loops in R
  • R script
  • Functions in R

Lesson 03 - Data Structures

  • Overview
  • Identifying Data Structures
  • Demo Identifying Data Structures
  • Assigning Values to Data Structures
  • Data Manipulation
  • Demo Assigning values and applying functions

Lesson 04 - Data Visualization

  • Overview
  • Introduction to Data Visualization
  • Data Visualization using Graphics in R
  • ggplot2
  • File Formats of Graphic Outputs

Lesson 05 - Statistics for Data Science-I

  • Overview
  • Introduction to Hypothesis
  • Types of Hypothesis
  • Data Sampling
  • Confidence and Significance Levels

Lesson 06 - Statistics for Data Science-II

  • Overview
  • Hypothesis Test
  • Parametric Test
  • Non-Parametric Test
  • Hypothesis Tests about Population Means
  • Hypothesis Tests about Population Variance
  • Hypothesis Tests about Population Proportions

Lesson 07 - Regression Analysis

  • Overview
  • Introduction to Regression Analysis
  • Types of Regression Analysis Models
  • Linear Regression
  • Demo Simple Linear Regression
  • Non-Linear Regression
  • Demo Regression Analysis with Multiple Variables
  • Cross Validation
  • Non-Linear to Linear Models
  • Principal Component Analysis
  • Factor Analysis

Lesson 08 - Classification

  • Overview
  • Classification and Its Types
  • Logistic Regression
  • Support Vector Machines
  • Demo Support Vector Machines
  • K-Nearest Neighbours
  • Naive Bayes Classifier
  • Demo Naive Bayes Classifier
  • Decision Tree Classification
  • Demo Decision Tree Classification
  • Random Forest Classification
  • Evaluating Classifier Models
  • Demo K-Fold Cross Validation

Lesson 09 - Clustering

  • Overview
  • Introduction to Clustering
  • Clustering Methods
  • Demo K-means Clustering
  • Demo Hierarchical Clustering

Lesson 10 - Association

  • Overview
  • Association Rule
  • Apriori Algorithm
  • Demo Apriori Algorithm

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