Data Science with R Programming


This Data Science with R Programming certification training course online offers ✔️64 hrs of training ✔️10 projects ✔️Math Refresher ✔️Statistics. Enroll now!

Data Science with R Programming


SKU: 2e04f23a0f6e Category:

Program Overview:

The Data Science with R Certification course enables you to take your data science skills
into a variety of companies, helping them analyze data and make more informed business
decisions.The course covers data exploration, data visualization, predictive analytics, and
descriptive analytics techniques with the R language. You will learn about R packages, how
to import and export data in R, data structures in R, various statistical concepts, cluster
analysis, and forecasting.

Program Features:

  • 64 hours of applied learning
  • 10 real-life industry projects
  • 365 days of access

Delivery Mode:

Online Bootcamp – Online self-paced learning


There are no prerequisites for this Data Science Certification with R programming course. If you
are a beginner in data science, this is one of the best courses to start with.

Target Audience:

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
  • Analytics Professionals
  • Software developers

Key Learning Outcomes:

When you complete this data science course, you will be able to accomplish the following:

  • Gain a foundational understanding of business analytics
  • Install R, RStudio, workspace setup, and learn about the various R packages
  • Master R programming and understand how various statements are executed in R
  • Gain an in-depth understanding of data structure used in R and learn to import/export data in R
  • Define, understand and use the various apply functions and DPLYR functions
  • Understand and use the various graphics in R for data visualization
  • Gain a basic understanding of various statistical concepts
  • Understand and use the hypothesis testing method to drive business decisions
  • Understand and use linear and non-linear regression models, and classification techniques for data analysis
  • Learn and use the various association rules with the Apriori algorithm
  • Learn and use clustering methods including k-means, DBSCAN, and hierarchical clustering

Certification Details and Criteria:

Online Classroom

  • Attend one complete batch of Data Science Certification with R programming training
  • Complete one project

Online Self-Learning:

  • Complete 85 percent of the course.
  • Complete one project

Course Curriculum:

Lesson 01 – Introduction to Business Analytics

  • Overview
  • Business Decisions and Analytics
  • Types of Business Analytics
  • Applications of Business Analytics
  • Data Science Overview
  • Conclusion
  • Knowledge Check

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
  • Conclusion
  • Knowledge Check

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
  • Conclusion
  • Knowledge Check

Lesson 04 – Data Visualization

  • Overview
  • Introduction to Data Visualization
  • Data Visualization Using Graphics in R
  • Ggplot2
  • File Formats of Graphic Outputs R
  • Conclusion
  • Knowledge Check

Lesson 05 – Statistics for Data Science-I

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

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
  • Conclusion
  • Knowledge Check

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
  • Conclusion
  • Knowledge Check

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
  • Conclusion
  • Knowledge Check

Lesson 09 – Clustering

  • Overview
  • Introduction to Clustering
  • Clustering Methods
  • Demo: K-means Clustering
  • Demo: Hierarchical Clustering
  • Conclusion
  • Knowledge Check

Lesson 10 – Association

  • Overview
  • Association Rule
  • Apriori Algorithm
  • Demo: Apriori Algorithm
  • Conclusion
  • Knowledge Check