Data Science Certification Training – R Programming Tiwary
Last Update May 1, 2024
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About This Course

Become an expert in the various data analytics techniques using R. Master the data exploration, data visualization, predictive analytics, and descriptive analytics techniques.

Get hands-on practice on R  by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, the Music Industry, and unemployment. The course is best suited for beginners as well as experienced professionals who want to use R for data analytics.

Course description

What’s the focus of this course?

The Data Science with R training course has been designed to impart an in-depth knowledge of the various data analytics techniques which can be performed using R. The course is packed with real-life projects, case studies

Mastering advanced statistical concepts:   The course also includes various statistical concepts like linear and logistic regression, cluster analysis, and forecasting. You will also learn hypothesis testing.

As a part of the course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and the Internet. R CloudLab has been provided to ensure a practical and hands-on experience.  Additionally, we have four more projects for further practice.

What are the course objectives?

This course will enable you to:
  • Gain a foundational understanding of business analytics
  • Install R, R-studio, and workspace setup. You will also learn about the various R packages
  • Master the 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 DPLYP functions
  • Understand and use the various graphics in R for data visualization
  • Gain a basic understanding of the various statistical concepts
  • Understand and use hypothesis testing methods 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 course?

There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training, especially 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 course. If you are new in the field of data science, this is the best course to start with.
What is CloudLab?
CloudLab is a cloud-based R lab offered along with the course to ensure a hassle-free execution of the project work included.
With CloudLab, you do not need to install and maintain R on a virtual machine. Instead, you’ll be able to access a preconfigured environment—on CloudLab via your browser.
You can access CloudLab from the Simplilearn LMS (Learning Management System) for the duration of the course.

Course Description

Introduction to Business Analytics

  • Types of Analytics
  • Case study
  • Data Science and its importance

Introduction to R

  • Installing R
  • Installing R Studio
  • Workspace Setup
  • R Packages
  • R Programming
  • if statements
  • for statements
  • while statements
  • repeat statements
  • break and next statements
  • switch statement
  • scan statement
  • Executing the commands in a File

R Data Structure

  • Data structures
  • Vector
  • Matrix
  • Array
  • Data frame
  • List
  • Factors
  • Apply Functions
  • DPLYR & apply Function
  • Import Data File
  • DPLYP – Selection
  • DPLYP – Filter
  • DPLYP – Arrange
  • DPLYP – Mutate
  • DPLYP – Summarize

Data Visualization

  • Data visualization in R
  • Bar chart, Dot plot
  • Scatter plot, Pie chart
  • Histogram and Box plot
  • Heat Maps
  • World Cloud

Introduction to Statistics

  • Type of Data
  • Distance Measures (Similarity, dissimilarity, correlation)
  • Euclidean space.
  • Manhattan
  • Minkowski
  • Cosine similarity
  • Mahalanobis distance
  • Pearson’s correlation coefficient
  • Probability Distributions

Hypothesis Testing I

  • Hypothesis Testing – T-Test, Anova
  • Hypothesis Testing II
  • Hypothesis Testing about population
  • Chi-Square Test
  • F distribution and an F ratio

Regression Analysis

  • Linear Regression Models
  • Non-Linear Regression Models Lesson
  • Classification
  • Decision Tree
  • Logistic Regression
  • Bayesian
  • Support Vector Machines


  • K-means Clustering and Case Study
  • DBSCAN Clustering and Case study
  • Hierarchical Clustering


  • Apriori Algorithm
  • Candidate Generation
  • Visualization of Associated Rules

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