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Data Science with R Certification Course

Course Description

Data Science with R Certification Course

Simplilearn’s Data Science certification with R Programming training makes you an expert in data analytics using the R programming language. This course enables you to take your Data Science skills into a variety of companies, helping them analyze data and make more informed business decisions.

Data Science Certification Course Overview

The Data Science Certification with R programming training 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.


This Data Science online certification with R programming is beneficial for all aspiring data scientists including, IT professionals or software developers looking to make a career switch into Data analytics, professionals working in data and business analysis, graduates wishing to build a career in Data Science, and experienced professionals willing to harness Data Science in their fields.


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.

Course Highlights

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Upon completion of your training, you’ll receive a personalized certificate of completion to help validate to others your new skills.

Course Syllabus

Lesson 00 – Course Introduction

Course Introduction

Lesson 01 – Introduction to Business Analytics

1.001 Overview
1.002 Business Decisions and Analytics
1.003 Types of Business Analytics
1.004 Applications of Business Analytics
1.005 Data Science Overview
1.006 Conclusion
Knowledge Check

Lesson 02 – Introduction to R Programming

2.001 Overview
2.002 Importance of R
2.003 Data Types and Variables in R
2.004 Operators in R
2.005 Conditional Statements in R
2.006 Loops in R
2.007 R script
2.008 Functions in R
2.009 Conclusion
Knowledge Check

Lesson 03 – Data Structures

3.001 Overview
3.002 Identifying Data Structures
3.003 Demo Identifying Data Structures
3.004 Assigning Values to Data Structures
3.005 Data Manipulation
3.006 Demo Assigning values and applying functions
3.007 Conclusion
Knowledge Check

Lesson 04 – Data Visualization

4.001 Overview
4.002 Introduction to Data Visualization
4.003 Data Visualization using Graphics in R
4.004 ggplot2
4.005 File Formats of Graphic Outputs
4.006 Conclusion
Knowledge Check

Lesson 05 – Statistics for Data Science-I

5.001 Overview
5.002 Introduction to Hypothesis
5.003 Types of Hypothesis
5.004 Data Sampling
5.005 Confidence and Significance Levels
5.006 Conclusion
Knowledge Check

Lesson 06 – Statistics for Data Science-II

6.001 Overview
6.002 Hypothesis Test
6.003 Parametric Test
6.004 Non-Parametric Test
6.005 Hypothesis Tests about Population Means
6.006 Hypothesis Tests about Population Variance
6.007 Hypothesis Tests about Population Proportions
6.008 Conclusion
Knowledge Check

Lesson 07 – Regression Analysis

7.001 Overview
7.002 Introduction to Regression Analysis
7.003 Types of Regression Analysis Models
7.004 Linear Regression
7.005 Demo Simple Linear Regression
7.006 Non-Linear Regression
7.007 Demo Regression Analysis with Multiple Variables
7.008 Cross Validation
7.009 Non-Linear to Linear Models
7.010 Principal Component Analysis
7.011 Factor Analysis
7.012 Conclusion
Knowledge Check

Lesson 08 – Classification

8.001 Overview
8.002 Classification and Its Types
8.003 Logistic Regression
8.004 Support Vector Machines
8.005 Demo Support Vector Machines
8.006 K-Nearest Neighbours
8.007 Naive Bayes Classifier
8.008 Demo Naive Bayes Classifier
8.009 Decision Tree Classification
8.010 Demo Decision Tree Classification
8.011 Random Forest Classification
8.012 Evaluating Classifier Models
8.013 Demo K-Fold Cross Validation
8.014 Conclusion
Knowledge Check

Lesson 09 – Clustering

9.001 Overview
9.002 Introduction to Clustering
9.003 Clustering Methods
9.004 Demo K-means Clustering
9.005 Demo Hierarchical Clustering
9.006 Conclusion
Knowledge Check

Lesson 10 – Association

10.001 Overview
10.002 Association Rule
10.003 Apriori Algorithm
10.004 Demo Apriori Algorithm
10.005 Conclusion
Knowledge Check


You Will Get Certification After Completetion This Course.


Frequently Asked Questions

Instructional methods, course requirements, and learning technologies can vary significantly from one online program to the next, but the vast bulk of them use a learning management system (LMS) to deliver lectures and materials, monitor student progress, assess comprehension, and accept student work. LMS providers design these platforms to accommodate a multitude of instructor needs and preferences.

Online education may seem relatively new, but years of research suggests it can be just as effective as traditional coursework, and often more so. According to a U.S. Department of Education analysis of more than 1,000 learning studies, online students tend to outperform classroom-based students across most disciplines and demographics. Another major review published the same year found that online students had the advantage 70 percent of the time, a gap authors projected would only widen as programs and technologies evolve.

All new learning innovations are met with some degree of scrutiny, but skepticism subsides as methods become more mainstream. Such is the case for online learning. Studies indicate employers who are familiar with online degrees tend to view them more favorably, and more employers are acquainted with them than ever before. The majority of colleges now offer online degrees, including most public, not-for-profit, and Ivy League universities. Online learning is also increasingly prevalent in the workplace as more companies invest in web-based employee training and development programs.

The concern that online students cheat more than traditional students is perhaps misplaced. When researchers at Marshall University conducted a study to measure the prevalence of cheating in online and classroom-based courses, they concluded, “Somewhat surprisingly, the results showed higher rates of academic dishonesty in live courses.” The authors suggest the social familiarity of students in a classroom setting may lessen their sense of moral obligation.

Choosing the right course takes time and careful research no matter how one intends to study. Learning styles, goals, and programs always vary, but students considering online courses must consider technical skills, ability to self-motivate, and other factors specific to the medium. Online course demos and trials can also be helpful.
Our platform is typically designed to be as user-friendly as possible: intuitive controls, clear instructions, and tutorials guide students through new tasks. However, students still need basic computer skills to access and navigate these programs. These skills include: using a keyboard and a mouse; running computer programs; using the Internet; sending and receiving email; using word processing programs; and using forums and other collaborative tools. Most online programs publish such requirements on their websites. If not, an admissions adviser can help.