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Analyze Data Using R for Statistical Analytics

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Analyze Data Using R for Statistical Analytics

This course is part of Analyze and Apply R for Data Analytics Specialization

Instructor: EDUCBA

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

9 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

9 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Use R programming to analyze data and perform exploratory data analysis.
Apply core statistical techniques and build analytical models in R.
Interpret insights using visualizations and real-world data analytics use cases.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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Build your subject-matter expertise

This course is part of the Analyze and Apply R for Data Analytics Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

Learners will analyze data using R, apply core statistical techniques, build analytical models, and interpret insights through visualization and real-world use cases. By the end of this course, learners will be able to confidently use R programming to perform data analysis, statistical modeling, and exploratory analytics.

This beginner-friendly course provides a structured, end-to-end introduction to Data Analytics using R, starting from R’s origin, architecture, and syntax, and progressing through vectors, data frames, visualization, and statistical methods. Learners gain hands-on exposure to essential programming concepts, data handling techniques, and analytical workflows that are widely used in academia and industry. What makes this course unique is its subtitles-driven, concept-aligned curriculum, ensuring every topic directly reflects real instructional explanations rather than abstract theory. The course emphasizes practical analytics, including regression, decision trees, time series analysis, and business-focused case studies such as insurance analytics. Designed for aspiring data analysts, students, and professionals, this course builds a strong foundation in R programming while developing analytical thinking skills that are transferable to real-world data science and statistical problem-solving scenarios.

Module details

This module introduces learners to the R programming language, covering its origin, architecture, file types, syntax rules, and core data types used in data analytics and statistical computing.

What's included

6 videos4 assignments

6 videosTotal 50 minutes

Comprehensice Course on R7 minutes
Origination of R8 minutes
Introduction to Architecture of R8 minutes
Different File Types in R9 minutes
Basic Syntax6 minutes
Different Data Types13 minutes

4 assignmentsTotal 60 minutes

Course Overview and Origin of R10 minutes
Architecture and File Types in R10 minutes
Basic Syntax and Core Data Types10 minutes
Introduction to R Programming Fundamentals30 minutes

This module focuses on essential R programming constructs, including vectors, variables, functions, operators, control structures, and string manipulation techniques required for efficient data processing.

What's included

6 videos4 assignments

6 videosTotal 45 minutes

Creating Vectors7 minutes
Creating Vectors Continues8 minutes
Functions and Variables in R5 minutes
Operators in R7 minutes
Loops and Functions in R13 minutes
Manipulation with Strings6 minutes

4 assignmentsTotal 60 minutes

Vector Creation and Usage10 minutes
Variables, Functions, and Operators10 minutes
Control Structures and String Manipulation10 minutes
Core Programming Concepts in R30 minutes

This module introduces data frames and visualization techniques in R, enabling learners to organize data and create meaningful graphical representations for exploratory data analysis.

What's included

6 videos4 assignments

6 videosTotal 55 minutes

Concept of Data Frame10 minutes
Executing with Values10 minutes
Charts in R12 minutes
Functions of Charts8 minutes
Performing Analytics in R7 minutes
Data Exploration and Preparation8 minutes

4 assignmentsTotal 60 minutes

Data Frames and Data Handling10 minutes
Introduction to Charts10 minutes
Performing Analytics with Visual Data10 minutes
Data Structures and Visualization30 minutes

This module covers statistical methods, regression models, decision trees, time series analysis, and real-world business applications to perform predictive and descriptive analytics using R.