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Analyze Data Using R for Statistical and Predictive Modeling

This course is part of Apply R for Predictive Analytics and Machine Learning Specialization

Instructor: EDUCBA

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Analyze and visualize data using R programming and core data manipulation techniques.
Apply statistical methods and build predictive models such as regression and decision trees.
Interpret analytical results to support real-world, data-driven decision-making.

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 Apply R for Predictive Analytics and Machine Learning 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 3 modules in this course

By the end of this course, learners will be able to analyze data using R, apply statistical methods, build predictive models, and interpret analytical results for real-world decision-making. Learners will gain hands-on experience with R programming fundamentals, data manipulation, visualization techniques, and advanced analytics such as regression, decision trees, and time series analysis.

This course is designed to guide learners from the basics of R—its origin, architecture, syntax, and data structures—to practical data analysis and business applications. Through structured modules, learners will work with vectors, data frames, loops, functions, and charts, and then progress to statistical analytics, distribution functions, and predictive modeling techniques. Real-world scenarios, including insurance industry case studies, help learners understand how analytics is applied in professional environments. What makes this course unique is its balanced focus on both programming and analytics, making it suitable for beginners as well as professionals transitioning into data analytics roles. With clearly aligned learning objectives, graded assessments, and practice quizzes, learners will build job-ready skills in R that can be applied across industries such as finance, insurance, and data science. Completing this course equips learners with a strong analytical mindset and practical R skills to confidently explore data, generate insights, and support data-driven decisions.

Module details

This module introduces learners to the R programming language by exploring its origin, architecture, syntax, and core data structures. Learners will build a strong foundation in R by understanding how to write basic programs, work with data types, create vectors, and define functions, preparing them for data analysis tasks.

What's included

9 videos4 assignments

9 videosTotal 69 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
Creating Vectors7 minutes
Creating Vectors Continues8 minutes
Functions and Variables in R5 minutes

4 assignmentsTotal 60 minutes

Getting Started with R10 minutes
Core Syntax and Data Structures10 minutes
Working with Vectors and Variables10 minutes
Graded-Foundations of R Programming30 minutes

This module focuses on controlling program flow, manipulating text data, working with data frames, and creating visualizations in R. Learners will develop skills to process real-world datasets, apply logical conditions, and generate meaningful charts to communicate insights effectively.

What's included

7 videos4 assignments

7 videosTotal 65 minutes

Operators in R7 minutes
Loops and Functions in R13 minutes
Manipulation with Strings6 minutes
Concept of Data Frame10 minutes
Charts in R12 minutes
Functions of Charts8 minutes
Executing with Values10 minutes

4 assignmentsTotal 60 minutes

Logic, Control Flow, and Strings10 minutes
Data Frames and Visualization Basics10 minutes
Advanced Charting and Execution10 minutes
Graded-Data Handling and Visualization in R30 minutes

This module equips learners with statistical and analytical techniques using R, including regression models, decision trees, and time series analysis. Learners will apply data exploration, modeling, and interpretation skills to solve real-world business problems and make data-driven decisions.