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.
Analyze Data Using R for Statistical Analytics
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.
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
February 2026
Assessments
16 assignments
Taught in English
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There are 4 modules in this course
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
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
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
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.
What's included
7 videos4 assignments
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Frequently asked questions
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More questions
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