R Programming Foundations for Data Science — PickAClass
3.6 (5) ⏱ 2h 42m 📚 27 lessons 🎧 Audio version

R Programming Foundations for Data Science

Build a strong foundation in R programming to manipulate data, perform exploratory analysis, and write clean code using modern data science workflows.

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About this course

Data science relies on powerful tools to transform raw numbers into meaningful insights, and R is one of the most trusted languages for the job. If you want to start analyzing data but have no prior programming experience, learning R is the perfect place to begin. This course guides you through the core concepts of R, from basic syntax to modern data manipulation workflows. You will transition from understanding fundamental data types to writing your own scripts that clean, filter, and summarize complex datasets efficiently. What you'll learn: - Understand fundamental R syntax, variables, and essential data types. - Manipulate data structures including vectors, matrices, lists, and data frames. - Apply modern tidyverse techniques to filter, arrange, and transform datasets. - Write clean, reusable code using control flow, loops, and custom functions. - Practice importing raw data and preparing it for exploratory analysis. You will start with the absolute basics of the R language, learning key terminology and foundational definitions. From there, you will progress through structured written explanations and practical code examples to build your confidence in data manipulation and programmatic thinking. This course is designed specifically for beginners with no prior programming or data science experience. Start your data science journey today by mastering the fundamentals of R programming.

What you'll get

  • 📜 Certificate of completion
    Add it to your LinkedIn profile
  • 💬 Personal AI tutor
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  • 🎧 Audio version included
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  • ♾️ Lifetime access
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  • 📱 Phone or computer
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  • 💸 14-day refund
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  • Short & focused
    2h 42m of practical content

Certificate of completion

Every course you complete on PickAClass issues a credential like this — original, with its own code, verifiable by URL, and detailed about what was actually demonstrated.

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Certificate of Mastery
This certifies that
Name Surname
has successfully demonstrated mastery of
R Programming Foundations for Data Science
Skills demonstrated
Behavioral pattern analysis
Foundational
1.2 hrs
Decision-architecture frameworks
Proficient
1.4 hrs
A/B test design
Proficient
1.7 hrs
Behavioral copywriting
Advanced
1.9 hrs
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PickAClass — Name Surname
R Programming Foundations for Data Science
Page 2 of 2
Performance detail
Coursework summary
Lessons completed 14 / 14
Practice questions 26 / 28
Assignments submitted 4 (avg 4.5 / 5)
Capstone project Reviewed — 4.6 / 5
Total practice 6.2 hrs
Performance benchmark
Cohort rank Top 12% of 1,625
Time to completion 11 days (median: 22)
Mastery score 91 / 100
Practice-question score 94%
Skill verification Verified Skill Path
Verify this credential
pickaclass.com/certificates/PCC-2026-X4F7-AP19
Issued under the academic standards of PickAClass. Skill levels reflect assessed performance against the course's competency rubric. This is an original credential of this platform.

Reviews (5)

Дмитрий Иванов BY Verified learner
★ 3 · September 7, 2026

Thoroughly enjoyed this course. The way the information was presented was excellent, and the practical applications were highlighted effectively. Great job!

Pedro Souza BR Verified learner
★ 3 · August 28, 2026

Hmm, I'm not sure this is for absolute beginners. It assumes a bit of prior knowledge that wasn't explicitly taught. Some examples were confusing.

Björn Ólafsson IS Verified learner
★ 4 · July 29, 2026

It's a good course if you have some prior knowledge. For absolute beginners, some concepts might be a bit challenging. The structure is logical, though.

Oded Solomon IL
★ 4 · July 23, 2026

It's a solid course. The structure is logical and most of the examples were helpful. Could use a few more real-world scenarios though.

إبراهيم الشريف TN
★ 4 · July 12, 2026

Pretty good foundation. The explanations were generally clear, and the structure made sense. I'd say it's a worthwhile course.

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