R Programming A-Z™: R For Data Science With Real Exercises!

Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2

4.53 (55890 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Learn to program in R at a good level

Learn how to use R Studio

Learn the core principles of programming

Learn how to create vectors in R

Learn how to create variables

Learn about integer, double, logical, character and other types in R

Learn how to create a while() loop and a for() loop in R

Learn how to build and use matrices in R

Learn the matrix() function, learn rbind() and cbind()

Learn how to install packages in R

Learn how to customize R studio to suit your preferences

Understand the Law of Large Numbers

Understand the Normal distribution

Practice working with statistical data in R

Practice working with financial data in R

Practice working with sports data in R

Course Gallery

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R Programming A-Z™: R For Data Science With Real Exercises! – Screenshot 2 — Screenshot 2 – R Programming A-Z™: R For Data Science With Real Exercises!

R Programming A-Z™: R For Data Science With Real Exercises! – Screenshot 3 — Screenshot 3 – R Programming A-Z™: R For Data Science With Real Exercises!

R Programming A-Z™: R For Data Science With Real Exercises! – Screenshot 4 — Screenshot 4 – R Programming A-Z™: R For Data Science With Real Exercises!

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Comidoc Review

Our Verdict

R Programming A-Z™ is a solid choice for beginners seeking R mastery through structured lessons and practical examples. While occasional inconsistencies require extra vigilance, comprehensive exercises and Kirill's effective teaching style keep learners engaged throughout their data science journey. With minor improvements in advanced material and clarity on target audience, this course can undoubtedly strengthen its position as a leading resource for R enthusiasts.

What We Liked

The course builds R programming skills progressively, starting from basics like vectors and loops to complex topics like matrices and packages.
Recaps and homework assignments at the end of each section help solidify understanding through practice, making lessons memorable and fun.
Kirill Eremenko is a knowledgeable instructor who provides valuable tips, ensuring learners grasp core programming principles.
The course covers working with various types of data - statistical, financial, sports - preparing students for real-world applications.

Potential Drawbacks

While the course intends to cover R from A-Z, it may be too basic for some learners and could benefit from clearer indication of target audience.
Some inconsistencies in code examples have been reported, prompting students to seek additional support or troubleshoot on their own.
The course can focus more on advanced R features like Rmarkdown for note-taking and visualization, beyond basic programming skills.
Statistical learning aspects could be expanded, giving a competitive edge in data science applications over other languages.