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
4.54 (55805 reviews)
278 740
students
10.5 hours
content
Jan 2025
last update
$119.99
regular price
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
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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.
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765242
udemy ID
17/02/2016
course created date
01/11/2019
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