R Programming A-Z: R For Data Science

Course Overview

This course walks new programmers through R from the very first install of RStudio, using short lecture videos paired with practical exercises rather than long theory dumps. The teaching approach leans heavily on building one small skill at a time — vectors, then variables, then data types, then loops — so each concept has somewhere to land before the next one shows up.

Beyond the syntax basics, the course spends real time on applied work: manipulating vectors and matrices, writing while() and for() loops, and running small statistical exercises using financial and sports datasets. It's less "here's R the language" and more "here's how you'd actually poke at data with R," which fits its stated audience of people who want a gentler, doing-based path into the language.

What You Will Learn

• Installing and setting up R and RStudio on Mac or Windows
• Core programming logic: variables, data types (integer, double, logical, character)
• Creating and manipulating vectors
• Writing while() and for() loops
• Building matrices with matrix(), rbind(), and cbind()
• Installing and managing R packages
• Customizing the RStudio environment
• Foundational statistics concepts, including the Law of Large Numbers and the Normal distribution
• Applying R to sample financial and sports datasets

Course Structure

The course is organized into 9 sections totaling 77 lectures and roughly 10 hours 32 minutes of video, plus 2 supplementary articles. It opens with an installation walkthrough and a warm-up exercise before moving into programming fundamentals, data structures, and applied statistical practice.

Who Is This Course For?

It's aimed squarely at people with zero coding or statistics background who've bounced off R before because of its learning curve. If you've already used R for a project or two, or you need advanced topics like machine learning modeling, this is likely too basic — the syllabus stays at foundational syntax and structures rather than pushing into predictive modeling or advanced statistical methods.

Format & Time Commitment

It's fully self-paced, on-demand video with lifetime access, so there's no cohort schedule or deadline pressure. At roughly 10.5 hours of core video plus homework exercises, it's a manageable weekend-to-two-week commitment depending on how much practice time you build in — and the course explicitly expects you to do homework, not just watch.

Pros and Cons

Pros

• Genuinely beginner-safe — assumes nothing going in
• Builds concepts incrementally instead of front-loading jargon
• Includes real datasets (financial, sports) instead of only toy examples
• Lifetime access and a completion certificate included
• Frequently discounted well below list price

Cons

• Stays introductory throughout — no coverage of advanced modeling, machine learning, or tidyverse-style workflows
• One recent learner reported that a homework dataset download was corrupted, suggesting file maintenance can lag
• No live instructor interaction or structured deadlines, which some beginners need to stay accountable
• Udemy certificates carry limited weight with employers compared to accredited or platform-verified credentials

FAQ

Is any prior experience required? No — the course is explicitly built for people with no programming or statistics background.

How long does it take to finish? The core video content runs about 10.5 hours, spread across 77 short lectures, so it's realistic to finish within a couple of weeks at a relaxed pace.

Is the certificate worth much? It's a standard Udemy completion certificate — useful as a personal milestone or portfolio note, but it's not an accredited or industry-recognized credential.

What if I want to go beyond the basics afterward? The instructor's platform lists a follow-up option, "R Programming: Advanced Analytics In R For Data Science," for learners who finish this one and want to go further.

Check the current price and start date on the official Udemy course page before enrolling.