Google Data Analytics Certificate Course 7 Review: Data Analysis with R Programming

Google Data Analytics Certificate Course 7: The R Programming Reality Check

Course 7 introduces R programming—a significant step up in technical complexity from the previous courses.

I've been in analytics since 2004, mostly using Excel and SQL. R was new territory for me when I took this. Here's my honest take.

What Course 7 Covers

R fundamentals - Variables, data types, basic syntax
Tidyverse introduction - Modern R data manipulation (dplyr, ggplot2)
Data cleaning in R - Practical data preparation workflows
R visualization - Creating charts with ggplot2
RStudio - The development environment most R users rely on

What I Liked

Practical Focus

The course uses realistic data and focuses on practical applications rather than theoretical programming concepts. You're learning R by doing actual analysis, not abstract coding exercises.

Tidyverse Approach

Teaching the tidyverse (dplyr, ggplot2) is absolutely the right call. It's modern, readable, and widely used in the R community. If you're going to learn R, learn it this way.

RStudio Walkthrough

They introduce RStudio early and show you around. The environment can be intimidating at first, so this guidance is valuable.

My Concerns

The R vs Python Debate

Here's the uncomfortable truth: the analytics job market leans more toward Python than R.

I'm not saying R is useless—it's powerful for statistical analysis. But most job postings ask for Python. Learning R isn't wasted effort (the concepts transfer), but know that you'll probably also need to learn Python eventually.

Compressed Timeline

Programming takes time to sink in. This course compresses a lot into a short period. Don't expect to be proficient after one pass.

I had to go back through sections multiple times. That's normal. Don't feel discouraged if it doesn't click immediately.

Should You Skip Course 7?

If you're completing the full Google Data Analytics Certificate, don't skip this. Here's why:

Programming exposure - Even if you end up using Python, the concepts transfer
Statistical thinking - R forces you to think about data structures and transformations
Portfolio value - Being able to say you know R (even basics) helps in interviews

Just know that this is the steepest learning curve in the certificate.

The Bottom Line

Course 7 is challenging but worthwhile. R programming opens doors to more sophisticated analysis techniques.

My advice: Take it slow. Practice the code examples yourself. Don't just watch—type it out. That's how it sticks.

Common Questions About Course 7

Q: I'm struggling with R. Should I just skip to Python instead?

Finish the course. The struggle is part of learning programming. Python will feel easier after you've wrestled with R concepts.

Q: How much time should I spend on Course 7?

Google estimates 26 hours. Budget more—closer to 35-40 hours if programming is new to you. Don't rush it.

Q: Will I be job-ready in R after this course?

No. You'll have foundational skills. Job-ready means building projects, practicing regularly, and going deeper. This course opens the door—you have to walk through it.