Google Data Analytics Certificate Course 4 Review: Process Data from Dirty to Clean

Google Data Analytics Certificate Course 4: Process Data from Dirty to Clean

If there's one thing that separates theory from reality in analytics, it's dirty data.

Course 4 of the Google Data Analytics Certificate—"Process Data from Dirty to Clean"—tackles this head-on. I gave it a well-deserved 5 out of 5 stars.

Here's why.

Why Data Cleaning Matters More Than You Think

Anybody who does analytics knows: dirty data is life. I should get a shirt that says that.

Being able to clean dirty data, recognize it early, and deal with all the issues that come with it is imperative for this career.

You'll spend 60-70% of your time on data cleaning. Courses that skip this step are doing students a disservice.

Week 1: Before You Clean, Check for Integrity

The course starts with a crucial foundation—data integrity.

Before you even think about cleaning, you need to:

Understand data constraints and compliance issues
Know when you have sufficient data to proceed
Recognize when data is fundamentally unreliable

One insight I appreciated: This is the "analysis before the analysis."

When someone asks me a question, I'm processing two things simultaneously:

Do I understand the question being asked?
Can I address it with the data that's available?

If the data can't answer the question, no amount of cleaning will fix that.

Week 2: All About Clean Data

This section breaks down the types of dirty data you'll encounter in the wild:

Duplicate data – Same records appearing multiple times
Outdated data – Information that's no longer current or relevant
Incomplete data – Missing fields or partial records
Inconsistent data – Conflicting information across sources
Incorrect data – Just plain wrong (typos, wrong values, etc.)

The course gives you a practical framework: if your data has one of these problems, here's the type of "garbage out" you'll deliver to the business.

Garbage in, garbage out isn't just a saying—it's a painful reality every analyst learns.

Week 3: Cleaning Data in SQL

Now we get practical with SQL cleaning techniques.

The course covers:

Different SQL dialects (MySQL, PostgreSQL, BigQuery)
When to use SQL vs. spreadsheets (hint: larger datasets favor SQL)
Debugging SQL code (essential skill nobody teaches explicitly)
Widely used cleaning queries (TRIM, CAST, COALESCE, etc.)

One common question I hear: "I learned MySQL—how hard is it to learn PostgreSQL?"

Course 4 addresses these dialect differences directly. The syntax is 95% the same. The concepts transfer completely. Don't let dialects intimidate you.

Week 4: Verify and Report Your Cleaning Results

This is where many analysts skip steps—and where good analysts separate from great ones.

The course emphasizes:

Data cleaning verification checklists
Change logs and documentation
Version control best practices
How to communicate what you've changed and why

Documentation isn't glamorous, but it's essential.

When someone asks "what did you do to this data?" three months from now, you need to have answers. Your future self will thank you.

Week 5: Adding Data to Your Resume (Optional)

Google continues building out the professional development angle with resume tips, LinkedIn optimization, and career guidance.

This content is valuable, especially for career changers who don't know how to translate their new skills into interview-ready language.

My Verdict: 5 Stars

This course hits close to home for anyone who's spent time in the analytics trenches.

The quizzes were challenging and thoughtful—not just regurgitating definitions, but applying concepts to realistic scenarios.

If you're pursuing the Google Data Analytics Certificate, Course 4 gives you skills you'll use every single day.

Not every day you'll use R programming. Not every day you'll build dashboards. But every single day? You'll be cleaning data.

The Bottom Line

Remember: Garbage in, garbage out. Course 4 teaches you to stop the garbage at the door.

This is one of the most practical courses in the entire certificate program. Don't skip it. Don't rush through it. Actually practice the techniques.

Your future career depends on mastering this unglamorous but essential skill.

Common Questions About Course 4

Q: Is data cleaning really that big a part of the job?

Yes. I've been doing this since 2004. Still spend 60-70% of my time on data wrangling. Every analyst I know reports similar numbers. It's the reality of the work.

Q: Can't I just learn data cleaning on the job?

You'll learn some of it on the job, but having foundational knowledge beforehand is huge. You'll be productive faster and make fewer costly mistakes.

Q: Do I need to master every cleaning technique in this course?

No. Get comfortable with the most common ones (handling nulls, deduplication, type conversion). The rest you can reference as needed. This isn't about memorization—it's about building your mental toolkit.