Google Data Analytics Certificate Course 3 Review: Prepare Data for Exploration
Course 3 is where you finally start writing SQL queries and touching real data. Here's what to expect and how to succeed.
Google Data Analytics Certificate Course 3: Where You Start Actually Using Data
Course 3 of the Google Data Analytics Certificate focuses on data preparation—arguably the most time-consuming part of any analyst's job.
This is where the certificate transitions from theory to practice. You're finally writing queries and touching real data.
What Course 3 Covers
- Data types and structures - Understanding different data formats (structured, unstructured, etc.)
- Data integrity - Ensuring accuracy, completeness, and consistency
- Databases and metadata - How data is organized and documented
- SQL introduction - Your first actual queries
- Data cleaning basics - Handling errors and inconsistencies
The Good
SQL Foundations
This is where you start actually querying data. The SQL introduction is gentle enough for beginners while covering essential operations like:
- SELECT, FROM, WHERE
- Basic filtering and sorting
- Introduction to JOINs
This is the most valuable part of the course. SQL is the universal language of data. Master it here.
Data Integrity Focus
The emphasis on data quality is crucial. I've seen too many analysts skip this step and end up with flawed insights.
"Garbage in, garbage out" isn't just a saying—it's a painful reality. This course teaches you to be skeptical of your data before trusting it.
BigQuery Introduction
They use BigQuery (Google's cloud data warehouse) for SQL practice. This is smart because:
- It's free to use with sample datasets
- The interface is clean and beginner-friendly
- It's used in real companies
The Challenges
BigQuery Learning Curve
BigQuery is great, but it can be overwhelming if you've never touched a database before. The UI, the project structure, the sample datasets—there's a lot to absorb simultaneously.
Don't panic. Focus on the SQL syntax, not the tool. The syntax transfers to any database.
Limited SQL Practice
You get the basics, but real proficiency requires more practice than this course provides.
After Course 3, you should be able to write simple queries. You won't be job-ready yet. That's okay—more practice comes in later courses.
Metadata Overload
There's a lot of discussion about metadata (data about data). It's important context, but it slows down the momentum of actually working with data.
I get why they include it. Just know that the exciting part is the SQL, not the metadata theory.
My Recommendation
Course 3 is where the Google Data Analytics Certificate starts getting practical. Take your time here.
Practice the SQL queries repeatedly. Don't just watch the videos—type the queries yourself. Change the filters. Try different columns. Break things and figure out how to fix them.
Don't rush to Course 4 until you're comfortable with basic query syntax.
The Make-or-Break Moment
If SQL clicks for you in Course 3, that's a great sign for your analytics career.
If it feels completely foreign and frustrating, don't give up yet. SQL is learnable. It just takes more reps than one course can provide.
Common Questions About Course 3
Q: Do I need to know SQL before starting Course 3?
No. They teach you from zero. But if you've seen SQL before, this will feel like review.
Q: What if BigQuery confuses me?
Focus on the SQL syntax in the editor, not the BigQuery interface. You're learning SQL, not BigQuery specifically. The syntax works in any database.
Q: How much time should I spend on Course 3?
Google estimates 21 hours. Budget more if SQL is new to you—closer to 25-30 hours with practice. This is foundational stuff worth getting right.
Excel for Analytics
The complete course for finance professionals who want to level up their Excel skills.
SaaS CFO turned educator. 20+ years in finance leadership, from Big 4 audit to building companies. Now helping 250,000+ professionals master the skills that actually move careers.