Google Data Analytics Certificate: What Worked, What Didn’t & What’s Next

-

Last week, I wrapped up the Google Data Analytics Professional Certificate, and I wanted to take a moment to share my experience and thoughts. This course marked my first big milestone in my career transition into data analytics, so I thought it would be helpful to reflect on what I learned, what worked well, and what didn’t.


This post covers my experience with the course up until the final case study—I’ll be dedicating a separate post to the capstone project next week.

What I Liked About the Course

A Focus on Soft Skills

One thing that really stood out to me about this course was its focus on soft skills before diving into technical ones. Instead of immediately jumping into tools like Excel, SQL, and Tableau, the course started with topics like effective communication, collaboration, and how to ask good questions.

As someone with no prior experience in a corporate job, I found this approach really helpful. It gave me a better understanding of what to expect in a professional setting and the kind of skills I should be developing beyond just the technical side. It also made for a much smoother introduction compared to other courses I've taken, which tend to dive straight into hard skills. It felt like a good balance and a gentler start to the journey.

Repetition & Pacing

When I first started, I thought the course was a little slow and repetitive. However, as I progressed, I actually came to appreciate this. The repetition helped reinforce key concepts, and I didn’t feel the need to constantly go back and review.

I also found the pacing to be pretty manageable—especially since I watched most of the videos at 2x speed. This made the learning experience feel more fluid and less time-consuming.

Encouraged Reflection

Another thing I really enjoyed was how the course encouraged moments of self-reflection. There were times when I had to pause and think about what I’d just learned, which led me to ask more questions. These moments of reflection were valuable in helping me connect the dots and make the material stick.


What I Didn’t Love

SQL Felt Disorganized

While the course was generally well-structured, the SQL section felt a bit scattered. Concepts were introduced quickly, without enough explanation, which meant I had to keep moving forward, assuming they weren’t important. Only later did I realise that these concepts were actually essential.

I found myself relying heavily on ChatGPT to fill in the gaps, and when I finally got comfortable with a particular concept, I’d discover a video explaining exactly what had confused me earlier. This made me feel like the sequence of topics could have been better organised.

Tableau Wasn’t Practical Enough

By the time I completed the course on data visualisation, I didn’t feel confident in using Tableau at all. The lessons felt more like “follow along and copy this” rather than teaching the why behind each action. While I understood what I was doing as I worked through the exercises, when I tried to apply the knowledge on my own, I quickly realised I was still very much a beginner.

I’ll need to do more learning outside of this course to really feel comfortable using Tableau. The course was a decent introduction, but it didn’t go deep enough for me to use the tool confidently in real-world scenarios.


How Long Did It Take?

I started the course just after Christmas and finished in exactly two months—though I took about two weeks off for travel and other commitments. So, overall, I’m happy with the pace.

Google estimates that the course will take around six months to complete, but I found that to be much longer than necessary. Here’s how my time broke down across the different courses:

  • 8 hours – Course 1: Foundations: Data, Data, Everywhere
  • 12 hours – Course 2: Ask Questions to Make Data-Driven Decisions
  • 9 hours – Course 3: Prepare Data for Exploration
  • 11 hours – Course 4: Process Data from Dirty to Clean
  • 14 hours – Course 5: Analyze Data to Answer Questions
  • 11 hours – Course 6: Share Data Through the Art of Visualization
  • 15 hours – Course 7: Data Analysis with R Programming
  • 26 hours – Course 8: Google Data Analytics Capstone: Complete a Case Study

Total: 106 hours (compared to their estimate of 260 hours)

I also took detailed notes throughout the course, which ended up being a 120-page document that I can always refer back to if I need to refresh my memory on any topic.


Final Thoughts

Google markets this course as an all-in-one solution to take you from beginner to job-ready, but after reading reviews, I knew that wasn't going to be the case—and I completely agree with that.

That being said, I still think it was an excellent starting point. I wanted a structured way to learn the fundamental skills of data analytics, and this course gave me exactly that. It introduced me to key tools like Excel, SQL, Tableau, and R all in one place. Even though I’ll need to keep learning—especially in Tableau and SQL—it provided a solid foundation and a clear roadmap for what I need to focus on next.

Is this the best or only course for aspiring data analysts? Probably not. But for me, having a comprehensive, all-in-one resource was incredibly valuable. It took away the guesswork of what to learn first, gave me a clear timeline (even though I finished much quicker than expected), and gave me a well-rounded introduction to both the technical and soft skills needed in the field.


What's Next?

As I look ahead, I now have a clear sense of what I need to improve on, and I feel much more informed about the industry as a whole. Beyond technical skills, this course helped me understand the importance of things like online presence (like LinkedIn and this blog), what data analyst interviews might be like, and what the role itself entails.

In my next post, I’ll break down the final capstone case study—what I worked on, how I approached it, and the key takeaways I gained from that experience. 

Comments

Post a Comment

Popular posts from this blog

My thoughts on DataCamp's Associate Data Analyst in SQL Career Track

-
Image
I started Data Camp's Associate Data Analyst in SQL career track at the end of March and expected it to take about 39 hours, since that’s what the estimate said. But in the end, it was closer to 45 hours and honestly, it was worth it. Not just because I finished the course or got the certification, but because of how much more clear SQL feels now, especially compared to when I was learning it for the first time during the Google Data Analytics course. Learning the Order of Execution One of the most helpful parts of this course was the way it explained concepts in a logical order. In particular, understanding the order of execution helped me finally get the difference between WHERE and HAVING. During the Google course, I couldn’t figure out why one worked and the other didn’t. It was never clearly explained that the filtering happens at different stages: WHERE filters rows before the grouping, and HAVING filters after. Once I understood the order of execution, it just clicked! M...
Read more

Reflections on the Stanford Statistics Course: How Statistics Can Reveal or Distort the Truth

-
Image
At the end of February, I took the next step in my data analytics journey: the Stanford Introduction to Statistics Course on Coursera. Since I had just completed the Google Data Analytics Professional Certificate Course that same day, I couldn't help but find myself comparing the two. What I didn’t expect was just how different the experience would be. A More Intense Learning Experience The course is labelled as beginner-friendly and estimated to take around 14 hours to complete. In my case, it took closer to 18 hours—partly because the content was quite dense and partly because I wanted to ensure I truly understood each concept before moving forward. With the Google course, I was able to move quickly, watching most sections at 2x speed. That wasn’t an option here. I frequently had to pause, re-watch explanations, and work through my own examples to fully grasp the material. While some concepts were familiar from previous studies, I aimed to go beyond just recalling formulas. I w...
Read more

Clarifying Commonly Confused Stats Concepts

-
Image
In a previous blog post about the Stanford Introduction to Statistics course, I mentioned that I’d be revisiting a few topics that took me a little longer to wrap my head around. This post is exactly that. A way for me to break down topics, not just to clear things up for myself, but also to create something I can revisit whenever I need a quick refresher. So here are the topics I'm going to cover: Law of Large Numbers vs Central Limit Theorem Significance Level vs. P-value Confidence Level vs Confidence Interval Standard Deviation vs Standard Error Z-test vs T-test 1. Law of Large Numbers (LLN) vs Central Limit Theorem (CLT) Both are foundational ideas in statistics and often mentioned together, but they’re doing two very different jobs. LLN says that as your sample size increases, your sample mean gets closer to the true population mean. CLT explains that regardless of your population’s distribution, the sampling distribution of the sample mean becomes approximately normal a...
Read more