This happened a little while ago, but I took and completed the "Google Data Analytics Professional Certificate" using the company's certification support program.
Along with my impressions from taking the course, here's a quick overview of what you can learn and how difficult it is!
What Is the Google Data Analytics Professional Certificate?
This is an online learning program offered by Google that covers everything from the basics to advanced applications of data analysis, divided into eight courses.
- How to Approach Data Analysis
- Organizing Analytical Data
- Using Spreadsheets
- Data Organization with SQL
- Creating visualizations with Tableau
- Using R Markdown
and more, covering a fairly broad range of skills.
The guideline is "about 6 months at 10 hours per week," but I went at a relaxed pace and finished in about 4 months. If you focus, I think you can complete it in even less time.
The courses are taken through the Coursera platform on a monthly subscription. Free enrollment campaigns are offered from time to time, so it's also worth watching for those.
There is a one-week free trial, so it's a good idea to use that period to decide whether you can keep up with the course. Please note that the subscription renews automatically after the trial ends!
You can register from the Coursera "Google Data Analytics Professional Certificate" page and start right away.
How the Learning Works
Each course is made up of multiple modules. You complete all the learning materials (videos, documents, and so on), take a quiz for each module, and once you pass the assignments for all courses, you receive a certificate of completion.
The documents include links to related tools and external materials, which help deepen your understanding. However, if you try to read through everything carefully, you'll never have enough time, so I took the approach of noting down key terms and links first and looking them up later.
You can pass the tests with this approach too!
There's a lot of material, but the course is generously designed so you can mark items as "Complete" whenever suits you, which really helped.
Difficulty Level
Hearing "SQL" or "R" might make you a bit wary, but the course walks you through everything from the basics, so it wasn't as difficult as I expected.
If you read the course materials thoroughly, you can pass the tests. And even if you don't reach the passing score, you can retake them. It's designed to be very considerate.
There is a daily limit on how many times you can take the comprehensive review tests, but the limit resets after 24 hours, so in practice I could take them as many times as I wanted!
Tools like BigQuery, Tableau, and RStudio appear, even though you may not normally use them. Don't worry, though, because the course explains everything from installation to usage.
Also, the tools used in the course are designed to stay within free tiers, so apart from the Coursera monthly subscription, you won't need to spend any extra money, which was easy on the wallet.
In a different sense, I found the translation feature to be the more challenging part.
The materials are mostly in English. Of course, videos, documents, and tests all come with a translation feature, and its accuracy is quite high, but occasionally words with similar meanings were translated into the same Japanese word.
Multiple-choice questions sometimes had two options that looked exactly the same, which made things tricky in their own way.
Introduction to Each Course
With that said, let's take a look at what you'll learn in each course.
[Course 1: Data Is Everywhere] As the first course, this is a mostly lecture-based introduction to the fundamentals of data analysis. You'll learn the full process data analysts follow when approaching an analysis. What you learn here will form the foundation for everything in Course 2 and beyond.
[Course 2: Asking Questions to Drive Data-Informed Decisions] This course focuses on the questions that help you dig deeper into the problems you need to solve. You'll learn interviewing and communication techniques, as well as how to share analysis results.
Now the hands-on learning really begins! You'll study essential spreadsheet functions and error types, knowledge you'll use often in real-world work.
This is a course that moves along relatively smoothly.
[Course 3: Preparing Data for Exploration] This course covers the process of preparing data for analysis. You'll learn about data structures and collection methods, and understand the concept of "clean data," which is central to Course 4. In addition to spreadsheets, you'll also work with data manipulation using SQL in BigQuery. This is where you'll encounter your first real challenge.
If you don't build a solid foundation, you may stumble later on, so we recommend taking your time and making sure you understand each concept as you go.
[Course 4: Turning "Dirty" Data into "Clean" Data]
You'll learn data cleaning methods that are essential for improving the accuracy of your analysis.
After understanding why cleaning is necessary and what happens when you analyze dirty data as-is, you'll get hands-on experience with data cleaning using spreadsheets and BigQuery.
There were many functions and queries, so it was quite a challenge.
[Course 5: Analyzing Data and Drawing Conclusions]
Putting into practice the "data preparation" and "cleaning" steps you've already learned, you'll now process the data. This is the phase that could be called the heart of the program, where you narrow down data by specific conditions, calculate averages, and perform various computations to uncover the facts and trends hidden within.
[Course 6: Sharing Data Through Visualization]
You'll learn how to visualize data with Tableau and share it effectively. Even when following the video instructions step by step, errors often appeared and changes didn't take effect, and I personally struggled quite a bit.
The tool is designed to be intuitive and usable without specialized knowledge, but honestly, I still don't feel confident using it well...
[Course 7: Data Analysis with R]
This is where you finally get into data analysis using the R language. Since it starts with installing R, you shouldn't have trouble if you follow the course materials. However, once you move into the analysis and visualization phases, unfamiliar terms and concepts come up rapidly, so be careful.
The range of visualizations you can create goes well beyond Tableau, and the moment you get the hang of it, it becomes really fun. Being able to complete the reporting process entirely in R was also very appealing.
Personally, this was my favorite course.
[Course 8: The Final Assignment to Wrap Up Your Learning]
This is the culmination of everything! Drawing on what you've learned so far, you'll take on a data analysis project on your own. Working through several case studies, you'll build your own output step by step.
The second half of the course mainly focuses on job hunting as a data analyst. You can learn by watching simulated interview scenes, but since the atmosphere is quite different from Japanese interviews, it's probably best to treat them as a reference only. To be honest, I mostly skimmed through them.
Summary
I've gone on at length, but how did it all sound?
I've packed in points that I personally would have loved to know before enrolling, while being careful not to give away the course content.
My honest impression after taking the course is that it's an accessible certification for anyone aiming to break into the web industry from scratch, whether for a first job or a career change. You may not use it much in day-to-day work at a production company, but when the time comes, being able to do data analysis is always better than not being able to.
The course walks you through even tools you'd never normally touch, from setup to basic usage, so people new to the web industry can study with confidence. That said, those who want to dig into more advanced techniques might find it a bit light. The Google Data Analytics Professional Certificate may not be widely known, but being able to publicly prove that you have Google-certified data analysis skills is a strong advantage in my view.
If you're interested, you might want to start with the free trial.
Currently working hard as a director who rose from new graduate hire! I'm striving to become a multi-talented professional who masters every field, from design to ad operations. At work, I aim for smooth, efficient direction, while in my private life I enjoy a homebound lifestyle in my fortress of a home. I happen to love anime.
Handan-chan
Web Director / Joined in 2022