Google Data Analytics Course Review
Updated: Jan 30
I received my Google Data Analytics Professional Certificate, and have written a review of the course. I had no previous experience or knowledge of data analytics/analysis before this course so this review comes from a complete beginner in the field.
As of January 30th, 2023 I've updated my review and reorganized it. While I still think this course is a good introduction for new analysts I don't think it's as great as Google made it out to be. And what I thought it was initially. I have some new thoughts/insights about the course. Which you can read about at the end. My general thoughts on each individual course remain the same.
The program is broken up into 8 different courses. For each course I gave an overview then added my own thoughts.
If you would like to skip to my overall thoughts click here.
Each course is broken down into weeks, and then subsections within those weeks that go over specific topics. Most of the material is either readings, videos, quizzes, discussions, or mini projects where you practice a specific skill you learned beforehand and try it either in Qwiklabs (a program Google uses so their students can practice skills) or projects you download and work on your own. At the end of each Course there is a larger Course challenge. While it is completely self-paced, Google suggests it should take you 6 months to complete while working on it 10 hours per week. Though most people should be able to complete it within a month or two. I took my time and did the 6 months, while I did get a better understanding of the concepts, looking back it wasn't necessary to take all that time. Especially since you paid on a monthly basis. If you take the course there's no need to go through the "suggested" timeline.
Course 1: Foundations: Data, Data, Everywhere
This course was the introductory course, it talks about what a data analyst does and the basic process of data analytics. It begins to showcase the tools data analysts use like SQL and Excel. It also went over the analytical skills necessary for data analysts and the life cycle of data. The videos were easy to follow along and good introduction to the world of data and analyzing it. It had a lot of videos and readings, and a fair number of "assignments" where it asked you a few questions and you provided answers.
I enjoyed this first course, it was quite beginner friendly and walked through how the course worked. And a general overview on what a data analyst does. There's nothing too complicated in this course and it can be completed quite quickly. I wouldn't focus too much on this course, especially with the suggested reads and "assignments". Watch the videos and then move on.
Course 2: Ask Questions to Make Data-Drive Decisions
This course focused on the first process of analyzing data: Ask. You learn how to ask effective questions to help make data-drive decisions, which is the main point of data analytics. Most of this course felt pretty basic, it went over Excel/Google Sheets but only the basic functions like functions and formulas. The course had a heavy focus on stakeholders and their expectations, and gave advice on that.
This course was pretty basic if you've had previous experience with excel and a solid foundation of mathematical skills. It wasn't difficult but it went over some necessary concepts. This course mostly had videos and it was mostly "lecture style". It's not great if you want more hands on practice with Excel.
Course 3: Prepare Data for Exploration
This course first went through the second step in analyzing data: prepare. It went over how data is collected and the different types of data formats. And it discussed bias, credibility, privacy and ethics within data. It goes into data types and structures as well as some basic SQL practices like the standard statement (SELECT, FROM, WHERE). The course talked about bias, how to avoid it, and how to organize and protect the data that you're working with. We were introduced to Qwikilabs where we could actually practice the skills we learned,
While the idea of Qwikilabs were good, being able to practice the skills learned, the execution was not. When I took the course (which was in March of 2021) the instructions were confusing and the platform would freeze up and not work. It was good for basic problems and concepts but don't expect to get in-depth practice from this.
Course 4: Process Data from Dirty to Clean
This course went through the third step, process. It goes through some data-cleaning tools and techniques and discussed the difference between clean and dirty data and why it matters. Even though it focused on making sure the data was clean, or free of errors, it felt like I was starting to begin the data analysis process. We learned more intermediate functions in spreadsheets, specifically Google Sheets, like: TRIM, LEN, and CONCATENATE. And we dove into SQL some more. There was more opportunities to practice our skills with "practice quizzes" aka mini assignments and Qwikilabs. This course finished off with a helpful section with resume advice.
The main thing I'll praise is the instructor Sally (you can find her on LinkedIn), you could feel her passion for SQL radiating off the screen, it made this lesson much easier to watch. She was one of the best instructors of the entire program. The downside of this course was it didn't give you enough practice for SQL. I didn't feel like I had a good handle on SQL at all after this. But it was the first time it was introduced so that also factors in.
Course 5: Analyze Data to Answer Questions
This course got into the fourth and most well known step, analyze. I finally felt like I was able to apply what I've learned to actual data analysis. I worked with real data and analyzed it. Everything I had learned previously was coming together. But it was quite a lot especially with all of the calculations I learned for spreadsheets like COUNTIF, SUMIF, Pivot tables. We were also introduced to JOINS and the different types of JOINS in SQL. And we performed data calculations and learned about aggregate functions.
It was overwhelming with the ways of analyzing the data. Instead of focusing on one took like Excel or SQL, the course would jump around between the two tools and it could get confusing. Everything learned in this course is essential for each tool: Excel with COUNTIF, SUMIF, and Pivot tables; and in SQL using JOINs and aggregating data. I was able to get a little bit of practice but I would've liked more practice scenarios and problems. It was a lot and I wouldn't expect anyone to have a full grasp of the concepts from this one course. It was good as an introduction.
Course 6: Share Data Through the Art of Visualization
This course introduced me to step number five, share. This is the data visualization process, it goes over basic principles of design, elements of art, and the different types of graphs. It also went over different data visualization types like: change over time, between objects, data composition, and relationships. The course introduced us to Tableau, a popular data visualization tool. And it went through basic visualizations with Tableau.
While we had a few opportunities to practice most of the practice was in the first two weeks and after the course focused on presentations. I would've loved more opportunities to practice with Tableau and while some of the presentation tips were helpful, specifically ones that dealt with presenting data, most of them weren't new. This was the course I liked the least. I felt underwhelmed by the information provided. There was no way I could create my own dashboard (which is common in data visualization) in Tableau only with this course.
Course 7: Data Analysis with R Programming
This course is a basic introduction to R programming. As a note, I already learned a bit of R while in college during my mathematical model class so this wasn't entirely new for me, mostly a review. It goes over functions, variables, comments, vectors, pipes, and data types. While it's a relatively basic introduction to R, it gives you enough to be able to create visualizations. And the last section was about R markdown,
There were quite a bit of hands on activities, much more than the last course, and I liked practicing my skills with R. I felt like I had more time to practice because you could also follow along with the videos. But my main criticism (which is shared by many) is why Google decided to teach R instead of Python. Python is much more popular for data scientists/analysts. While R is used it is less common and more difficult to learn. I would've preferred to learn Python instead.
Course 8: Google Data Analytics Capstone: Complete a Case Study
This course went over what a capstone project is and gave you two tracks to follow. Track 1 you chose a business question similar to the kind that interviewers might ask. And there were several different options to choose from, with specific business tasks and datasets to use. Track 2, was more "free", you choose a public dataset, one that you're personally interested in, and answered questions you came up with.
I did Track 1 for my portfolio project. Overall this project took me over 40 hours to complete and here is my detailed article on it, you can view that here. I went through the entire process:
Ask - I was given a given a business question to answer
Prepare - I prepared my data (downloaded it for use)
Process - Cleaned my data and looked for errors.
Analyze - Analyzed my data using Microsoft Excel for my initial analysis then transferred my data to R for further and more detailed analysis
Share - Using Tableau to create charts and then created a dashboard. For the background and overall design elements I used Figma.
Act - In the article I included insights on how this information is useful to the marketing team and the overall goal of converting casual riders to annual members.
I've heard from many others that they felt like the course did not provide them enough tools/help to complete the case study. I think the reason why I was able to complete the project was because of my math background and experience with modeling and using R. Otherwise I think this project is too overwhelming and difficult for someone with just this course to take on.
This course, not including the project, took me 6 months to complete complete. Keep in mind I took detailed notes throughout the course and made myself a study guide for the entire course. I didn't want to be passive in my learning. But you are able to skip through the lessons, since it's all online and self paced there's no instructor to ensure you're processing all of the information. Looking back, that was unnecessary and I took too long. I think it should take at most 3 months but most people can complete it within 1-2 months.
I did enjoy the course, it presented the information in easy-to-understand ways. Someone without a mathematical or technical background should be able to get through this course if they put the time in it. It gave a basic overview of what a data analyst is but it went through the common tools used by data analysts such as: SQL, Tableau and Spreadsheets.
But I do have a few criticisms:
There was not enough practice with Tableau. It would be incredibly difficult for anyone to create a comprehensive dashboard with just this course.
The course jumped around a lot between tools and it could get confusing.
I wish Python was the language taught instead of R (which I like using). This course prides itself in making someone "job ready" (more on my thoughts on that later). Most data analyst jobs require Python rather than R. Though in the end it is preference by the company.
It did not prepare the person to complete the capstone project. Just with this course it would be difficult to complete the project. It's an overwhelming first project. A "guided project" would've been better for this.
The biggest criticism I have. This program will not get you job ready. While it claims to do that, many people, myself included feel like that's not the case. You will probably need to supplement your learning on each of the skills (SQL, Tableau, and R) after this course. I remembered nothing from SQL and took another course to refresh my memory. There's a lot more to getting a data analyst job than this program, especially if you're switching careers. A few things include: creating a portfolio; revising your resume; and networking.
If you're interested in what I'd recommend as a learning path for data analysts check out my article. Also if you'd like to find other resources for learning about data analytics, where to host your portfolio and what sites I use to practice my skills check out my article.
I would recommend this course to anyone who is potentially interested in data analytics. Or who want to learn more at a relatively low-cost and time commitment. As long as you don't think this program will get you a job and realize it's best to use as an introduction only.