The Google Data Analytics Professional Certificate: A Reflection
By: J Edgar Mihelic
Tags: Google, Google Analytics, Data Science, Coursera, Data Analytics

I need to start this out by pointing out that I am a nerd. My wife jokes that I “study for fun.” I have always liked learning new things.

Image by StockSnap from Pixabay

I also have an MBA and a Master’s in Economics from schools in the Chicago area, but ones that unless you’re from the area you most likely haven’t heard of. It was while I was working on the econ degree that I realized that I was missing some quantitative skills that I would really need if I were going to up my game and maybe apply for PhD programs in economics. The degree I was working on wasn’t as focused on the quant side as I would like, and the program was small so there were not a lot of tracks. I reached out to my professors and I became friendly with one of the finance professors who did a lot of work in R. The problem is that it’s hard to stack new skills when you’re working full time and going to school.

I then wrote a wish list that I still have on my computer at work. It’s a list of things I want to learn and do: “Learn programming; R*; SQL; Excel Better; Python?; Stata; Relearn Calculus; Work on Poetry.” You can see my interests are broad there. I took some time to work on an R course on Coursera through Johns Hopkins and it didn’t work for me. I liked the idea of MOOCS from early on but that was my first taste and there was too wide a gulf between what was presented in the course and lessons and then the assignments that were evaluated. I couldn’t begin to approach the problems without Googling and that felt like cheating. So, of the sequence I only did a couple of the courses over the summer.

But I didn’t abandon that list — I graduated in the spring of 2020 with a MA in econ and plenty of time, so I started a sequence again in Coursera this time from Duke in the Excel to SQL track. That one I liked since it had a lot of hands-on work and the assignments didn’t make me feel stupid. The only drawback to it was that the program underestimated how much time each week’s lessons would take.

I was in the middle of the fourth course in that sequence, learning SQL when I got an email from Google that they were offering a data analytics track. I logged onto their site and watched the presentation, and I knew I wanted to be a part of it. It seemed like a no-brainer. The course sequence was laid out to be an eight-course program that could be done over about six months, and you just had to pay the Coursera fees.

Via Coursera

The basic framework the classes teach is the six steps of the data analysis process: ask, prepare, process, analyze, share, and act. They reinforce that in every class and in most of the little lectures. There are bookend classes but the middle six classes each focus on one of the steps. There are software programs that they teach along with the classes. For spreadsheets they go through the Google Sheets steps, but they also provide the details in excel. During visualization, the go through the steps for the online version of Tableau. In the course for SQL, you log onto the Google Cloud platform Big Query and play around with datasets that are available and walk through uploading your own data. There is a class in R that takes you through downloading R and R Studio and creating your own fancy charts and graphs with ggplot2.

Overall, the courses were not that difficult for me. Based on work and school experience I had familiarity with the software and programming languages they used. That made me able to just burn through the program. The six-month program I was able to finish in about a month. They released the first six classes in the middle of march and in about three weeks I got through those. They didn’t release the last two classes, the course for R and the capstone, until April and it took only a few days to get through those. I was able to do them quickly, but I did invest the time. I watched the videos and did the journals. I participated in the discussion board and connected with people going through the program on LinkedIn, building the network that might be helpful in my future journey (Join my network!). The evaluations were not that hard, multiple choice questions and you can retake them if you fail.

You can get a lot out of the process. Though it wasn’t a huge challenge to me, I think I really benefited from working through that “ask, prepare, process, analyze, share, and act” framework since it was basically my existing process, but it gives you a name for what you’re doing. It also made me excited for data analysis, giving me a lot of jumping off places for when I want to learn more. The courses are supposed to be set up to be laid out for a beginner, and I think it would work. The videos are accessible and not full of jargon and fun to watch. The hands-on activities won’t make you quit like the ones that made me quit that first foray into R programming. Most importantly if you finish you have Google on your LinkedIn and CV, and they have resources available for people who are trying to climb the ladder. It was worthwhile for me, and I hope it can benefit others. I think there were a lot of takeaways for me to grow in my current role. I’m excited for what’s next.