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What I'm Learning About Data Analytics
I finished up Google's Data Analytics Certification over on Coursera earlier this week, and I'm happy to have finished for a few reasons.
Having completed anything always provides me with a nice sense of intrinsic euphoria. I like doing stuff. And whenever I begin something, I need to know there's a finish line out there somewhere.
This is the first certification I've paid for so I was dishing out about $40/month to complete it. I did finish in about 3 months, though, so that was half the time they laid out, and right on schedule for my goal.
While I enjoyed a great deal of this course, it was (like myself) focused more on breadth than depth. I did not find that it was as valuable to me as I originally hoped. More on this below...
I could really sense the trends of the internet in how Google structured the course. The videos were rarely over 5 minutes and hardly ever over 9 minutes long. The assignments were structured by weeks that I could often complete in one two hour session. And the instruction was by a diverse field of professionals with varying styles.
In many ways, this was a happy thing; particularly for the completionist in me. I love the act of finishing things for completion's sake.
However, this can detract me from my bigger goals too. There were several "weeks" where I simply plowed through the material to take the quiz and move on. It's almost an impossible thing to try and balance the wildly divergent interests and learning styles of a class that numbers in the thousands.
And I struggle to critique the course itself...at least not more than my own tendency to move forward to the next thing. For a beginner, the pacing and the length of time spent on rudimentary topics was probably spot on. My interest waned in the early half of the certificate (the first three courses or so especially) until we got into SQL, Tableau and R Programming.
It was during these points, especially with the Excel/Google Sheets instruction, that I tempered my expectation of leveling up my skillset. What I found was a welcome refresher on pivot tables, v-lookups, and other functionality that are useful in my everyday work.
I was most looking forward to learning Tableau, SQL and R. And I was not disappointed here. A healthy introduction to all three was given, and I have already used R to attack a small dataset for an organization I work with.
It was in these three areas that I had to reel in my expectations, though. There's simply no way to become proficient in any of these languages or tools without using them regularly and persistently growing my knowledge set.
Perhaps if I'd started with zero knowledge, I'd view things differently. Perhaps I don't give myself credit enough for the myriad pieces of tooling that I have a bit of knowledge about already. As it is, this was a great method to learn more in topics that intrigue me.
If this lands people a $65K+ job, then it's well worth the tiny investment up front even if you were to take the full 6 months to complete it. I'd recommend spending a healthy amount of time on the final case course to prepare at least one or two polished case studies to be able to present and discuss in interviews.
I just love learning stuff. Rather than focus too heavily on what I haven't gotten out of a course, I do want to continue to value the process of learning. Perspective is a powerful ally when properly directed.
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