To learn Excel with stories.

Why not add a little spice to the way we learn about data science?

Photo by JESHOOTS.COM on Unsplash

I am a doctor of Medicine who made a career shift about 10 years ago from medicine to healthcare. I spent 2 years in the United States studying business from a healthcare perspective, but the subject that I was totally attracted to was “Business Analytics”. In that class, I learned how to use data to make summaries, inferences and actionable recommendations to businesses. We were taught to use JMP as our main statistical software, but after going back to my home country and realizing that I needed a fortune to buy an original copy of JMP for my statistical endeavors, I finally decided that I needed to learn a more simple and affordable statistical software to use in case I was not able to afford JMP anymore, so I have decided to learn Excel.

Now that I have started using Excel in many of my projects and research papers, I must confess that learning about statistics and statistical software as pure curricula was boring. What would be more interesting is to learn data with stories, so the driver for learning would be solving a problem and not solving a question or getting a grade.

“I don’t want to learn how to use a power driller. I just want to know how to make a hole in the wall.”

So, I have decided to write story-driven tutorials on how to use the different Excel functions and graphs that are commonly needed for our daily activities. Let me walk you through one of those Excel features right now!

I wanted to write SEO-optimized articles about learning Excel. I went to Ubersuggest and created an individual account. In the “Keyword ideas” section of Ubersuggest, I entered the term “learn excel”, set the minimum monthly search volume to 250, and set the maximum SEO difficulty to 40, which are the parameters needed to find the most competitive keyword search terms, and finally hit “Apply” to come up with a final list of 12 hot keywords.

I extracted those keywords into Excel, and the list was as follows:

As you see, it was a pretty small data set. Anybody can conclude that the upper three keywords had the greatest monthly search volumes with yet decent SEO difficulty scores.

Yet, some people may give up the relatively large monthly search volume for a more competitive SEO score, so, they may choose to use the keywords with the less SEO difficult score. I have highlighted those keywords in yellow in the screenshot below.

It seems that the keyword “learn excel for free” is the most competitive keyword, having nearly 1000 monthly searches per month, while showing the least SEO difficulty score of 20. You can opt for other keywords like “learn excel for free online”, or “learn excel free online” with a greater SEO difficulty score of 21, but with fewer monthly searches of 720. It all depends on your size as a starting blogger or writer.

Let’s make a scatter chart in Excel to visualize the data.

This is how to do it.

And the final scatter plot of “Monthly search volumes” versus “SEO difficulty score” for the 12 keywords will look like that:

If you have a good eye, you may have noticed that there are only 10 points within the above scatter plot, whereas I have consistently said that we had 12 keywords. The answer to this observation lies in that we have two keywords with exactly the same numbers for both the search volume and the SEO difficulty.

If you look back at figure 1, you will notice that the keywords “to learn excel” and “how learn excel” each has exactly a monthly search volume of 5400, and a SEO score of 31. That’s why they are represented in the scatter plot by only one point (shown below). The other nearby point belongs to the keyword “learn excel”, which has 5400 searches, but a SEO score of 34.

The other two keywords that are represented by a single point are “learn excel for free online” and “learn excel free online”, because each of them has 720 search volumes per month and a SEO difficulty score of 21. I have highlighted that point within the scatter plot in the screenshot below.

So now you can understand why we had only 10 points for the 12 keywords.

All I had to do next was to make the scatter plot more readable. The title of the plot should not be “SEO difficulty” but “Monthly search volumes by SEO difficulty”. Also, I had to put titles to both the horizontal and the vertical axes of the scatter plot. This is how I did it.

And now the final version of the scatter plot will look like this:

I think if I just told you how to do the previous functions in Excel without a plot or story in which I can show how much I needed that excel function or graph for my own purpose, you would not have been willing to follow the story and read till the end. After all, you may want to write a SEO friendly article one day, and although 12 keywords are easy to sort without Excel, yet, if you were to have 200 keywords, then I think you would probably be interested in learning how to visually sort those keywords out!

Let me know what you think. Are stories really the best tools to learn data science?

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