Why Excel is the best way to learn data analytics

Why Excel is the best way to learn data analytics

The more I advance into analytics, the more I come back to Excel as a teaching and prototyping cool. Yes, of course, Excel has its weaknesses — but as a medium for learning, it’s unmatched.

It’s visual, it’s transparent, and it keeps things simple.

The more I advance into analytics, the more I come back to Excel as a teaching and prototyping cool. Yes, of course, Excel has its weaknesses — but as a medium for learning, it’s unmatched.

Here’s why:

It reduces cognitive overhead

Cognitive overhead is described as “how many logical connections or jumps your brain has to make in order to understand or contextualize the thing you’re looking at.”

Often an analytics learning journey looks like this:

  1. Learn a brand-new statistical technique.
  2. Learn how to implement the brand-new technique using brand-new _coding _techniques
  3. Progress to more advanced statistical and coding techniques, without ever having felt really comfortable with the basics.

It’s hard enough to learn the statistical foundations of analytics. To learn this while _also _learning how to code invites sky-high cognitive overhead.

Now, I do believe there is _great _virtue to practice analytics via coding. But it’s better to isolate these skill sets while mastering them.

Excel provides the opportunity to learn statistical techniques without the need to learn a new programming language at the same time. This greatly reduces cognitive overhead.

Laptop and notes at desk

It’s a visual calculator

The first mass-market offering of a spreadsheet was called VisiCalc — literally, a visual calculator. I think of this often as one of the spreadsheet’s biggest selling points.

Especially to beginners, programming languages can resemble a “black box” — type the magic words, hit “play” and presto, the results. Chances are the program got it right, but it can be hard for a newbie to pop open the hood and see why.

By contrast, Excel lets you watch an analysis take shape each step of the way. It lets you calculate and re-calculate, visually.

Seeing is believing, right?

You can’t take shortcuts

Open-source tools like R and Python give you access to a wide variety of packages, which usually means you don’t have to “start from scratch” with basic functions.

While Excel add-ins for analytics are available, many of them cost. But that’s OK! In fact, left with the bare building-blocks of Excel, there’s more opportunity to get face-to-face with what’s being built.

In Excel, we can’t always rely on an external package to conduct our analysis for us. We’ve got to get there by our own devices.

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