Translated by Carlos Secada of the original by Cassie Kozyrkov
Do you think that you are “not a data person”? Or maybe you’re looking forward to becoming a data analyst but are worried that you’ll have to take a course, or worse, an entire PhD, just to get started? I would like to take this opportunity to show you that you are already a data analyst. (If you.)
Let’s start by analyzing some data together!
This cube of numbers — er, I mean, matrix — seems complex and hard to understand. And it is only a small part of the whole, which repeats a similar concept 100,000 times.
If you’re breaking out in a cold sweat, it’s because you’re having a perfectly normal human reaction: a bout of boredom. Guess what, you’re right!
This matrix *is* boring.
Anyone who tells you that data is automatically exciting is either lying or has strange hobbies. To make this matrix more interesting, we need context. Two big categories:
- Context that makes these numbers tools.
- Context that makes these numbers relatives.
The first category relates the data to your needs: if you have a specific problem, you’d be delighted if I told you that this matrix contains the solution. Pain relief is a game changer when it comes to math motivation.
While in the first category it deals with increase profit to interact with the data, in the second it is about decrease the cost.
In other words, maybe we can find a way to make the meaning behind this data easily jump off the screen and into your brain. Even if the result is boring, it might be less boring than what we started with. Let’s try using some super-secret image processing software, only for doctors, to graph it…
Ta-da! With the help of MS Paint, we discovered that this dataset is just a photograph of my (boring*) hardwood floor. By using the software, we have reduced the effort of interacting with this data set: now making sense of it is as easy as looking at a photo. And more importantly, we found out that you are already an analyst.
What you have done right there is called data visualization and is part of the basic skill set of an analyst, along with data transformation (for example, cutting your ex out of a picture) and the data summary (eg complaining that only 3 selfies out of 1,722 are good enough for your Insta).
If someone ever tries to intimidate you into thinking you’re not qualified to analyze data, here are some reminders to protect yourself from their bad influence.
If you’ve ever looked at a photograph, you’re already a data analyst.. (If you’re looking at this screen, welcome! Right now, you’re using software to make sense of the data.)
(Have I cheated by using a photograph to make my point? I plead not guilty. Digital photos are legitimate data sets: they are stored in a brain-unfriendly form, but can be full of meaning when analyzed with the right tools. There are many other types and sources of data, but the same basic principles apply in all cases.)
if you ever done an online searchyou are already a data analyst.
If you’ve ever done an internet search, you’re already a data analyst.
If you’ve ever used a map to find your way, you’re already a data analyst.
If you’ve ever checked the weather on the other side of the world, you’re already a data analyst.
If you’ve ever opened a spreadsheet, you’re already a data analyst.
I’ve never met anyone who really isn’t a “data person”: take a moment to compare your computer-enhanced ability to store, process, and transmit information to that of a typical ancient Greek. Yes, to them you are basically Athena.
The reason you take all these things for granted is that you have already learned how to use data processing tools. Everything from Microsoft Paint to Google Maps to Spotify is analytics software turned essential. The makers of these tools know better than to call them that, but it is what they are. The modern world is full of wonders and you are already a part of it.
Not at all. There are some big differences between an amateur analyst and a professional one. If you are curious about what they are, check out my next article… but the good news is that it all comes down to practice and experience. If you dream of a career as an analyst, stop dreaming and start doing it. Just challenge yourself to look at all the new data formats you can, and along the way try to learn tools that promise to streamline that for you. There is nothing stopping you! Have fun!
Head over to my mini course on analytics in English, if you’re wanting to complement your practical sessions with some concepts from the analytics career.
One of my goals for this newsletter is to help turn the jumble of my scattered reflections into coherent learning journeys…
I used a boring photograph of my soil to remind me that our forays into data don’t always lead to momentous revelations. Sometimes yes, but most of the time no. That is part of the job.
This data was boring and not particularly useful to you, but seeing it in a familiar format makes it easier for you to extract meaning (and move on). That’s half the job of an analyst: turning the numbers into something you can understand. The other half is about maximizing inspiration per minute, but that’s a separate article.
Are you offended that I used MS Paint to make my point? Well then, here is Python! Same idea.
Learn more about data science and artificial intelligence in Spanish here.