# ๐ฉโ๐ป The Data Learning Ladder

It's easy to be intimidated by the insane amount of data tools out there, especially when you're starting from scratch. And so many different resources tell you to learn different tools.

In fact, a recent survey shows there's over 2,000+ data tools you could be learning.

But here's the truth.

You don't have time to learn 2,000+ tools.

You don't even have time to learn 20 tools.

And luckily, you don't need to.

But that begs the question, what tool should you start with if you're just starting as a data analyst?

# ๐ข Where to Start as a Data Analyst?

If you're just starting out, you don't want to waste time learning skills that aren't useful.

You also want to get your foot in the door as soon as possible.

This gives us two levers to play with: popularity and difficult to learn.

We want to start with the popular (and useful) data programs that are easy to learn.

You can think of this like a 2D matrix with the x-axis measuring difficulty & the y-axis measuring how often a tool is required.

It would make sense to start in Quadrant 2 with tools that are required often, but are easy to learn.

But that still means we have to determine what skills are required the most & easiest to learn.

# ๐ What Data Skills Are in Demand?

It's a difficult question to know. One way is to trust experts in the field like ivy league legend Columbia University. They're smart--they should be trustworthy right?

They state that MATLAB is the 3rd most popular data tool ๐

That might not mean anything to you right now, but I'll show you how we can prove in a data-driven way, MATLAB isn't even in the Top 10....or even Top 25

As a data nerds, we should try to use data when answering this question. It's actually hard data to get, but luckily for us, Luke Barousse has been doing it for us.

He created datanerd.tech which has web scraped and analyzed 2.5M+ data jobs listings. It then reports what percentage of job descriptions mentioned these skills as requirements.

The data is constantly being updated, but as of writing this article, here's the Top 10

But honestly, I think only skills over 10% should really be focus points; that leaves us with The Big 6:

SQL - 47%

Excel - 33%

Python - 31%

Tableau - 24%

Power BI - 20%

R - 17%

It's also important to note, these results are for ALL levels of data analyst, both junior and senior.

Now we know which ones are important, but which ones are easy?

# ๐ฉโ๐ป Easy Data Skills to Learn

Of course learning data skills is a bit subjective, depending on your previous experience and intelligence level. But there are some universal guidelines when it comes to ease of learning.

### You're Already Familiar with Excel

Chances are, you've used Excel to analyze data at some point in your life. Whatever it was school, or work, there's a good chance you've at least opened Excel before. And that's great because you're already familiar with the skill!

That's why I think Excel is one of the easiest things you can learn as an aspiring data analyst.

There's not even that much to learn inside of it!

### BI Tools Are Like PowerPoint

If you're haven't used Power BI or Tableau much, it can be quite intimidating. Don't let it be. Both are drag-and-drop analysis programs that feel similar to PowerPoint--click on the stuff and drag it to the right places.

Obviously, it's a bit more complicated than just Excel. But it's very manageable.

### Learning Programming is Hard

I won't sugar coat it, learning to program is hard. It's like learning a new language (that's why they call them programming languages). And it takes time to even know the terms to begin to program.

Concepts like loops, functions, variables are difficult to comprehend and take a minute to comprehend.

And then you can start learning Python or R.

### Ignoring Everything Else

Of course there are easier data programs we could learn, but they're not part of The Big 6, so we ignore them.

### Data Skills (Easiest to Hardest)

In my opinion, these are the easiest data skills to learn:

Excel

Tableau

Power BI

SQL

R

Python

# ๐ The Order of Operations

Now we know the most required data skills, and we know the easiest data skills.

We can combine those two lists and create an "order of operations" to learning data.

You might remember this term from when you were learning basic math in grade school. It's a concept to determine the sequence in with mathematical operations should be executed.

You may even remember, PEMDAS, or as I remember it, "Please excuse my dear aunt sally".

**P**arentheses: Perform calculations inside parentheses first.**E**xponents: Next, calculate powers and roots.**M**ultiplication and**D**ivision: Then, perform multiplication and division from left to right.**A**ddition and**S**ubtraction: Finally, perform addition and subtraction from left to right.

It's an easy way to remember where to start and what order to proceed.

It's basically the top-right quadrant of the Data Skill Matrix

Here's the Data Learning Order of Operations, or The Data Learning Ladder

Excel

Tableau

SQL

Python

We start with Excel because it is by far the easiest to learn, and the second most popular.

Then we move to Tableau because although less popular than SQL or Python, it's much easier to learn.

Then move to the most popular tool, SQL.

And then finish with Python.

You might be wondering what happened to R or Power BI. Honestly, Power BI and Tableau are similar enough that if you learn one, learning the other one won't take you long. Same with Python and R. My suggest is to just learn one of them now, and pick the other up later.

This gives you the Data Learning Ladder.

To make it easier to remember the order, I created a phrase to repeat to remind you this is the FASTEST way to landing a data job.

Every Turtle Sprints Past = Excel Tableau SQL Python

Or if you wish for the more thorough version (with Power BI and R), Every Turtle Powerfully Sprints Past Rapidly

When you're not sure what step to take next, simply refer to the ladder.

I hope this helps.

Your Data Coach,

Avery