129: Best Data Skills to Learn (And EXACTLY When to Learn Them)

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It's easy to be intimidated by the

insane amount of data tools out there,

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especially when you're starting from

scratch and so many different resources

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tell you so many different tools to learn.

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In fact, a recent survey shows that

there's over: 2000

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that you could be learning or using.

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But here's the truth.

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You don't have time to learn 2000 tools.

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Heck, you don't even have

time to learn 20 tools.

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And luckily for you, you don't need to.

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That begs the question, what tools

should you start out with if you're just

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starting as an aspiring data analyst?

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Well, if you're just starting out, you

definitely don't want to waste time.

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That is like the number one thing.

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So you don't want to waste time

learning skills that aren't useful.

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You also want to get your foot

in the door as soon as possible

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and land that first data job.

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So this gives us two different levers

or two different variables to play with.

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Number one, how popular a data tool is.

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And number two, How

difficult a tool is to learn.

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We want to start with the popular

and useful data programs that

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are easy to learn as well.

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You can sort of think of this like a

2D matrix with the x axis measuring

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difficulty and the y axis measuring

how often a tool is required.

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So it makes sense to start with

the quadrant where tools are

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high in demand and easy to learn.

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This is the lowest hanging fruit and the

place where most people should start.

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But that still means that we

have to determine what skills

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are required the most and which

ones are the easiest to learn.

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Now, what skills are in demand the most?

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It's a difficult question to know.

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One way is to trust experts

in the field like Ivy League

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legend Columbia University.

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They're smart, right?

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They're super trustworthy.

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Ivy League, Columbia.

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Uh, and if you go to their

website, you'll actually see that

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they state that MATLAB is the.

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Third most popular data tool that might

not mean anything to you right now,

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but I'll show you in a few minutes

how you can actually prove data wise.

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This isn't true at all.

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That MATLAB isn't even in the top

10 of data skills to learn, or

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even the top 25 for that matter.

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As a data nerd, we should try to use data

when answering these types of questions.

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It's actually a hard data to

get, unfortunately, but luckily

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for us, my friend Luke Bruce.

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Has already been doing it.

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He created something called Data

Nerd Tech, which is web scraped

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and analyzed, 2.5 million different

data, job listings, especially the

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requirements, and then reports.

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What percentage of job descriptions

mention skills as requirements?

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The data is constantly being

updated, but as of the creation

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of this video, here is the top 10.

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sql, Excel, Python, Tableau, power

bi R, SaaS, PowerPoint, word, Azure.

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But honestly, I think only skills that

are required over 10 percent of the

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time should be the real focus points.

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So that leaves us with the big six.

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SQL at 47 percent Excel at

33% Python at 31%, Tableau at

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24%, Power BI 20%, and R 17%.

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These are what I call the big six,

and they're the six most important

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things to learn when you're trying

to land your first data job.

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It's also important to note that these

results are for all levels of data

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analyst roles, both junior and senior.

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Senior and intermediate.

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So just for the junior roles, there

might be some slight differences.

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Now we know which data skills

are important and required

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often in job descriptions, but

which ones are easy to learn?

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Of course, learning data skills is

a bit subjective, depending on your

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previous experience and honestly, your

intelligence level, but there are some

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universal guidelines when it comes

to the ease of learning data tools.

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Like you're probably

already familiar with Excel.

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Am I wrong?

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You've used it before.

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Like in school or at another job,

you've analyzed some sort of data at

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some point in your life, probably in

Excel, whether it's school or work,

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you've probably opened up Excel.

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Correct me if I'm wrong, go in the

comments and tell me if I'm wrong, but

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you're probably okay at Excel right now.

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And that's awesome because Excel is really

used quite often in the data fields.

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That's why I think Excel is one of

the things that you should start

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with when you're starting your data

career journey is because it's easy.

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It's one of the easiest things

that you can learn because

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you're already familiar with it.

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And to be honest, there's not

even that much more to add to it.

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Of course, there's different techniques

and things that you can do in Excel,

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but chances are you're already

familiar with 50 percent of it.

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Next there's BI tools, BI standing

for business intelligence, and

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honestly, they're like the, PowerPoint.

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If you haven't used Power BI or

Tableau much, they can sound quite

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intimidating, but don't let it be.

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Both are pretty easy.

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They're just drag and

drop analysis programs.

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It honestly feels a lot like PowerPoint

where you click on something and

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you drag it to different places.

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And based on where you drag

it, different things happen.

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It's all point and click drag and drop.

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You'll be able to figure it out.

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I promise.

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SQL or SQL stands for

Structured Query Language.

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And it is a language, so it is a

little bit harder to learn, but

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there's not really all that much,

honestly, when you're first trying to

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land your first day at a job, there's

probably about 20 different commands

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that you should be using in SQL.

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And so while it takes time to

learn those commands, there's

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only really about 20 of them.

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And it's not that bad when you contrast

that to another programming language like

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Python or R and I won't sugarcoat it.

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Learning to program is hard.

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It's like a new language.

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That's literally why they call

them programming languages.

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And it takes time to even know

the terms to start to program.

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Concepts like loops, functions, variables,

these are very difficult to comprehend

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and they take a while to figure out.

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When I was first learning loops, It did

not come easy and it took me a couple of

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weeks, but once you figure those out, then

you can actually start learning Python

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or R that's the problem with these two.

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They're really in demand, but

they're quite difficult to learn.

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Now, of course, there are easy data

programs that we could be learning out

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there, some that are maybe even easier

than Excel or easier than Tableau,

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but they're not part of the big six.

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So we're going to ignore them.

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So in my opinion, these are

the easiest data skills to

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learn from easiest to hardest.

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Number one, Excel, two, Tableau,

three, Power BI, four, SQL,

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five, R, and six, Python.

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Great!

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So now we know the most

required data skills and we

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know the easiest data skills.

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So we can combine these two lists

together and create our ultimate list

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or what I call the order of operations.

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To learning data skills.

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Now you might remember this idea of

order of operations from when you

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were learning math in grade school.

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It's a concept to determine the

sequence in which mathematical

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operations should be executed.

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Basically it's what you do

first in a math equation.

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You may even remember PEMDAS,

or if I like to remember it,

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please excuse my dear aunt Sally.

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And this is kind of a

mnemonic to help you remember.

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The correct mathematical

order of operations.

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So let's break down this

mnemonic one by one.

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The P, or please, this stands for

parentheses, which basically means

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you should perform the calculations

inside of parentheses first.

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Next, there's the E, or excuse, which

is calculating the powers and roots

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inside of the mathematical equation.

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Next, there's the M, or the D.

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Which is the my and the dear, which is

saying that you should do multiplication

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and division from left to right.

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Next, next is the A and the S or

the Aunt Sally, which means that you

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should be performing the addition and

subtraction also from left to right.

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Please excuse my dear Aunt Sally.

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It's an easy way to remember where to

start and in what order to proceed.

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It's basically the top right quadrant

of the data skill matrix or the

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section with the Easy to learn skills

with the most in demand skills.

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So here is my official data learning

order of operations, or simply

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said the data learning ladder.

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It's number one to learn Excel.

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Number two, learn Tableau.

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Number three, move on to SQL.

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And four, finally finish with Python.

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We're starting with Excel because it

is by far the easiest to learn and the

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second most popular tool out there.

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Then we're moving to Tableau because

although less popular than SQL or Python,

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it's much easier to learn them both.

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Like I said, drag and drop, click.

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It's easy.

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Then move to the most popular data

tool, SQL, which is a little bit

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harder to learn than Excel and

Tableau, but still not nearly as

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hard as something like Python or R.

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And then we're going

to finish with Python.

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Now you might be wondering,

well what happened to R?

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Or what happened to Power BI?

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And honestly, Power BI and Tableau

are similar enough that if you

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learn one, learning the other is

not going to take you very long.

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You'll be able to figure it out and a

lot of the concepts are quite the same.

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That's also true with Python and R.

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They are somewhat similar.

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Once you learn one of these

languages, learning the other

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language won't be nearly as bad.

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My suggestion is just to

learn one for right now and

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then pick up the other later.

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Excel, Tableau, SQL, Python.

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This gives you the data learning ladder.

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And to make it easier to remember,

I created a phrase or mnemonic

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that you can repeat in your mind

to remind you that this is the

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fastest way of landing a data job.

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It is every turtle sprints past.

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Excel, Tableau, Python.

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Or if you wish for the more

thorough version with Power BI

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and R included, it is every turtle

powerfully sprints past rapidly.

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When you're not sure what step to

take next on your data journey,

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simply refer to this ladder.

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I hope this helps, and if this video

did help, I'm sure you're going to

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find this video super helpful as well.