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00:00:00
It's easy to be intimidated by the
insane amount of data tools out there,
00:00:04
especially when you're starting from
scratch and so many different resources
00:00:08
tell you so many different tools to learn.
00:00:10
In fact, a recent survey shows that
there's over 2000 different data tools
00:00:14
that you could be learning or using.
00:00:16
But here's the truth.
00:00:18
You don't have time to learn 2000 tools.
00:00:20
Heck, you don't even have
time to learn 20 tools.
00:00:23
And luckily for you, you don't need to.
00:00:25
That begs the question, what tools
should you start out with if you're just
00:00:29
starting as an aspiring data analyst?
00:00:31
Well, if you're just starting out, you
definitely don't want to waste time.
00:00:34
That is like the number one thing.
00:00:36
So you don't want to waste time
learning skills that aren't useful.
00:00:39
You also want to get your foot
in the door as soon as possible
00:00:42
and land that first data job.
00:00:44
So this gives us two different levers
or two different variables to play with.
00:00:48
Number one, how popular a data tool is.
00:00:50
And number two, How
difficult a tool is to learn.
00:00:52
We want to start with the popular
and useful data programs that
00:00:56
are easy to learn as well.
00:00:57
You can sort of think of this like a
2D matrix with the x axis measuring
00:01:01
difficulty and the y axis measuring
how often a tool is required.
00:01:05
So it makes sense to start with
the quadrant where tools are
00:01:09
high in demand and easy to learn.
00:01:12
This is the lowest hanging fruit and the
place where most people should start.
00:01:15
But that still means that we
have to determine what skills
00:01:18
are required the most and which
ones are the easiest to learn.
00:01:21
Now, what skills are in demand the most?
00:01:23
It's a difficult question to know.
00:01:25
One way is to trust experts
in the field like Ivy League
00:01:28
legend Columbia University.
00:01:30
They're smart, right?
00:01:31
They're super trustworthy.
00:01:32
Ivy League, Columbia.
00:01:34
Uh, and if you go to their
website, you'll actually see that
00:01:36
they state that MATLAB is the.
00:01:39
Third most popular data tool that might
not mean anything to you right now,
00:01:43
but I'll show you in a few minutes
how you can actually prove data wise.
00:01:47
This isn't true at all.
00:01:48
That MATLAB isn't even in the top
10 of data skills to learn, or
00:01:52
even the top 25 for that matter.
00:01:54
As a data nerd, we should try to use data
when answering these types of questions.
00:01:58
It's actually a hard data to
get, unfortunately, but luckily
00:02:01
for us, my friend Luke Bruce.
00:02:03
Has already been doing it.
00:02:04
He created something called Data
Nerd Tech, which is web scraped
00:02:08
and analyzed, 2.5 million different
data, job listings, especially the
00:02:12
requirements, and then reports.
00:02:14
What percentage of job descriptions
mention skills as requirements?
00:02:18
The data is constantly being
updated, but as of the creation
00:02:21
of this video, here is the top 10.
00:02:23
sql, Excel, Python, Tableau, power
bi R, SaaS, PowerPoint, word, Azure.
00:02:32
But honestly, I think only skills that
are required over 10 percent of the
00:02:35
time should be the real focus points.
00:02:38
So that leaves us with the big six.
00:02:40
SQL at 47 percent Excel at
33% Python at 31%, Tableau at
00:02:48
24%, Power BI 20%, and R 17%.
00:02:52
These are what I call the big six,
and they're the six most important
00:02:55
things to learn when you're trying
to land your first data job.
00:02:58
It's also important to note that these
results are for all levels of data
00:03:01
analyst roles, both junior and senior.
00:03:03
Senior and intermediate.
00:03:05
So just for the junior roles, there
might be some slight differences.
00:03:08
Now we know which data skills
are important and required
00:03:11
often in job descriptions, but
which ones are easy to learn?
00:03:14
Of course, learning data skills is
a bit subjective, depending on your
00:03:18
previous experience and honestly, your
intelligence level, but there are some
00:03:22
universal guidelines when it comes
to the ease of learning data tools.
00:03:26
Like you're probably
already familiar with Excel.
00:03:29
Am I wrong?
00:03:29
You've used it before.
00:03:31
Like in school or at another job,
you've analyzed some sort of data at
00:03:35
some point in your life, probably in
Excel, whether it's school or work,
00:03:38
you've probably opened up Excel.
00:03:40
Correct me if I'm wrong, go in the
comments and tell me if I'm wrong, but
00:03:44
you're probably okay at Excel right now.
00:03:47
And that's awesome because Excel is really
used quite often in the data fields.
00:03:51
That's why I think Excel is one of
the things that you should start
00:03:54
with when you're starting your data
career journey is because it's easy.
00:03:58
It's one of the easiest things
that you can learn because
00:04:00
you're already familiar with it.
00:04:01
And to be honest, there's not
even that much more to add to it.
00:04:05
Of course, there's different techniques
and things that you can do in Excel,
00:04:08
but chances are you're already
familiar with 50 percent of it.
00:04:10
Next there's BI tools, BI standing
for business intelligence, and
00:04:14
honestly, they're like the, PowerPoint.
00:04:16
If you haven't used Power BI or
Tableau much, they can sound quite
00:04:20
intimidating, but don't let it be.
00:04:22
Both are pretty easy.
00:04:23
They're just drag and
drop analysis programs.
00:04:25
It honestly feels a lot like PowerPoint
where you click on something and
00:04:29
you drag it to different places.
00:04:30
And based on where you drag
it, different things happen.
00:04:32
It's all point and click drag and drop.
00:04:34
You'll be able to figure it out.
00:04:35
I promise.
00:04:36
SQL or SQL stands for
Structured Query Language.
00:04:39
And it is a language, so it is a
little bit harder to learn, but
00:04:43
there's not really all that much,
honestly, when you're first trying to
00:04:45
land your first day at a job, there's
probably about 20 different commands
00:04:49
that you should be using in SQL.
00:04:51
And so while it takes time to
learn those commands, there's
00:04:53
only really about 20 of them.
00:04:55
And it's not that bad when you contrast
that to another programming language like
00:04:59
Python or R and I won't sugarcoat it.
00:05:02
Learning to program is hard.
00:05:03
It's like a new language.
00:05:05
That's literally why they call
them programming languages.
00:05:08
And it takes time to even know
the terms to start to program.
00:05:12
Concepts like loops, functions, variables,
these are very difficult to comprehend
00:05:17
and they take a while to figure out.
00:05:19
When I was first learning loops, It did
not come easy and it took me a couple of
00:05:23
weeks, but once you figure those out, then
you can actually start learning Python
00:05:27
or R that's the problem with these two.
00:05:29
They're really in demand, but
they're quite difficult to learn.
00:05:32
Now, of course, there are easy data
programs that we could be learning out
00:05:35
there, some that are maybe even easier
than Excel or easier than Tableau,
00:05:39
but they're not part of the big six.
00:05:41
So we're going to ignore them.
00:05:42
So in my opinion, these are
the easiest data skills to
00:05:44
learn from easiest to hardest.
00:05:47
Number one, Excel, two, Tableau,
three, Power BI, four, SQL,
00:05:52
five, R, and six, Python.
00:05:54
Great!
00:05:55
So now we know the most
required data skills and we
00:05:58
know the easiest data skills.
00:05:59
So we can combine these two lists
together and create our ultimate list
00:06:03
or what I call the order of operations.
00:06:05
To learning data skills.
00:06:07
Now you might remember this idea of
order of operations from when you
00:06:11
were learning math in grade school.
00:06:13
It's a concept to determine the
sequence in which mathematical
00:06:16
operations should be executed.
00:06:18
Basically it's what you do
first in a math equation.
00:06:21
You may even remember PEMDAS,
or if I like to remember it,
00:06:24
please excuse my dear aunt Sally.
00:06:27
And this is kind of a
mnemonic to help you remember.
00:06:29
The correct mathematical
order of operations.
00:06:32
So let's break down this
mnemonic one by one.
00:06:35
The P, or please, this stands for
parentheses, which basically means
00:06:38
you should perform the calculations
inside of parentheses first.
00:06:41
Next, there's the E, or excuse, which
is calculating the powers and roots
00:06:45
inside of the mathematical equation.
00:06:47
Next, there's the M, or the D.
00:06:50
Which is the my and the dear, which is
saying that you should do multiplication
00:06:54
and division from left to right.
00:06:56
Next, next is the A and the S or
the Aunt Sally, which means that you
00:06:59
should be performing the addition and
subtraction also from left to right.
00:07:03
Please excuse my dear Aunt Sally.
00:07:05
It's an easy way to remember where to
start and in what order to proceed.
00:07:09
It's basically the top right quadrant
of the data skill matrix or the
00:07:13
section with the Easy to learn skills
with the most in demand skills.
00:07:18
So here is my official data learning
order of operations, or simply
00:07:22
said the data learning ladder.
00:07:24
It's number one to learn Excel.
00:07:26
Number two, learn Tableau.
00:07:28
Number three, move on to SQL.
00:07:31
And four, finally finish with Python.
00:07:33
We're starting with Excel because it
is by far the easiest to learn and the
00:07:37
second most popular tool out there.
00:07:39
Then we're moving to Tableau because
although less popular than SQL or Python,
00:07:43
it's much easier to learn them both.
00:07:45
Like I said, drag and drop, click.
00:07:47
It's easy.
00:07:47
Then move to the most popular data
tool, SQL, which is a little bit
00:07:51
harder to learn than Excel and
Tableau, but still not nearly as
00:07:54
hard as something like Python or R.
00:07:55
And then we're going
to finish with Python.
00:07:58
Now you might be wondering,
well what happened to R?
00:08:00
Or what happened to Power BI?
00:08:01
And honestly, Power BI and Tableau
are similar enough that if you
00:08:05
learn one, learning the other is
not going to take you very long.
00:08:09
You'll be able to figure it out and a
lot of the concepts are quite the same.
00:08:12
That's also true with Python and R.
00:08:14
They are somewhat similar.
00:08:16
Once you learn one of these
languages, learning the other
00:08:17
language won't be nearly as bad.
00:08:19
My suggestion is just to
learn one for right now and
00:08:22
then pick up the other later.
00:08:23
Excel, Tableau, SQL, Python.
00:08:26
This gives you the data learning ladder.
00:08:27
And to make it easier to remember,
I created a phrase or mnemonic
00:08:31
that you can repeat in your mind
to remind you that this is the
00:08:34
fastest way of landing a data job.
00:08:36
It is every turtle sprints past.
00:08:39
Excel, Tableau, Python.
00:08:41
Or if you wish for the more
thorough version with Power BI
00:08:44
and R included, it is every turtle
powerfully sprints past rapidly.
00:08:49
When you're not sure what step to
take next on your data journey,
00:08:52
simply refer to this ladder.
00:08:53
I hope this helps, and if this video
did help, I'm sure you're going to
00:08:57
find this video super helpful as well.