Data analytics tools are obviously key tools that any data analyst or data
scientist needs to know how to use. So how do you pick which one that you
should learn? I'm going to go through four different tools that I think are
great options to learn and how you pick between them if you're just getting
started. Hi, I'm Jen. Welcome to the channel!
For this analytic tools comparison, I'm going to use two main criteria that I think
are really at the heart of the decision on which tool to learn. The first is how
widely used is it and that ties in to how many jobs are there that are
available for people that have the skill set? And then my second criteria is how
easy is it to learn? Because if you can learn it pretty quickly, you can add it
to your bank of skills, throw it on your resume, and give yourself more options
for jobs. Since this review is really focused on analysts - data analysts and
data scientists - that are making decisions about which tools to use, I'm
specifically not going to focus on decision factors that companies use in
order to figure out which of these softwares that they want to implement
within their company. So if it's expensive - doesn't matter,
you're not footing the bill, the company is, but that could impact how many
companies are using it and the job availability. I'm also not going to look
much at the IT setup that's required for them. Again, this is more of a company
decision and it'll show up a little bit in available jobs, but it's not really
something that you should be too concerned with the logistics of in
deciding which software that you want to learn. We also won't really talk about
customer support for these options. Though I will say all of the four
different tools that I'm going to go through either have fantastic customer
support from the company or there's a wealth of resources online and other
users that will help work out any problem that you have with any of them.
I wouldn't be concerned from that aspect that if you get stuck that you're
not going to have help. You'll have it whether it's from the company or other
users with any of these. The first tool: Python. Python is most
popular at startups and smaller companies that are really concerned with
cost. I guess I did talk a little bit about cost there, but that talks more
about the opportunity that's out there. Right now, in terms of job postings and
what programming language they require, Python is actually the most prevalent.
There are a lot of good reasons - a lot of good job opportunities - for learning
Python. In terms of learning Python, it's pretty simple to learn Python and
there's a lot of good documentation for it. The one major drawback is there's not
a common GUI interface for it. This can be a drawback depending on your
level of skills and how much you're going to build them. Some of other tools
like SAS have a fantastic GUI interface and you can use a mix of knowing how
to code, but also just really understanding the logic of how things go
together to do your work. That's one drawback. It's not a reason not to learn it.
There's obviously a lot of advantages - a lot of jobs - that want you to have it.
That's our first option. Second is R. R is another option that a lot of
startups or smaller companies concerned with cost efficiency are really focused
on. And it's also second in terms of number of job openings associated with
that programming language. Of the four different tools I'm recommending that
you consider learning, R is definitely the hardest. Part of this is because by
its nature it's a really basic language. It means that some things that you can
do really elegantly and quickly in other languages, you can't do in R. It might
take a lot of code to be able to do really complex things. The great thing is
because you learn the basics and then you use basics to create anything that
you want is it can be very versatile for you to learn how to do new things
because you're not learning a lot of new ways of working. There are a lot of
advantages from that perspective, but if you're doing a lot of coding it can get
really tedious to do long amounts of code for complex things
that you could do - in some cases - in one or two lines in some of the other
programming languages. Third: SAS. This is the third most job openings of the three
I'm talking about in this section, but it isn't really that much behind. There
isn't a big gap between Python R and SAS. The main reason that you would
learn SAS even though it's number three on the list in terms of popularity is
there are a lot of industries where SAS is really the only analytic software
that they're using or at least the main analytic software that they're using. If
you're going into healthcare or banking, there's a 95% chance the company that
you're going into for data analytics is going to be using SAS. It is a really
powerful tool. It's expensive to set up, but there there are a lot of great
things about the tool. SAS also has the highest market share in private
organizations. Again, it's industry specific but if that's
the industry you want to go into, SAS is critical. The other thing that I expect
will help SAS in the future is they are one of the most heavily invested in
research and work on AI and predictive analytics which we all know is growing.
We see it around us all the time. This may end up pushing it above some of
these other programming languages or at least becoming a big - the main - market
player in this area as we get into more AI
heavy tech. In terms of ease of learning, SAS can be really easy to medium. They
have great interfaces for the user to use. For instance, if you're using their
Enterprise Guide product then you don't really actually have to know any coding
to do the majority of work. Though there are things that you can't do directly in
it, that you will have to know how to code. And there are some times where it's
much quicker to put in a little bit of code than to do everything through the
interface. But it's a great way to learn it,
to get your foot in the door with coding. If you have a background in SQL, those
skills also translate really well into SAS. It will be very easy for you to pick
up the SAS language. The fourth tool is one everyone should know: Excel. You have
to know Excel as an analyst. I know it makes a lot of people cringe. They want
to use the heavy tools. They want to use the elegant, the eloquent different
programming languages. But at the end of the day, Excel does a lot and if you're
working with small amounts of data, which you are going to be at some point, even if
you're a big data analyst, at some point you're going be working with small
amounts of data in your day where it's just quicker to use Excel. It's also
widely available. It's pretty cheap. Most of us have it on our desktops. Even if
you don't have Excel, if you have an open-source version, it's going to be
fairly similar. In terms of job outlook, you're probably not going to get an
analyst role solely based on Excel. There are some excel data analyst
jobs out there so you can't completely discount it. It's just going to be a
little less prevalent. Most analytics roles and certainly any data science
roles, you're going to have to know one of the other three tools that I talked
about. The other reason that knowing Excel is a good place to start is
because the framework that you build, the skills that you learn in the logic and
how things tie together is really useful in any system you're working on. I truly
believe the hardest part of solving any analytics problem or doing any analysis
isn't the actual in the software analysis work. It's figuring out all the
logic you want to use to get there. You can figure out, you can find people
online, you can contact the company to find out the exact mechanics of
implementing it in the software. But knowing that logic is really the central
piece, the central skill set that you're bringing. Getting familiar with that in
Excel makes it that much easier in all of the other systems. Which of these
tools do you utilize the most or which ones are you now more interested in
learning after watching this video? Let me know in the comments. We'll talk more
about everything analytics in the coming weeks. I look forward to seeing you
back here.