Top 4 Data Analytics Tools

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.


  1. What would you recommend for me if I´m just starting to get involved into analytics. Should I start with Data Visualisation tools or with Data Analytics tools? Both at the same time?
    Thank you Jen!

  2. Thank you so much, I am learning SQL and have math background, but you did not mention SQL .Did I make wrong choice of analytics tools?

  3. Learn more about the key analytics skills and tools needed to become a data analyst. I've written an extensive guide to building the right skills and finding a data analyst job that's a great fit for you:

  4. "SQL" because it just works :). But all depends on what you try to query. Mostly I prefer to insert any kind of data source into tables (in database) and query them with SQL afterwards. I really should get my head on python with panda (or other plugin) to get faster results with csv files for instance… but I hate python syntax :/

  5. Do you have a video on the best learning tools for learning these languages? I’m wanting to transition into this area of work from biotech field, and I’ve self taught R and SQL from datacamp. What should my next step be? Do you have an opinion about datacamp in particular? I’m also brushing up on Excel 🙂 . Looking for career guidance. Thanks Jen!

  6. What analytical methods would you use to determine what the company is selling and not selling in the Wholesale industry? (Back story) its a new role in the company and they want to have someone find trends and patterns through their data and turn it into actionable decision making. Currently, they are using NetSuite ERP, Excel and SQL. Any insights on tips or tricks would be greatly appreciated. I start at the end of the month. And are you apart of the IIBA community? Great videos. Thank you.

  7. Thanks for your video and I recently decided to become a data analyst.

    Short summary based on my understanding below:
    Top 4 analytical tool you need to learn

    From the number of high to low demanding job vacancies:

    Python: easier to learn and lots of documentation for it

    R: more complex. a really basic language. Lots of codes.

    SAS: a majority usage of 95% in healthcare and banking. Skills of SQL can be transformed very well into that of SAS.

    Excel: when working with small amounts of data

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