thank you HubSpot for sponsoring this video the amount of hype data scientist job family
has received is real let me tell you three reasons why data analyst Jeff Webley might
be a better one than a data scientist quick disclaimer this video is not to bash any job
family every job family has a place of its own this video is just for the purposes of conveying
the two job families and three things that I've specifically noticed that makes data analyst
jaw family stand out back in 2008 Harvard Business Review published this article where they call data
scientists the sexiest job of 21st century since then data scientist jaw family has taken off and
it is currently one of the most popular role out there however the jail family is not perfect you
can talk to any data scientist who's working in the industry and they will tell you some of the
big issues with the data science jaw family in the industry starting with the lack of standardization
which leads to a lot of ambiguity a very huge scope and makes it difficult to make progress
within the career ladder and as well as make it difficult for job search purposes so before
we talk talk about the three reasons I wanted to take some time to define the data scientist
and the data analyst to our family to show you like what is different and what is similar I'm
going to segment the rules into four bucket and then I'll talk through each the first bucket is
math and statistics the second bucket is coding the third bucket is software and tooling and the
last one is my other bucket so quickly going through both of these jaw families let's talk
about math and stats for a data analyst you're expected to understand descriptive statistics
basic statistics and foundational math for a data scientist role you're expected to know Advanced
statistics linear algebra calculus and more from coding standpoint a data analyst is expected to
know SQL a bit of python to complement your work in SQL but in majority of the rules
data analysts primarily work with SQL for data scientists you're expected to know SQL as well
as scripting languages such as Python and R in respect to software and tooling data analyst is
expected to know tools such as Excel Google Sheets for visualization they're expected to know tools
such as tableau microstrategy Energy power bi and ETL for data scientists you're expected to
know ETL on how to extract your data where the data lives and for the tools you're expected to
know our studio if you work with our Jupiter notebook Google collab notebooks understand
how to work with code reviews understand how code review process works and to be able to do
it on your own and the last in my other bucket for data analysts you're expected to have good
communication analytical skills problem solving skills for data scientists you are expected to
have good communication skills problem solving skills and having a good business understanding
so in my other bucket basically the required skills for data analysts and data scientists
as both now that we understand like what are the core skills acquired for both roles let's talk
about what makes data analyst jaw family better than data scientists there are three reasons
primarily data analysts might be a better fit for you number one is that a job family has low
barrier to entry for a data scientist rule most cases you're expected to have a master's degree or
PhD in a lot of cases if it is for a data analyst role PhD is not one of the requirements there are
a lot of data analysts who have taken boot camps certificates online courses as well as Bachelor
and master's degree to enter the field there is a wide variety of entry points for a data
analyst role now that also means that if it's low barrier to entry that means a lot more people will
enter and then there will be a lot of competition but that's not the point of this conversation yes
that that might happen but at the same time your barrier to entry is lower than a data scientist
role which means that it would be for somebody who just wants to get into data analytics it
would probably be if you're planning to do a sell teaching route data analytics is something that
you might be able to teach on your own or take certain online certificates and courses to help
yourself prepare for that role talking about data analysts and data scientist toolkit one
of the common skills that both roles need to learn is Python and I found this free ebook to
Learn Python that I wanted to share with you all the ebook is created by HubSpot who is sponsoring
this section of the video in this ebook you'll find introduction to python with the lens of
data analytics and get educated on libraries such as pandas numpy and more what I really like about
this ebook is that it breaks down python Concepts in very simple fashion for example while loop is
one of the concepts in Python that is not very intuitive to get at first but if you read the
examples given in this ebook it goes into a lot more detail I'm linking the ebook in description
below it's available to download for free thanks to HubSpot and now let's talk about the reason
number two why data analytics might be a better career choice for you the second reason why data
analytics might be better fit for you is in data analytics oftentimes you are building tangible
things for example you are building dashboards You're Building reporting in a data scientist
role you're often doing Advanced statistics and building models and using machine learning a lot
of your work tends to be research focused many times there is no tangible output that you can
point to show your impact whereas in a data analyst role you actually have let's say if
your worker wires for you to build dashboards or your build rapport you're able to have tangible
artifacts that you can show to prove the value of your work and if you're the type of person
who enjoy building tangible links then data analytics might be a better fit for you than
a data scientist role the third which is one of my favorite reasons to consider data analytics role
is the scope and the standardization of the gel family for a data scientist role the jaw family
is not standardized oftentimes a data scientist at company a does not translate well to a data
scientist at Company B which leads to a lot of ambiguity and it makes it difficult to job search
as well for a data analyst role however things are way it's not perfect but it is more well defined
than a data scientist role in that case a data analyst at company a is going to easily translate
to a data analyst at Company B which makes your interview process easier and within the company
you're also able to have a better understanding of your scope and able to work toward making a
progress in your career I do hope that the data scientists drop family eventually comes to a point
where data scientists at company A and B means the same thing but we're not there yet so I know these
three points might not be that big of a deal for everybody but it might be a big deal for
somebody again the last point that goes without saying is the type of work that you would enjoy
if you would enjoy doing more data sciency work such as building statistical and machine learning
models then you should definitely consider data scientists role regardless of all the drawbacks
that there are but if you enjoy building reporting doing data analysis in Excel doing dashboarding
then you should definitely consider the data analytics role now there is another role which
I didn't mention at all and I'll definitely cover it in my next video is the product NLS rule the
product analyst role is basically a mix of both of these roles but I do anticipate that a lot of data
scientists in the future will be converted to the product Analyst job family anyways these were the
three things that I wanted to mention that make the data analytics draw Family better than data
scientists do you agree with these points do you have any other points that you would like
to add let me know in comments and I will talk to you in a different video have a good one bye