Why You Should Become a Data Analyst and NOT a Data Scientist

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