The amount of hype data scientist job family has received is real. Let me tell you three reasons why data analyst job family might be a better one than a data scientist.
Quick disclaimer, this article is not to bash any job family. Every job family has a place of its own. This article is just for the purposes of comparing the two job families and three things that I’ve specifically noticed that makes data analyst job family stand out.
Back in 2008, Harvard Business Review published an article where they call data scientists the sexiest job of 21st century. Since then, data scientist job family has taken off and it is currently one of the most popular role out there. However, the job 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 job 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 about the three reasons, I wanted to take some time to define the data scientist and the data analyst job family to show you like what is different and what is similar. I’m going to segment the roles into four buckets 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 job families, let’s talk about math and 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 roles, 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 analysts is expected to know tools such as Excel, Google Sheets. For visualization, they’re expected to know tools such as Tableau, MicroStrategy, 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 RStudio if you work with R, Jupyter Notebook, Google Colab 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 is both. Now that we understand what are the core skills required for both roles, let’s talk about what makes data analysts job family better than data scientists.
There are three reasons primarily data analysts might be a better fit for you. Number one is that the job family has low barrier to entry. For a data scientist role, most cases you’re expected to have a master’s degree or PhD in a lot of cases.
Whereas 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 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 self-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 the role. Talking about data analyst and data scientist toolkit, one of the common skills that both roles need to learn is Python.
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, your work requires you to build dashboards or your build report, 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 things, 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 scope and the standardization of the job family. For a data scientist role, the job 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 scientist job 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 science work, such as building statistical and machine learning models, then you should definitely consider data scientist 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 blog is the product analyst role. 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 that make the data analytics job 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 blog. Have a good one. Bye.