A data analyst job and a computer science role are not, on the surface, identical. However, there are overlapping skills common to both professions: programming abilities, analytical talents, and a knack for problem-solving that drive results.
In this article, we take a look at foundational skills for computer science and data analytics fields and help you understand what makes them similar and what makes them different.
The Shared Technical Foundation
In both fields, Python is a dominant language. That said, it’s one that is used differently depending on the application. CS roles, for example, use it for building applications, algorithms, and designing back-end logic. Data analysts, on the other hand, use it for data manipulation purposes. Typically, this happens in libraries like Pandas and NumPy.
SQL, on the other hand, is universal in both fields. Computer science roles use it to build application databases. Analytical roles, on the other hand, use it for pulling, filtering, and aggregating data that is then used to generate reports.
Statistics and Math
Naturally, both jobs also have a heavy emphasis on statistics and math. This is particularly obvious in analytical careers, but certainly has a bearing on computer science roles as well.
CS tends to be grounded in probability and statistics, while data analytics is built upon a foundation of practical statistics. This includes everything from understanding averages and comprehending trends to demonstrating confidence in the face of patterns.
An ability to use statistical and mathematical formulas to interpret data is foundational in both career paths when working with data.
Obviously, knowledge that analytical roles involve working with data does not require you to stretch your imagination.
Data is also important in computer science roles. Data analysts are using numbers to answer specific business questions, while engineers are usually using it to feed systems or applications.
Logical Problem Solving
Finally, both fields require an ability to examine problems logically and develop granular solutions. In the world of computer science, this focus is very process-driven, with a clear emphasis on making sure that pieces connect.
When it comes to data analytics, the focus is similar. Here, the analyst is looking for answers within the numbers—helping businesses draw clear insights from dense information sets.
Where These Roles Diverge
There are obvious differences that can be viewed simply by reviewing job descriptions. For example, engineers write code for products that other people wind up using. This means that they need to carefully track every step of their workflow, test their programs thoroughly, and review everything carefully before the software actually goes live.
Because the work is typically public-facing, the stakes are generally quite high.
Data analysts, on the other hand, tend to focus significantly more on technical process. They place a higher weight on discovering information through complex processes and then presenting it in formats that are more comprehensible to the general public.
Often, these findings are viewable through dashboards, presentations, or even face-to-face conversations.
System Design Versus Business Context
In computer science, the focus is always on how connected pieces work within a wider system. This is true of applications as well as anything else that a computer scientist may focus on.
On the other hand, in the world of analytics, the focus is much more business-specific. They’re looking at numbers, but as far as the business that employs them is concerned, they’re there to discover answers.
The Tooling
It’s also worth noting that the tooling process can be pretty different between career paths. In computer science, the tools used are typically built specifically for production. This includes version control, testing frameworks, deployment pipelines, and so on.
In data analytics, the tools are more focused on answers. This includes spreadsheets, Tableau, or Power BI dashboards.
Nitty-Gritty Job Details
All of this is well and good, but what is it like on the experiential level to work within these fields? How does the credentialing process go? Are the salaries comparable? Are the careers equally in demand? And what about safety from AI replacement? Let’s break it down.
Computer science typically has the steeper formal barrier, with many software engineers expected to at least have a CS degree, along with a portfolio. Often, it’s this latter factor that is harder, particularly now as opportunities for entry-level work are diminishing.
Data analytics has a slightly lower formal barrier to entry. There are certainly degrees relevant to this career path, but it is possible to get your foot in with a more generalized business degree, along with provable relevant knowledge.
In terms of compensation, it is generally software engineers who make more. Their base salaries hover in the $140,000 to $170,000 range, while data analysts are closer to $80,000 to $115,000.
In terms of demand, it’s a little shaky. Both career paths have been in a state of fluctuation in recent years, and it is true that software engineering opportunities in particular have been shrinking recently. That’s possibly related to economic factors, but there’s also a related element of AI exposure.
In both cases, proven experience is going to be helpful. Both jobs are compressing in similar ways currently at the earlier stages.
How concerned should prospective candidates be? Honestly, there’s not a concrete answer to that question. We can only say now that both career paths are still alive and kicking, and though it may take more job applications to find a good fit than had been previously necessary, it’s still very possible to find lucrative work in both fields.
How to Tell Which Career Path Fits You
At the end of the day, despite the fact that there are overlapping characteristics between these two professions, they’re ultimately quite different.
The work of a computer scientist will appeal to people with a passion for building things. If you like to be creative while simultaneously being very technical, this is going to be the right choice for you.
On the other hand, if your primary focus is on problem-solving through numbers, data analytics will be of greater interest.
Both are rewarding career paths, both in a personal and financial sense. Choose the one that’s right for you.
