Software Engineer vs Data Scientist: What’s the Difference?
The career opportunities in tech are abundant. You’ll find careers that fit all kinds of personalities and skills. Each career path focuses on unique specialties and skillsets to accomplish projects and contribute to organizational goals.
Most of these careers may require a similar level of experience and approach to developing websites, apps, and other programming tasks. Yet there’s often a key distinction in everyday tasks, responsibilities, or specific programming environment you’re motivated to work within.
Software engineering and data science are two of the most popular career paths in technology that have piqued the interest of college graduates and tech learners. With strong potential for high earnings, both fields work towards producing meaningful results for the organization they work in. While both require an essential programming foundation, they have distinct environments in which they work in.
If you’re exploring a career in tech, you might have these two popular fields on your list. As you sort out your next steps, we’re here to help. Next, we go through both the similarities and differences of what it means to be a software engineer and data scientist.
Similarities between the Two
Programming is the foundation on which many careers in tech are built off of, and software engineering and data science are no exception. Consequently, there are a few overlapping areas between the roles and responsibilities of these two career paths.
Both fields are highly technical in nature and require a specialized skillset. A basic understanding of programming language, coding, and other software and data familiarity are required. While the level of specialization in each area may vary, it’s still valuable to have a firm foundation in programming and other related fields.
For both roles, a large amount of analysis and critical thinking is required. Whether through creating new systems or finding relevant patterns, it becomes imperative to look at details and find opportunities for improvement in both camps.
A large component of analysis is what happens after all that information and insight are gathered. Those numbers don’t just sit in a spreadsheet but are incorporated into active projects. Both career paths incorporate informed decision-making and relying on research to guide their next steps.
The roles that software engineers and data scientists play in organizational success are profound. Their work transforms the way users interact with technology and accomplish their work. From asking your phone to set a reminder, to a seamless workflow for your organization—the projects these tech specialists achieve are highly influential. Not only that, but these careers also carry impressive salaries, earning potential, and future career growth that intrigue potential candidates.
Differences between Software Engineer and Data Scientist
Despite both careers following a strong theme of programming and a robust, detailed skill set, these two roles implement that skill and influence in different ways within an organization or project. Here, we share a few of the key software engineering vs data science differences you’ll want to know before jumping into your next career.
Focus of Work
A key difference between software engineers and data scientists is where they spend their time. Simply put, engineers work on developing apps and features for end-users while scientists gather and analyze data involved in those platforms. They can work together but each has their own arena in which they add value.
Since the focus of their work is different, so are their responsibilities. Software engineers are responsible for developing new software products and continuously patch and improve them to make sure they are working properly.
Data scientists, on the other hand, are responsible for sifting through important data and information that help to inform decisions and next steps in development. While programming ability may be required to some level, the work of the data scientist is much more aligned with using the analysis to influence and direct the organization.
Data scientists are rooted in a process-oriented approach to identifying and resolving an issue. They engage in research and use the insight they’ve discovered to propose informed and value-adding solutions. They rely on those data analytics to move forward.
Software developers, on the other hand, take a more methodical and continuous improvement approach to solve business problems. They rely on frameworks and other methodologies in their process of creating new apps, website design, or even launching a new video game.
Earning Potential and Growth
When looking at these careers from a salary potential and growth trajectory, there are also a few apparent differences. According to Indeed.com, the average salary for data scientists is $124,220 while the average salary for software engineers is $108,366.
Their job growth outlook for 2018-2028 also differs slightly, despite being faster than average trends. The Bureau of Labor Statistics projects a 21% growth for software development careers and a 16% growth for data science. Although a minimal difference, it’s worth noting as you explore future opportunities.
Take Your Next Step in Tech
Both data science and software engineering pose great opportunities for future career growth, earning potential, and opportunity to influence organizations. While they differ in the focus and environment of their work, their expertise and diligence can help make important projects happen.
Which career path intrigues you? Take the next step by developing the skills needed to thrive, regardless of the direction you choose.