Within the bustling industry of tech, there are a ton of different roles that all sound just about the same. Right?
From software engineering to programming, web development to data science to computer science, one might think these roles are interchangeable. Though they share some key similarities, they’re fundamentally different.
Two roles that often get mistaken for one another are data science and engineering. We’re looking today at what makes them both similar and different. Depending on what you want to do, this could inform any training or education you complete prior to looking for a job or changing careers.
Data science vs. software engineering
Software engineers, aptly named, use engineering and programming skills to create software on mobile devices, web applications, and other systems, games, or web pages. They use code in a variety of languages to not only create said applications, but continuously iterate and bug proof.
The job growth for software engineers is certainly one of the highest in tech, and moreover in any industry across the globe. Software engineer salaries are some of the highest in tech too, netting an average salary of $118,217 per year for all levels across the country (according to Glassdoor).
Data scientists and their roles are slightly more nuanced. A lot of what they do involves software engineering principles and skills, but with a finer lens on data analysis, market trends, statistics, and a creation of algorithms or data sets.
Data science job growth is equally as strong as it is for software engineering, but the average earning potential is slightly higher due to the more analytical nature of the role. In many cases, this involves additional schooling.
Similarities: Data science vs. software engineering
Overall, data science and software engineering share some key similarities. While not entirely transferable (in that, you couldn’t necessarily apply to both types of jobs with the exact same experience), there are some overlapping skills and responsibilities.
Algorithms, algorithms, algorithms.
According to Investopedia,
“An algorithm is a set of instructions for solving a problem or accomplishing a task. One common example of an algorithm is a recipe, which consists of specific instructions for preparing a dish or meal. Every computerized device uses algorithms to perform its functions in the form of hardware- or software-based routines.”
In both data science and software engineering, algorithms are a major part of the role. Whether creating them or optimizing them for speed and accuracy, both data scientists and software engineers utilize them in some fashion.
Teamwork and cross-department functionality.
Many people think those who work in deep tech wear headphones, put their head down, and type for eight hours a day. Not true!
Software engineers and data scientists alike actually sync quite often both within their own team and across departments — possibly with one another. Both need to be able to clearly communicate decisions to their teammates, leadership, and possibly stakeholders while rooting their findings in data (be it user, customer, or otherwise).
Job stability, growth, and earning potential.
As aforementioned, roles in data science and software engineering are two of the fastest growing in any industry, especially tech. The need for engineers and data scientists topped a list of 50 best jobs in America, citing overall job satisfaction, median salary, and number of openings as the reasons why.
Now, as more companies turn to technical solutions for their business, these tech roles (and others) are more needed than ever.
Differences: Data science vs. software engineering
In-depth knowledge of math, science, and other crucial subjects (plus educational background).
It’s important to note that neither role is “harder” or “easier” than the other. Rather, they’re each a specialization within a wide breadth of technological roles.
Data scientists use their analytical background, typically in mathematics or computer science, to sift through data and draw logical conclusions. They use programming languages and principles differently than a software engineer.
Software engineers are heavily involved in programming, specifically writing or interpreting code. They use those skills to create user-friendly software applications. While some programming languages have mathematical influence, you don’t necessarily need a background in these subjects.
Roles within an organization.
Though there are some overlaps in their day to day, software engineers and data scientists have fundamentally different job descriptions. For example, a software engineer:
- Will analyze what a user needs from software.
- Can design and develop the software based on what a user needs.
- Performs tests and automations before releasing software
- Takes a structured approach to software development.
- Creates and maintains end-user systems.
Whereas a data scientist::
- Analyzes Big Data and derives insights.
- Works with a huge volume of data to uncover insights for making informed decisions.
- Communicates clearly with business stakeholders.
Choose the career path that makes the most sense for you.
There’s no wrong choice in the debate between data science vs. software engineering, but your educational background and willingness to learn additional skills could help determine which role you choose.
In any case, getting started or brushing up on coding fundamentals is a great place to start regardless of the tech role you’re hoping to move into.
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