Explore the key differences between software engineers and data scientists – from daily tasks to required skills, salaries, and career growth. Learn which tech path suits your strengths and goals, and how to transition between the two roles.
Job seekers thinking about their future careers in tech may be trying to decide between the software engineer and data scientist route. While these two roles have a lot in common, there are key differences that will impact the day-to-day working life of the employee.
While software engineers focus on systems building, data scientists spend their time performing analysis and using statistical techniques. Both roles are growing in demand and will remain important in tech industries for the foreseeable future.
The most crucial differences between software engineering and data science are in terms of responsibilities. Both roles require creativity and problem-solving skills, as well as the patience to troubleshoot problems, but here’s where they vary:
Software engineer responsibilities:
Data scientist responsibilities:
A typical software engineering project could be the creation of a web app, whereas a data scientist could be working on customer churn prediction or sentiment analysis. These tasks are related, but different.
Many IT skills can be useful for careers in both software engineering and data science, but it’s important to hone your training in line with one or the other. A better understanding of which specific skills are required for each can help to decide which career you’re best suited for.
Software engineer skills:
Data scientist skills:
Transferable skills like teamwork and communication will be useful in both software engineering and data scientist roles, as they’re required for successful career growth in tech. Complementing technical skills, these attributes are crucial for ensuring members of IT departments work well together.
A good place to start your education is a computer science, data science or other related degree, which will form a strong basis for your future career. These courses cover a broad spectrum of IT principles, which can be useful if you don’t know exactly which route you’re going to take.
Build upon this foundation with technical certifications like Amazon Web Services (AWS) or Microsoft Certified Azure Developer. Software engineers could find becoming a Certified Secure Software Lifecycle Professional (CSSLP) helps to advance their career, while the Google Professional Data Engineer certification is good for data scientists.
Research suggests that in 2020, more than ten per cent of all jobs advertised in the UK were related to software development and that trend has seen no signs of abating. The average national salary for a software engineer is £49,000 with finance, healthcare and entertainment particularly lucrative areas to be in.
Companies of all sizes now rely heavily on data to make informed decisions, solve problems and plan for the future, putting data scientists in demand. The average annual salary is also £49,000, with telecommunications, finance and energy good sectors to go into.
Making the choice between being a software engineer and a data scientist should be a combination of looking at the differences and talking to people already in the role. Weighing up how many of your skills, interests and career goals align with each job is a good place to start.
Speak to experienced security cleared professionals at an in-person event, then apply for software engineer jobs and data scientist roles. To transition from a software engineer to a data scientist, acquire skills in statistics, data analysis and machine learning algorithms, learn programming languages like Python or R and build a portfolio of data science projects.
To move in the opposite direction, learn backend web development languages and frameworks, build a portfolio of software projects, solidify your understanding of databases, and acquire proficiency in front-end technologies like HTML and CSS.
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