What is Data Science and what do Data Scientists do?
Data Science is a highly specialised technical field that combines several key elements – these include statistics, machine learning, and data visualisation.
The main aim of Data Science is to uncover valuable insights from data via several tried and tested techniques, from machine learning (a subset of AI/Artificial Intelligence, focused on algorithms that learn patterns in data) to reinforcement learning (where machines learn by interacting with their environment and receiving feedback).
Data Scientists bring the raw data they analyse to life. They extract, analyse and interpret huge volumes of data from lots of sources, and then present findings from the data in clear, understandable formats. To handle the data they work with, Data Scientists use many tools – including AI, machine learning, data mining (uses machine learning to find patterns in large volumes of data) and algorithms.
In the age of data, the real power is not in collecting information, but in learning from it.
– Prof. Dr Talha Ali Khan, Vice President of Research & Program Leader for MSc Data Science
What is Software Engineering and what do Software Engineers do?
Software Engineering is a systematic process that involves designing, developing, user testing and maintaining software via several techniques. Software Engineering is vital to ensuring that software is a quality product, reliable and relatively easy to maintain.
When it comes to Software Engineers, these are professionals that essentially build the digital world that we all use day to day – from phone operating systems to office applications (Microsoft Word, Excel), internet browsers and many more.
Software Engineers are also in high demand right now. With a predicted employment growth of 17% through to 2033, the sector is also expected to be worth over 5 billion by the same year – making it a highly lucrative and prized industry.
A key principle of any effective software engineering, not only reliability-oriented engineering – simplicity is a quality that, once lost, can be extraordinarily difficult to recapture.
– Prof. Dr. Rand Kouatly, Professor for Information Technology & Communication. Program Director Software Engineering & Vice-Dean for Business and Art & Design
Key Differences Between Data Science and Software Engineering
Now that we’ve laid the groundwork for Data Science and Software Engineering, let’s dive into some of the key differences between both industries.
It’s easy to assume that both sectors are very similar, as they both involve programming and technology.
Where the two sectors diverge however, is in their focus, their use of methodology, and how they are applied.
| DATA SCIENCE | SOFTWARE ENGINEERING |
| Has a process-orientated approach. | Has a framework-orientated approach. |
| Uses data-visualisation tools, data analytics tools and database tools. | Uses programming instruments, database devices, CMS devices, testing devices and integration apparatuses. |
| Includes stages such as Hadoop and MapReduce. | Includes stages such as information modelling, commerce arranging, programming, upkeep and venture administration. |
| Data sources can include Sensor Data, Transactions and Public Data Banking. | Data sources can include End-User Needs and New Features Development. |
| Careers in Data Science can include Data Scientist, Data Analyst, Business Analyst, Data Engineer and Big Data Specialist. | Careers in Software Engineering can include Release Engineer, Testers, Data Engineer, Product Manager, Administrator and Cloud Consultant. |
| Involves fundamental information of domains, algorithms, big data handling, data mining and structured or unstructured information. | Involves knowledge of core programming languages, testing, construct tools, set-up tools and discharge administration tools. |
Education Path: Data Science vs Software Engineering Degrees
Now you’ve got an understanding of the similarities and differences between Data Science and Software Engineering, let’s investigate the educational paths available to you.
To become a Data Scientist, you’ll first need to complete a bachelor’s degree either in data science or a directly related subject area, such as computer science, statistics or mathematics. This will give you a good grounding in the core areas of Data Science, such as programming, machine learning and data visualisation.
Then, to further your studies and career prospects, you can study a Data Science MSc. At UE, a master’s in data science will enable you to gain in-demand data science skills across all industries in today’s job market.
According to Coursera, holders of a master’s degree in data science earn around $297 extra per week, as well as improve their chances of specialising into a more in-demand position.
For Software Engineering, the traditional route to a career in the sector involves studying a bachelor’s degree in computer science, mathematics or software engineering itself.
A master’s degree in Software Engineering is the most logical next step and will give you advanced skills and knowledge in the field – ideal for seeking a high paying job or specialising within a niche area.
The M.Sc. Software Engineering degree at UE provides students with the most the state-of-the-art technology, enabling them to work and innovate in the automotive domain and any other business domain available in the market today.
For both sectors, taking on a postgraduate degree will help you to differentiate yourself from normal bachelor’s graduates (within a very competitive job landscape).
Essential Skills Required for Data Science and Software Engineering Careers
Applicable to both the Data Science and Software Engineering sectors is the requirement to be proficient in several programming languages.
These include Python and SQL, as well as the ability to highlight issues, problem solve and implement effective strategies.
Below is a breakdown of the differences in essential skills required for Data Science and Software Engineering career paths.
| DATA SCIENCE | SOFTWARE ENGINEERING |
| Must be able to understand and apply statistical methods to look at data and interpret trends. | Must be adept at programming languages such as Java, C++ and JavaScript. |
| Must be able to visually represent data to communicate findings and insights to stakeholders. | Must be good at identifying and fixing programming bugs to ensure software runs and performs smoothly. |
| Must be able to use a variety of techniques to extract information from large datasets. | Must be comfortable using development frameworks such as Scrum and Agile to allow streamlined project management. |
| Must be able to design and use algorithms to allow machine learning systems to work effectively. | Must be able to design efficient and scalable software systems and architectures. |
| Must be able to use tools and frameworks (Hadoop and Spark) to process large amounts of data. | Must be adept at using version control tools to manage and track codebase changes. |
Roles and responsibilities of Data Science vs Software Engineering Careers
The responsibilities within the worlds of Data Science and Software Engineering vary, but the most common types are as follows:
Data Science responsibilities
- Partnering with cross-functional teams to understand data requirements and deliver actionable insights.
- Analysing large datasets to uncover trends, patterns, and opportunities.
- Building and presenting detailed reports and visualisations to communicate findings to key stakeholders.
- Designing statistical models to interpret and make sense of complex data.
- Streamlining workflows and tasks using software engineering best practices.
Software Engineering responsibilities
- Partnering with cross-functional teams to seamlessly integrate software with existing systems and platforms.
- Building and deploying software solutions tailored to user needs and feedback.
- Architecting key software components to meet technical and business requirements.
- Ensuring software quality through rigorous testing and debugging.
- Maintaining clear and thorough documentation of development processes and methodologies.
- Overseeing version control and codebase management using tools such as Git.
Similarly, some of the roles you could find yourself working in for each sector include:
Data Science roles
- Machine Learning Engineer – Builds and deploys algorithms that enable systems to learn from experience, driving automation and unlocking new efficiencies across the business.
- Data Scientist – Specialises in uncovering insights from large, complex datasets by applying statistical techniques and building predictive models to guide key business decisions.
- Data Analyst – Responsible for processing and interpreting data to surface trends, producing detailed reports, and shaping informed strategic planning.
Software Engineer roles
- Software Architect – Defines the high-level structure of software systems, with a focus on scalability, performance and long-term maintainability.
- Quality Assurance Engineer – Responsible for rigorously testing software to detect and resolve defects, ensuring every release meets the highest quality standards.
- Software Developer – Crafts and maintains software applications, translating user needs and business goals into clean, functional code.
These roles and responsibilities show the breadth within each industry, and the possibilities available to you.
“Good programmers know what to write. Great ones know what to rewrite and reuse.” – Eric S. Raymond, US software developer
Career Opportunities in Data Science vs Software Engineering
Both Data Science and Software Engineering offer fantastic career paths, whichever direction you decide to choose.
From healthcare to technology, finance, e-commerce and more – the options open to you are varied, especially if you decide to study for a postgraduate degree at UE.
The table below demonstrates the variety of career opportunities waiting for you in either sector (and the general skills required).
| DATA SCIENCE | SOFTWARE ENGINEERING |
| Data Scientist (Python/R, Statistics, Machine Learning, Data Visualisation, Domain Knowledge) | Front-End Engineer (JavaScript Knowledge, Building Responsive Layouts with HTML and CSS) |
| Data Analyst (SQL, Data Visualisation Tools, MS Excel, basic statistics knowledge) | Back-End Engineer (Knowledge of Node.js, Python, Ruby or Java) |
| Machine Learning Engineer (Python/Java, Cloud Platforms) | Full-Stack Engineer (Switching Between Frameworks and Languages, Troubleshooting Tech Stack Layers) |
| Data Engineer (SQL, Python/Java, Big Data Technologies, Database Systems) | DevOps Engineer (Setting up CI/CD Pipelines, Managing Container Environments with Docker and Kubernetes) |
| Business Intelligence Analyst (BI Tools, SQL, Data Visualisation) | Security Engineer (Running Security Audits, Building and Maintaining Authentication and Encryption Protocols) |
| Data Architect (Database Technologies, Data Modelling, Data Integration) | Machine Learning Engineer (Building and Training ML Models, Monitoring Model Drift, Bias and Accuracy) |
| Data Scientist Manager/Director (Leadership and Management Skills, Technical Expertise in Data Science and Analytics) | Cloud Engineer (Automating Infrastructure Tools, Troubleshooting Issues, Setting up Scalable Servers) |
| Data Science Researcher (Advanced Knowledge in Data Science, research skills) | Mobile App Developer (Building and Maintaining Apps for iOS and Android, using native SDKs) |
Data Scientist vs Software Engineer Salary Comparison
The fields of Data Science and Software Engineering are both specialised, high-demand sectors with lucrative salary opportunities.
| JOB ROLE | AVERAGE SALARY (U.S.) | SALARY RANGE (U.S.) |
| Data Scientist | $122,738 | $37,500 – $196,500 |
| Software Engineer | $147,524 | $63,500 – $205,500 |
Both positions offer fantastic salary potential the more experienced you become.
Data Scientist vs Software Engineer: Which One Should You Choose?
Whether you choose to embark upon a career in Data Science or Software Engineering, both career paths offer great rewards and longevity in recognition of the level of skill, training and commitment each requires to succeed.
Once you have decided which path suits you best, deciding to study a postgraduate degree at the University of Applied Sciences Europe puts you in a great position once you graduate, thanks to high quality teaching, practical studies and an excellent learning atmosphere.
Conclusion
Whether you’re more inclined to study the more statistical, data and algorithm-based nature of Data Science, or the more programming-based, software involved requirements of Software Engineering, both sectors offer lucrative rewards in terms of salary and experience for your CV/resume.
Start your master’s degree journey at UE today and kick-start your career!
FAQ's
Similarly, as of 2025, top cities for Software Engineer opportunities in Germany include Berlin, accounting for 52% of total job openings, followed by Munich at 26% and Hamburg at 14%. This high demand goes in hand with excellent salary opportunities – the average salary in Germany for a Software Engineer is €59,893, with more lucrative openings in senior roles.
