Also considering analytics degrees? Compare data analytics and business analytics before deciding how much model development or business decision-making you want.
Which master’s should you choose?
Shortlist Data Science if you want to investigate datasets, build analytical models and communicate findings. Shortlist Artificial Intelligence if your priority is developing intelligent systems. These are starting points for choosing courses: a degree title alone cannot tell you how technical, specialised or suitable a programme is.
Machine learning can appear in both. Compare compulsory modules, available options and the dissertation before choosing. A Data Science course with substantial machine learning may fit your goals better than an AI course whose specialisms do not interest you.
A real UK course comparison
The University of Southampton offers both degrees. Its published course pages show different compulsory foundations while also listing overlapping advanced options. This is an example of one university, rather than a description of every UK master’s.
| What to compare | MSc Data Science | MSc Artificial Intelligence |
|---|---|---|
| Compulsory examples | Data Visualisation; Foundations of Data Science | Foundations of Artificial Intelligence; Intelligent Agents |
| Advanced interests | Check options such as deep learning and natural language processing | Check options such as deep learning and natural language processing |
| Your decision | Are data investigation and modelling central to your interests? | Are intelligent systems and agent behaviour central to your interests? |
Options can change and may have prerequisites. Use the curriculum for your intended entry year when checking a shortlist.
Match the course to your background
- Computer science graduate: check whether a course advances beyond topics you have already studied.
- Engineering or mathematics graduate: check the programme’s accepted disciplines and required computing preparation.
- Commerce or management graduate: investigate conversion programmes and quantitative prerequisites before assuming eligibility.
Before paying an application fee
- Map your transcript to the stated academic requirements.
- Separate compulsory modules from optional ones.
- Check the project format and relevant supervision areas.
- Compare total tuition, living costs and course duration.
- Compare the skills you will develop with several current job descriptions.
Neither title guarantees admission, a particular salary or employment. Choose using curriculum fit, preparation and affordability, rather than a general claim that one field is always better.
Two student profiles, two different decisions
Hypothetical applicant A: a computer science graduate who has already studied introductory machine learning and wants to implement NLP systems. The important comparison is advanced compulsory work, project supervision and assessment. Repeating introductory material may offer limited academic progression, even if the university is attractive.
Hypothetical applicant B: an engineering graduate with good mathematics but limited assessed programming. This applicant needs to check whether the programme accepts their preparation and whether teaching starts at a suitable level. Their first task is an academic transcript and prerequisite check.
Neither example predicts admission or employment. Use the course module comparison worksheet to document the evidence behind your own decision.
When Business Analytics belongs on the shortlist
If your preferred work is using analysis to support commercial decisions, also investigate Business Analytics and Data Analytics master’s in Ireland. Assess their technical content rather than assuming that a business-focused title means less coding.
Compare the cost of the whole degree
Use our total-cost worksheet to keep tuition, living costs and uncertain funding separate. A course that fits academically can still be unsuitable if its funding plan depends on unconfirmed income.
Official sources
Sources checked on 6 October 2026. University requirements and module availability may change.
Need help narrowing your options?
Share your academic background and preferred destination.
Explore another destination
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Entry requirements can decide before the course title does
Southampton’s AI MSc asks for relevant computing study and qualifying modules in advanced maths, advanced programming, and AI, advanced algorithms or machine learning. A general engineering degree with introductory coding does not establish those prerequisites. Map each requirement separately.
The full-time analytics course shortlist records the Data Science course’s full-time mode, 2027 intake, fees and international deadline, alongside Bath’s Business Analytics alternative. Compare the accepted subjects before comparing university reputation.
A practical test for “Data Science and ML” versus “Artificial Intelligence”
Choose an output you want to build: a reproducible customer analysis, a forecasting model, an NLP application or an intelligent agent. For each shortlisted degree, identify one required module and one assessable project that develops that output. Mark an elective as uncertain until availability and prerequisites are confirmed. If your target depends entirely on one optional module, ask what happens if it is not offered.
Application evidence to prepare
Attach the relevant transcript lines and syllabus descriptions to your own evidence sheet. Include the programming language, level of assessment and mathematical content. Keep course eligibility separate from country-equivalent grades and English requirements. Use the transcript worksheet before paying any application or offer-related charge.