BSc Computer Science
Strong foundations in algorithms, data structures, and systems · Campus-based seminars and supervised labs · Portfolio capstone with external industry review
| Overview | Details | Notes |
|---|---|---|
| Degree | BSc | — |
| School | School of Computational Arts & Sciences | — |
| Field | Computer Science | — |
| Mode | On-campus | — |
| Duration | 3 years (full-time) or 4–5 years (part-time) | — |
| Credits | 180 ECTS | — |
Purpose
This program forms careful builders: engineers who can reason, communicate, and ship.
You will learn to design systems that are testable, observable, and humane—software that respects its users.
Curriculum structure
Year 1 (Foundations): programming, discrete mathematics, systems fundamentals, academic writing.
Year 2 (Core): algorithms, databases, networking, operating systems, secure development.
Year 3 (Practice): distributed systems, applied AI, product studio, capstone.
Teaching format
- Campus seminars
- Supervised labs with TA feedback
- Studio critiques for capstone milestones
Capstone
A 12-week project with problem framing, a technical design document, implementation, evaluation, and a live defense.
| Item | Summary | Notes |
|---|---|---|
| Tuition | €3,900 (EUR) | Tuition covers seminars, studio feedback, and oral examinations in selected core modules. |
| Scholarships | MDU Access Grant (needs-based) · Women in Computing Scholarship · Partners in Practice Scholarship | Limited awards; apply early where possible |
| Start terms | September / January | Program-specific deadlines may apply |
| Language | English | Some modules include academic writing support |
Covers supervised learning end-to-end: baselines, feature engineering, train/validation discipline, and error analysis. Students practice building reproducible evaluation reports, spotting leakage/overfitting, and communicating results with clear metrics and caveats.
- ml
- evaluation
- fundamentals
Build production-grade LLM applications beyond prompting: retrieval and grounding, safety and policy checks, tool use, and systematic evaluation harnesses. Students implement test suites for quality, hallucination risk, and regression, then iterate on architecture with measured evidence.
- llm
- evaluation
- responsible-ai
Design and operate responsible ML services: evaluation protocols, bias and slice checks, monitoring/alerts, rollback strategies, and incident-style postmortems. Emphasis on documentation, governance, and shipping models that remain reliable under drift.
- ai
- ethics
- mlops
An introduction to programming through small, complete systems: variables, control flow, functions, and working with files. Strong emphasis on readable code, incremental testing, debugging via tracing, and using version control to iterate with feedback.
- programming
- fundamentals
Study core data structures and complexity with hands-on implementation: stacks, queues, trees, hash tables, and graphs. Focus on choosing the right structure, analyzing time/space tradeoffs, and writing performance-aware code with clear invariants.
- algorithms
- performance
Model real domains in relational databases: schema design, normalization, SQL querying, indexing, and integrity constraints. Includes a mini project with audits and migrations, emphasizing data quality, reproducibility, and reasoning about consistency.
- databases
- sql
- data-modeling
- Responsible AI
- Evaluation Protocols
- Public Sector Technology
- Distributed Systems
- Observability
- Reliable Software
- Reproducibility
- Data Governance
- Research Operations
- · Upper-secondary qualification (or equivalent)
- · English proficiency (IELTS 6.5 / TOEFL iBT 90, or waiver)
- · Basic mathematics readiness (self-assessment + short diagnostic)
If you enjoy making decisions explicit—writing down assumptions, testing them, revising honestly, and respecting boundaries—this program is built for you.