HAN University

What you'll study

The programme combines core modules in data science theory with practical, project‑based learning. You will study topics such as data mining, supervised and unsupervised machine learning, time series analysis, natural language processing, and deep learning fundamentals, alongside data engineering topics like databases, data warehousing and cloud data pipelines. Emphasis is placed on model evaluation, reproducible workflows, interpretability and ethical considerations in data use.

Typical components include:

• Core modules: statistical methods for data analysis, machine learning, data engineering and software tools for data science (Python/R, SQL, version control, containerisation).
• Applied labs and workshops: hands‑on sessions with real data sets, use of cloud platforms, model deployment and performance monitoring.
• Electives and specialisation: options to explore advanced topics such as computer vision, natural language processing, advanced predictive analytics or business analytics.
• Project work and internship: substantial individual or team projects carried out in collaboration with companies, public organisations or research groups, culminating in a master’s thesis that applies methods to a practical problem.

Instructional methods prioritise practical learning: case studies, guest lectures from industry partners, collaborative projects and supervised thesis research. Small class sizes enable individual supervision and coaching on both technical and communication skills.

Entry requirements

Applicants are normally required to hold a relevant bachelor’s degree in a discipline such as computer science, information technology, mathematics, statistics, engineering, econometrics or a closely related field. Demonstrable proficiency in programming (typically Python or R) and basic knowledge of statistics and linear algebra are expected. Candidates without a directly related degree but with substantial relevant professional experience and demonstrable quantitative skills may be considered.

Other requirements typically include:

• Academic transcripts showing a completed undergraduate degree from a recognised institution.
• A curriculum vitae and a statement of purpose describing your motivation and any relevant project or work experience.
• Proof of English language proficiency if your previous education was not in English (accepted tests and levels are specified by the university).
• References or recommendation letters may be requested to support the application.

Career prospects

Graduates of the Master in Applied Data Science are prepared for a range of roles in both private and public sectors. Typical career paths include data scientist, data engineer, machine learning engineer, analytics consultant, business intelligence specialist and research or production roles in AI teams. The programme’s applied focus and industry collaboration aim to make graduates work‑ready for roles that require turning complex data into operational solutions, model deployment and data‑driven decision support.

Alumni find positions in sectors such as finance, healthcare, manufacturing, logistics, public administration and technology firms. The programme’s project and internship elements also help build a professional network and a portfolio of real‑world work that employers value.

Why study at HAN University

HAN University of Applied Sciences has a strong reputation for applied, practice‑oriented education and close ties with regional and national industry. The Applied Data Science master leverages HAN’s facilities — including modern computing labs and access to cloud environments — and its partnerships with businesses and public organisations for project collaborations and internships.

Students benefit from a teaching approach that balances technical depth with professional skills such as communication, project management and ethical data practice. Small cohort sizes enable focused supervision on thesis and applied projects. HAN’s location in the Arnhem–Nijmegen region places students within a vibrant ecosystem of tech companies, healthcare institutions and research organisations, facilitating networking and employment opportunities after graduation.