MSc Data Science

The MSc in Data Science at the University of Aberdeen provides comprehensive training in statistics, programming, machine learning, and big-data analytics to prepare students for data-driven careers. It suits graduates from a wide range of disciplines who want to develop strong analytical, technical, and problem-solving skills for industry or research.

Curriculum structure

Year of Study (one-year full-time):
Students begin with foundational modules such as Introduction to Programming, Introduction to Data Science, Statistics and Time Series Analysis, and Introduction to Python and R, which develop essential skills in coding, statistical reasoning, and working with diverse datasets. As the year progresses, they take advanced modules including Machine Learning, Data Visualisation, Advanced Statistics and Special Applications, and Image Analysis, enabling them to build predictive models, interpret complex data, create effective visualisations, and work with high-dimensional or image-based data. The programme concludes with a Data Science Project, where students apply end-to-end data-science methods—data cleaning, modelling, evaluation, and presentation—to address a real-world analytical problem.

Focus areas:
Programming; statistical analysis; machine learning; big-data analytics; data visualisation; predictive modelling; image analysis.

Learning outcomes:
Proficiency in Python and R; ability to analyse complex datasets; build and validate machine-learning models; create meaningful visualisations; conduct full data-science projects; apply data methods across disciplines.

Professional alignment (accreditation):
Prepares graduates for roles such as data scientist, data analyst, data engineer, business intelligence specialist, or research analyst across technology, finance, healthcare, public sector, and academic environments.

Reputation (employability rankings):
The University of Aberdeen maintains strong employability outcomes, and its data-science graduates benefit from high industry demand for analytical and machine-learning expertise.