Hasselt University’s Master of Statistics and Data Science offers four specializations: Biostatistics, Bioinformatics, Quantitative Epidemiology, and new from 2020-21 onwards, Data Science.
The specialization 'Biostatistics' focuses on (generalized) linear models, multivariate methods, longitudinal data, Bayesian methodology, incomplete data, clinical trials and time-to-event analysis, as well as on specific methods and issues related to clinical trials.
The specialization 'Bioinformatics' focuses on (generalized) linear models, high dimensional data analysis methods, analysis of omics data, machine learning, computer intensive methods programming, and on very recent and important applications such as metabolomics, genomics, microbiome, …
The specialization 'Quantitative Epidemiology' focuses on (generalized) linear models, multivariate methods, modeling infectious diseases, mathematical biology, data mining, incomplete data, spatial and environmental epidemiology.
The specialization 'Data Science' is build on the handling, managing, visualizing and analysing many different types of complex and/or big data sources, with a focus on modern programming and computing environments, and with a solid knowledge of statistical principles.
For a detailed overview of the courses in the curricula of each specialization, we kindly refer to the study guide.
In the case you are looking for a top-level, indicative and comparative overview of the curricula of all four specializations, do consult this curriculum overview.
Major relevance to COVID-19
The programme is also highly relevant for many aspects of the COVID-19 pandemic. For example, in the specialization "Quantitative Epidemiology" students learn about the mathematical modelling for predicting the evolution of the outbreak and many other data-centric methods for studying effects on public health. In the specialization "Biostatistics" the design and analysis of clinical trials is studied for e.g. the safety, dosing and efficacy of antiviral therapies and vaccins. The search for COVID-19 diagnostics, monitoring techniques and treatments is also very strongly driven by high throughput molecular technologies. For example, qPRC for detecting viral RNA as a method for disease diagnoses, sequencing for viral variant detection for studying the geographical spread of the virus, ....
Data analysis methods for these complex data are studied in the "Statistical Bioinformatics" specialisation. More generally, the management of the COVID-19 pandemic is very strongly driven by many aspects of data sciences (including data management of heterogeneous and big data bases, data visualisation, computations, ....).
For more information on some of the data aspects of the COVID-19 pandemic, do visit the COVID-19 pages of our Data Science Institute.
The introductory phase, situated in the first semester of the first year, provides thorough fundamental knowledge of statistics, data management and programming (R, Python and SAS). Students will become familiar with data, statistical analysis, and, first and foremost, statistical concepts and reasoning. Apart from topic-related subjects, such as regression, a lot of attention is devoted to group-based project work.
In the second semester of the first year, the focus shifts from univariate models for continuous data to discrete data models and nonparametric approaches, as well as to correlated outcomes, combined with the discovery of associations. Within the second semester 3 subjects and an optional subject are common to all specializations. Additionally, students opt for two compulsory subjects specific to the chosen specialization.
The second year offers more specialized subjects. Each specialization offers a minimum of 27 ECTS of compulsory, specialized subjects. The master thesis of 24 ECTS is the main study/work subject of the second semester, and can be linked to an internship. Students are also invited to broaden their horizons by taking an optional course from the other specializations.
The university decree for Flanders is built around a credit point system that is based on the principles of ECTS (European Credit Transfer System). Each year of a full-time degree programme counts 60 credits. Ideally, these credits are equally spread over two semesters, i.e. 30 credits per semester. Given that the expected total study load per year ranges from 1,500 to 1,800 hours for a full-time programme, one credit represents a study load of 25 to 30 hours. Study load includes time spent in class, personal work and exams.