Higher Education in Flanders

[This programme is a cooperation between UHasselt/tUL and K.U.Leuven.]

Subject-related competences.

Epidemiology & Public Health Methodology: the students know different study designs, alternative statistical models, estimation methods and inference techniques, are able to select the most adequate approaches and to interpret results correctly, with appropriate argumentation.

Biostatistics: the students can coordinate and supervise the design and analysis stage of statistical studies; they master the whole spectrum of different statistical modelling techniques with in-depth theoretical insights (repeated measurements, survival analysis, ...) and can interpret results of corresponding statistical analysis correctly and accurately.

Bioinformatics: the students can coordinate and supervise the design and analysis stage of statistical studies; they know the necessary foundations of molecular biology and specialized knowledge and applied skills in database management, programming, statistical techniques, and knowledge discovery and integration, as required in the rapidly evolving research fields in genomics and proteomics.

General: they have developed an overall analytic and synthetic intellectual capacity; and are able to develop new concepts autonomously. Interdisciplinary competences: the students are able to get sufficiently acquainted with application areas (public health, food safety, biology, clinical trials, toxicology, genetics, genomics, proteomics...) in order to operate efficiently in a multidisciplinary research team; and they have developed interrelational skills and are trained to act in an international environment; they are able to write scientific reports and give oral presentations according to internationally established standards; the students have a mature and practical view on the role and the impact of biostatistics on policy making in public health, agriculture, environment, ... and are willing to face ethical considerations and issues.

Competence in one's own discipline
After completing the study program, the student
- has insight into the aim of studies and the consequences they have for further analysis (experimental studies, observational studies, survey methods, etc.)
- is familiar with sample sizes determination and data collection methodology
- can use graphical methods to explore, summarise and describe numerical data
- has a deep insight into the basic underlying theoretical concepts (probability theory, distribution theory, estimation theory, hypothesis testing, etc.)
- can recognize patterns and structures in data (independence, correlation, repeated measurements, missing observations, etc.)
- is familiar with and can use correctly different statistical modelling techniques (simple, multiple, and logistic regression, variance analysis, discrete data analysis, multivariate methods, non-parametric techniques, etc.)
- is able to correctly interpret and motivate the results of an analysis
- understands, considers and communicates the limitations of statistical analyses
- is capable of independently understand the underlying concepts of new methods sufficiently in order to correctly apply them to new situations
- views new developments and applications in statistics as a coherent process, and not as disconnected methodologies
- follows the professional literature and knows how to reasonably and efficiently collect statistical information (from periodicals, the Internet, etc.) and knows how to evaluate its quality
- has developed his/her analytical and synthetic thinking skills to be able to solve real statistical problems both  individually and in a team, and to be able to write and defend a sound scientific report (master's thesis).
 
Competence in auxiliary disciplines:
The student
- has sufficient skills in calculus (derivatives and integrals) and in applied linear algebra to understand the background of the statistical methods applied
- has mastered standard word-processing, presentation software and Internet applications
- has thoroughly mastered several large statistical software packages, and is able to manipulate data across the various systems
- understands the specificity of statistical software, and knows which package and which procedure is most suitable for common data analyses
-  is able to correctly interpret output
- appreciates the combined action of statistics and fast calculation techniques, and is able to use this combination for statistical analyses that employ computer-intensive methods
- knows the methodology, masters the software, knows how to manage data in databases, and is able to demonstrate relationships among those data
 
Competence in fields of application:
- The student has sufficient scientific insight into several areas of application to demonstrate that the student deals with applied statistics in a proper way as 'data in context'. The student is capable of mastering this 'context' in a different discipline to be able to carry out an optimally adapted statistical analysis.
 
Interdisciplinary competence:
the student
- can prepare clear and professionally written (scientific) reports, also in an international context
- can prepare clear and motivated oral presentations (supported by relevant audio-visual resources) for a general as well as a scientific audience
- explored his/her skills to work in a team context, and can foster the proper group dynamic
- can plan both personal tasks and group assignments, finalize them with respect to deadlines
- has developed the skills to provide an effective consultancy service. This includes adaptation to the terminology of the client, clarification of the question's formulation, and ability of reformulating it into an understandable statistical procedure, propose an action plan, and produce final conclusions clear for the client
- is trained to work in an international context with fellow international students. Can work and function easily within an international context using English as the language of communication.

• Bachelor of Information Sciences
• Bachelor of Knowledge Engineering

• Bachelor of Biomedical Sciences

• Bachelor of Biology
• Bachelor of Chemistry
• Bachelor of Mathematics
• Bachelor of Physics

• Bachelor in Chemistry
• Bachelor of Applied Economic Sciences: Business Engineering
• Bachelor of Bio-Engineering Sciences
• Bachelor of Engineering Sciences

• Bachelor of Applied Economic Sciences
• Bachelor of Applied Economic Sciences: Business Engineering in Management Information Systems
• Bachelor of Medicine
• Bachelor of Psychology
• Bachelor of Sociology
• Bachelor of Transportation Sciences

In order to gauge whether the expected entry level matches your curriculum, we require that you submit a complete transcript of your academic records.

(1) A complete list of course titles for which you have obtained a credit should be part of this. Indicate the course size (in ECTS-credits).

(2) Also indicate the result you obtained, preferably according to the ECTS-scale; if a different scale is used, please provide an summary explanation on the meaning of the scores.

(3) For the courses that you deem most relevant as a preparation for the master that you are considering, provide a shot (about one half to one page) description according to the standard guidelines for an ECTS-study guide.

• Biostatistics
• Bioinformatics
• Epidemiology & Public Health Methodology

• Biostatistics
• Bioinformatics
• Epidemiology & Public Health Methodology

• Applied Statistics
• Biostatistics
• Bioinformatics

• Applied Statistics
• Biostatistics
• Bioinformatics

• Applied Statistics
• Biostatistics
• Bioinformatics