Master of Science in Statistical Data Analysis

Increasing computer power and the professional need to extract objective information from observed data have led to complex databases. International professional and research standards in various fields demand high quality data analysis, performed by qualified statisticians. The Statistical Data Analysis programme offers scientists from a variety of fields intensive training in modern statistical methods and data analysis.

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Subsequent Master's Programme

1 year 60 credits

Faculty of Sciences

English

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Programme summary

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Off to a good start

After graduation

Full programme

(version 12 )

Course schedule 1st semester Course schedule 2nd semester Print

1 General Courses 12

Course	MT1	Semester	Language	Instructor	Crdt
Principles of Statistical Data Analysis	1	sem 1	en	Jan De Neve	6

Statistical Computing	1	sem 1	en	Veronique Storme	6

2 Majors 33

Subscribe to 33 credit units from 1 major from the following list. Subject to approval by the faculty.

2.1 Major Statistical Science

33

Course	MT1	Semester	Language	Instructor	Crdt
Analysis of Continuous Data	1	sem 1	en	Els Goetghebeur	6

Categorical Data Analysis	1	sem 1	en	Beatrijs Moerkerke	6

Statistical Inference	1	sem 2	en	Frederik De Keersmaeker	6

2.1.1 Elective Course List

15

Subscribe to no more than 15 credit units from the following list.

Course	MT1	Semester	Language	Instructor	Crdt
Geostatistics	1	sem 2	en	Ellen Van De Vijver	5

Econometrics: Time Series Analysis	1	sem 1	en	Gerdie Everaert	5

Epidemiology and Clinical Trials	1	sem 1	en	Brecht Devleesschauwer	5

Statistical Genomics	1	sem 1	en	Lieven Clement	5

Survival Analysis	1	sem 2	en	Els Goetghebeur	5

Causal Machine Learning	1	sem 2	en	Stijn Vansteelandt	5

Experimental Design	1	sem 2	en	Stijn Luca	5

Analysis of Clustered and Longitudinal Data	1	sem 2	en	Stijn Vansteelandt	5

Analysis of High Dimensional Data	1	sem 1	en	Lieven Clement	5

Statistical Consulting	1	sem 2	en	Els Goetghebeur	5

Advanced Econometrics: Non-Linear Methods	1	sem 2	en	Gerdie Everaert	6

Bayesian Statistics	1	sem 2	en	Koen De Turck	5

Subscribe to at most 5 credit units from 1 module from the following list.

2.1.1.1 Elective Courses Flemish Community

Subscribe to at most 5 credit units from the study programmes of the universities of the Flemish Community.

2.2 Major Computational Statistics

33

Course	Ref	MT1	Semester	Language	Instructor	Crdt
Statistical Modelling		1	sem 1	en	Els Goetghebeur	8

Big Data Science		1	sem 2	en	Dieter De Witte	5

Programming	UKV	1	sem 1	en	Peter Dawyndt	5

Databases		1	sem 2	en	Antoon Bronselaer	5

2.2.1 Elective Course List

10

Subscribe to no more than 10 credit units from the following list.

Course	MT1	Semester	Language	Instructor	Crdt
Bayesian Statistics	1	sem 2	en	Koen De Turck	5

Analysis of High Dimensional Data	1	sem 1	en	Lieven Clement	5

Causal Machine Learning	1	sem 2	en	Stijn Vansteelandt	5

Statistical Consulting	1	sem 2	en	Els Goetghebeur	5

Deep Learning	1	sem 2	en	Seppe vanden Broucke	6

Natural Language Processing	1	sem 2	en	Chris Develder	6

Subscribe to at most 5 credit units from 1 module from the following list.

2.2.1.1 Elective Courses Flemish Community

Subscribe to at most 5 credit units from the study programmes of the universities of the Flemish Community.

3 Master's Dissertation 15

Course	Ref	MT1	Semester	Language	Instructor	Crdt
Master's Dissertation		1	year	en	N. N.	15

Learning outcomes

Competence field 1: Competence in one or more scientific disciplines

Have advanced knowledge of a wide range of statistical methods for designing studies and analysing data.
Apply advanced knowledge of methods and techniques creatively to answer complex research questions.
Use statistical software to perform data analyses.
Interpret and critically assess statistical analyses.
Understand the role of statistics in the Scientific Method.
Demonstrate solid insight into state-of-the-art developments of at least one component of statistical data analysis.

Competence field 2: Scientific competence

Develop a well founded roadmap for solving a statistical problem.
Survey statistical theory, methods and techniques as reported in the scientific literature.
Select adequate statistical methods and techniques for answering a specific research question.
Use statistical analysis results to provide evidence based solutions for practical problems in an objective and scientific manner.
Discover interconnections and novel points of view.

Competence field 3: Intellectual competence

Independently and critically reflect on one’s own thought process and translate this into thoughtful conclusions and adequate responses.
Demonstrate an openness towards new scientific developments and their applications in broad scientific, economic or societal context.
Engage in lifelong learning.
Independently recognise and resolve or prevent complex statistical problems.

Competence field 4: Competence in collaboration and communication

Take responsibility and initiative in a group effort for a range of statistical problems.
Present one’s own research, ideas, thoughts and opinions clearly, respecting the intended audience, in written and oral form.
Report accurately on the design, conduct, analysis, assumptions, limitations and conclusions of statistical studies, both in oral and written form, respecting the intended audience.
Demonstrate professional behaviour, with an appropriate level of dedication, involvement, respect, accuracy, and tenaciousness.

Competence field 5: Competence in social responsibility

Handle data in a safe and ethical manner.
Integrate social responsibility into professional activities.
Promote the role of evidence as a basis for empirical research and policy making.
Integrate a commitment to scientific integrity into professional activities.