De elektronische studiegids voor het academiejaar 2025 - 2026 is onder voorbehoud.





Inference for Statistics and Data Science (4564)
  
Coordinating lecturer :Prof. dr. Olivier THAS 
  
Co-lecturer :Prof. dr. Inigo BERMEJO DELGADO 

Language of instruction : English
Credits: 3,0
  
Period: semester 1 (3sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
 
Sequentiality
 
   Mandatory sequentiality bound on the level of programme components
 
 
  Following programme components must have been included in your study programme in a previous education period
    Concepts of Probability and Statistics (1798) 5.0 stptn
    Linear Models (3560) 5.0 stptn
 

Prerequisites

The student knows the basics of statistical inference and probability and linear models.


Content

In this course students will learn about some more advanced and state-of-the art statistical inference issues and techniques that are relevant for modern applications that go beyond the scope of traditional statistical methods :

  • prediction versus association

  • observational versus experimental studies

  • basics of causal inference and causal machine learning.

Examples (with R code) will also be discussed.


Organisational and teaching methods
Organisational methods  
Lecture ✔  
Project ✔  
Teaching methods  
Group work ✔  
Paper ✔  


Evaluation

Period 1    Credits 3,00

Evaluation method
Written evaluaton during teaching periode50 %
Transfer of partial marks within the academic year✔
Homework✔
Report✔
Written exam30 %
Transfer of partial marks within the academic year✔
Paper✔
Take-home assignment✔
Oral exam20 %
Transfer of partial marks within the academic year✔
Debat✔
Use of study material during evaluation✔
Explanation (English)All course materials and own notations may be used.
Evaluation conditions (participation and/or pass)✔
Conditions To get a pass mark, the student must pass for each of the following parts: (project, paper and oral exam).
Consequences If the condition is not met, the final mark will by the minimum of: - 9 - the total score of all evaluation components.

Second examination period

Evaluation second examination opportunity different from first examination opprt
Yes
 

Compulsory course material
 

Course notes or slides will be made available on Blackboard. 


Learning outcomes
Master of Statistics and Data Science
  •  EC 

    • The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software.

     
  •  DC 

    • ... correctly using state-of-the-art analysis methodology.

     
  •  DC 

    • ... correctly using state-of-the-art design methodology.

  •  EC 

    • The student can critically appraise methodology and challenge proposals for and reported results of data analysis.

  •  EC 

    • The student can work in a multidisciplinary, intercultural, and international team.

  •  EC 

    • The student is an effective written and oral communicator, both within their own field as well as across disciplines.

     
  •  DC 

    • The student is an effective writer in their own field.

  •  EC 

    • The student is capable of acquiring new knowledge.

  •  EC 

    • The student knows the ethical, moral, legal, policy making, and privacy context of statistics and data science, and always acts accordingly.

     
  •  DC 

    • The student can explain basic principles regarding ethics and integrity in general.

     
  •  DC 

    • The student can explain ethical issues and dilemmas within the fields of statistics and data science.

  •  EC 

    • The student knows the international nature of the field of statistical science and data science.

  •  EC 

    • The student routinely monitors his/her own learning process and adjusts and improves it accordingly.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
2nd year Master Bioinformatics J
2nd year Master Biostatistics J
2nd year Master Biostatistics - icp J
2nd year Master Data Science J
2nd year Master Quantitative Epidemiology J
Exchange Programme Statistics J



1   Education, Examination and Legal Position Regulations art.12.2, section 2.
2   Education, Examination and Legal Position Regulations art.15.1, section 3.
3   Education, Examination and Legal Position Regulations art.16.9, section 2.