De elektronische studiegids voor het academiejaar 2026 - 2027 is onder voorbehoud.





Computer Intensive Methods DL (3396)
Coordinating lecturer:Prof. dr. Ziv SHKEDY 
Member of the teaching team:dr. Rahmasari Nur AZIZAH 

Credits: 3,0
Study load hours: 81
Period: semester 1 (3sp)

Language of instruction: English
Exam contract: not possible

2nd Chance Exam1: Yes
Final grade2: Numerical
Tolerance3: See included in these programmes
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
    Programming in R DL (4432) 3.0 stptn  
 
Advising sequentiality bound on the level of programme components
 
 
  Following programme components are advised to also be included in your study programme up till now.
    Data Management DL (4431) 5.0 stptn  
 


Content

Simulations, Monte Carlo methods, Bootstrap techniques, Randomization methods, Permutation methods.


Compulsory course material
 

Copy of slides.
 

Recommended reading
  Bootstrap Methods and their Application,A. C. Davison ; D. V. Hinkley,Cambridge University Press,9780521573917,Available as e-book: https://ebookcentral-proquest-com.bib-proxy.uhasselt.be/lib/ubhasselt/de tail.action?docID=1218089&pq-origsite=summon

An Introduction to the Bootstrap,Bradley Efron; R.J. Tibshirani,Chapman and Hall/CRC,9780412042317,Available as e-book: https://www-taylorfrancis-com.bib-proxy.uhasselt.be/books/mono/10.1201/9 780429246593/introduction-bootstrap-bradley-efron-tibshirani


Organisational and teaching methods
Organisational methods  
Distance learning ✔  
Project ✔  


Evaluation

Semester 1 (3,00sp)

Evaluation method
Oral exam50 %
Open questions✔
Other exam50 %
Other Written project (in three parts).

Second examination period

Evaluation second examination opportunity different from first examination opprt
No


Learning outcomes
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Master of Statistics and Data Science
  •  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 has the habit to assess data quality and integrity. 

  •  EC 

    • The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application.

  •  EC 

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

  •  EC 

    • The student is capable of acquiring new knowledge.

 
Included in these programmesTolerance3
Y



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.