Computer Intensive Methods (0599)
  
Coordinating lecturer :Prof. dr. Ziv SHKEDY 
  
Member of the teaching team :dr. Rahma AZIZAH 

Language of instruction : English
Credits: 3,0
  
Period: semester 1 (3sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
 
Exam contract: not possible
 
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 (4406) 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 (4405) 5.0 stptn
 

Content

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


Organisational and teaching methods
Organisational methods  
Lecture ✔  
Project ✔  


Evaluation

Semester 1 (3,00sp)

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

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
 

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


Learning outcomes
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.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  

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.