Advanced Programming in Python (4427)
  
Coordinating lecturer :Prof. dr. Frank NEVEN 
  
Co-lecturer :Prof. dr. Bart MOELANS 
 dr. larissa CAPOBIANCO SHIMOMURA 
  
Member of the teaching team :De heer Sebastián BUGEDO BUGEDO 
 De heer Thomas MUNOZ SERRANO 

Language of instruction : English
Credits: 5,0
  
Period: semester 2 (5sp)
  
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 Python (3306) 5.0 stptn
 

Prerequisites

The students can write simple imperative programs in Python. In particular, they can utilize primitive types, strings, lists, dictionnaries, sets, iteration, conditions, procedures and functions, and they can debug programs.


Content

This course introduces advanced Python topics, such as regular expressions, files and exceptions, object oriented programming and recursion, as well as basic algortihms on graphs, pandas and numpy.


Organisational and teaching methods
Organisational methods  
Lecture ✔  
Practical ✔  
Project ✔  
Self-study assignment ✔  
Teaching methods  
Exercises ✔  
Homework ✔  


Evaluation

Semester 2 (5,00sp)

Evaluation method
Written evaluation during teaching period60 %
Transfer of partial marks within the academic year✔
Homework✔
Take-home assignment✔
Written exam40 %
Transfer of partial marks within the academic year✔
Closed-book ✔
Evaluation conditions (participation and/or pass)✔
Conditions A minimum score of 40% on each of the three components of the evaluation (assignments, project and final exam) is required to pass the course.
Consequences The student will receive a score of maximum 9/20.

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
 

Compulsory textbooks (bookshop)
 

Textbook 1:

Intro to Python for computer science and data science, Paul Deitel, Harvey Deitel, First edition, Pearson

ISBN: 9780135404676


Learning outcomes
Master of Statistics and Data Science
  •  EC 

    • The student is able to efficiently acquire, store and process data.

  •  EC 

    • The student is capable of acquiring new knowledge.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
1st year Master Bioinformatics J
1st year Master Bioinformatics - icp J
1st year Master Data Science J
exchange bachelor informatica K 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.