University: Technical University of Košice
Faculty: Faculty of Electrical Engineering and Informatics
Department: Department of Cybernetics and Artificial Intelligence
Course Number: 26001074 Course Name: Python Programming
Type, scope and method of learning activities:
Course Type: Lecture, Laboratory exercise
Recommended scope of the course content (in hours):
Full-time study (hours per week): -
Part-time study (hours per semester): ST 2,2
Study Method:
Number of credits: 6
Recommended semester of study: ST
Recommended semester Study programme Study grade Study Method
1.rok ST Intelligent Systems (IntS_Bc_D_sk)
Intelligent Systems (IntS_Bc_D_en)
Level of study:
Course completion requirements:
Assessment and completion of the course: Credit test and examination
Continuous assessment: Student passes the continuous assessment and receives credits when he or she meets the requirement to obtain at least 21% out of 40%.
Credit test
Final assessment: Student passes the final assessment and passes the examination when he or she meets the requirement to obtain at least 31% out of 60%.
Overall assessment: Overall assessment is the sum of the assessments obtained by students in the assessment period. The overall result is determined in accordance with the internal regulations of the Technical University in Košice. (Study Regulations, the internal regulation principles of doctoral studies)
Learning outcomes:
The course aims to acquiant students with programming in Python and to explain procedural, object-oriented and functional programming. The student who passes the course gains knowledge about data structures, algorithms and optimization methods.
Brief course content:
1. Python syntax, basic language constructs
2. Methods, iterative programs, recursion
3. Lists, dictionaries
4. Sorting and search algorithms
5. Algorithm complexity, optimization, dynamic programming
6. Divide-and-conquer algorithms
7. Search trees
8. Program testing and debugging; exceptions and errors
9. Object-oriented programming in Python
10. Simulations and computational models
11. Work with data structures in Python – numpy, pandas, matplotlib
12. Graphical User Interfaces in Python
Recommended Reference Sources:
• GUTTAG, J. V.: Introduction to Computation and Programming Using Python. Revised and expanded edition, MIT Press, 2013
• CORMEN, T. H. – LEISERSON, C. E. – RIVEST, R. L. – STEIN, C.: Introduction to Algorithms. 3rd edition, MIT Press, 2009
• RASCHKA, S.: Python Machine Learning. 1st edition, Packt Publishing, 2015
• CHUN, W. J.: Core Python Programming. 2nd Edition, Prentice Hall, 2006
• McKINNEY, W.: Python for Data Analysis: Data Wrangling with Pandas, Numpy, and IPython. 2nd Edition, O’Reilly Media, 2017
• SEVERANCE, C.: Python for Everybody: Exploring Data in Python 3. 1st edition, CreateSpace Independent Publishing Platform, 2016
Recommended optional program components:
Languages required for the course completion: Slovak, English
Course assessment:
Total number of students assessed: 352
  A B C D E FX  
  13% 16% 22% 20% 6% 24%  
prof. Ing. Peter Sinčák, CSc.
Ing. Ján Magyar, PhD.
Last modified: 31.08.2023
Approved by: person(s) responsible for the study program