Course code 07 72 5260 30
ECTS credits 3
Course name in language of instruction 
Programming in Python
Course name in Polish Programming in Python
Course name in English 
Programming in Python
Language of instruction English
Type of classes
Teaching hours per semester
Lecture Tutorials Laboratory Project Seminar Other E-learn.
Contact hours 15 30 5
Distance learning No No No No No No No
Weighted grades 0.30 0.70 0
Unit running the course Instytut Informatyki
Course coordinator dr inż. Przemysław Nowak
Course instructors dr inż. Przemysław Nowak, mgr inż. Jakub Walczak
Prerequisites 
Familiarity with structured, procedural, and object-oriented programming as well as with concepts related to statistical data analysis
Course learning outcomes
  1. The student can characterize the Python programming language.
  2. The student can analyze code written in Python.
  3. The student can develop code in Python for data analysis and visualization.
  4. The student can develop programs in Python that involve a command-line interface.
  5. The student can develop web applications in Python using a web framework.
Assessment methods Oral presentations of solutions to laboratory assignments intermingled with oral quizzes related to the lecture material: outcomes 1, 2, 3, 4, 5.
Programme learning outcomes
    Grading policies Lecture: answers to questions related to the lecture material during oral presentations of solutions to laboratory assignments. Laboratory: oral presentations of solutions to laboratory assignments and answers to related questions.
    Course content LECTURE: 1. Introduction to the Python programming language. 2. Numerical computing and plots (NumPy, Matplotlib, SciPy). 3. Data analysis and visualization (Pandas, Seaborn). 4. Advanced code organization (namespaces, closures, decorators, object-oriented programming, exceptions). 5. Input-output operations and the standard library. 6. Web frameworks (Flask, Flask-SQLAlchemy, Jinja). LABORATORY: 1. Managing Python virtual environments. 2. Conducting a statistical analysis for a given dataset and documenting it as a Jupyter notebook. 3. Implementation of a simulation of a simple multiagent system in text mode. 4. Implementation of a simple web application.
    Basic reference materials 
    1. The Python Tutorial: https://docs.python.org/3/tutorial/
    2. Python Programming: https://en.wikibooks.org/wiki/Python_Programming
    3. Scientific Python Lectures: https://lectures.scientific-python.org/
    4. Think Python: How to Think Like a Computer Scientist: http://www.greenteapress.com/thinkpython/html/
    5. Dive Into Python 3: http://diveintopython3.problemsolving.io/
    Other reference materials 
    1. Dokumentacja języka Python: https://docs.python.org/3/
    2. Dokumentacja NumPy: https:/umpy.org/doc/stable/
    3. Dokumentacja SciPy: https://docs.scipy.org/doc/scipy/
    4. Dokumentacja Matplotlib: https://matplotlib.org/
    5. Dokumentacja Seaborn: https://seaborn.pydata.org/
    6. Dokumentacja Jupyter Notebook: https://jupyter-notebook.readthedocs.io/
    7. Dokumentacja Flask: https://flask.palletsprojects.com/
    8. Dokumentacja Jinja: https://jinja.palletsprojects.com/
    9. Dokumentacja SQLAlchemy: https://docs.sqlalchemy.org/
    10. Dokumentacja Flask-SQLAlchemy: https://flask-sqlalchemy.readthedocs.io/
    Course workload
    Type of classes Teaching hours
    Lecture 15
    Laboratory 30
    Other 5
    Study of the lecture material 10
    Warm-up homework 1
    Preparation for the practice 2
    Development of solutions to assignments 24
    Exercises following the tutorial 1
    SUM : 88
    Comments 
    Category „Other” includes participation in office hours, practical forms of assessment methods, scientific seminars, and workshops organized in cooperation with the business community.
    Updated on 2025-08-08 17:10:18