Programming Python

Semsters: WS 19/20, SS 20
Lecturer: Univ.-Lekt. Sebastian Böck Dipl.-Ing. Dr.techn.

This course taught the basic concepts of the Python programming language as well as some of the underlying theoretical concepts of computer science in the third semester (WS 19/20). Apart from Python’s own grammar and language, this course covered aspects of functional and object-oriented (OO) programming, communication over networks, programming of graphical user interfaces (GUI), and showed how to implement and use them in Python. Furthermore, “best coding practices” and the advantage of well-structured and reusable code were shown.

Literature

  • Severance, Charles. Python for Everybody. Exploring Data Using Python 3. California: CreateSpace Independent Publishing Platform, 2016. http://do1.dr-chuck.com/pythonlearn/EN_us/pythonlearn.pdf
  • Downey, Allen; et al. How To Think Like a Computer Scientist. Learning with Python. Green Tea Press, 2002.

In the fourth semester (SS 20) advanced programming techniques and Python concepts were taught. Among others, this covered aspects such as client /server programming, graphical user interfaces (GUI), data science and machine learning basics. Several Python packages and their usage were explained with real-world examples and simple artificial intelligence (AI) agents were used to solve problems in a (self-)learned way.

Group Project 1
Group: Cecilia Janz, Mara Gabriel, Theresa Hajek, Michaela Koffler, Alla Charnagolov, Lisa-M. Weidl
Format: Python Program
Topic: Hangman Game

Group Project 2
Group: Theresa Hajek, Lisa-M. Weidl
Format: Python Program
Topic: Text Analyzer

Group Project 3
Group: Lucian Gheorgitta, Lisa-M. Weidl
Format: Python Program
Topic: Web based News Source Analyzation Tool

We decided to focus on the UK as they recently (January 2020) left the European Union, which profoundly impacted Ethnic minorities facing rising overt racism, with levels of discrimination and growing abuse. We developed a real-time web application that shows what topics are discussed most regarding specific countries mentioned in the top news sources BBC, Daily Mail, Guardian, and Telegraph. For the news sources, we applied web scraping. To find out which topics are discussed most, we used text analyzation and created a dictionary based on a world cities database. We even applied machine learning and trained it with a limited set of categories of different topics to know which political and economic ones are the most popular.