(in Polish) Python w analizie danych ekonomicznych 2400-M1ABPAD

• Learning programming in the age of LLMs
• "Error debugging"
• Data operations: Pandas
• Time and dates
• Fundamentals of object-oriented programming
• Visualizations: Seaborn, matplotlib
• Python on the internet: using APIs, JSON, XML
• Web scraping: requests, webdriver
• Using virtual environments
• Databases and Python
• Econometrics using Python – The statsmodels library

Szacunkowy nakład pracy studenta: 4ECTS x 25h = 100h
(K) - godziny kontaktowe (S) - godziny pracy samodzielnej
konwersatorium (zajęcia): 30h (K) 0h (S)
ćwiczenia (zajęcia): 0h (K) 0h (S)
egzamin: 2h (K) 0h (S)
konsultacje: 8h (K) 0h (S)
przygotowanie do ćwiczeń: 0h (K) 18h (S)
przygotowanie do wykładów: 0h (K) 0h (S)
przygotowanie do kolokwium: 0h (K) 0h (S)
przygotowanie do egzaminu: 0h (K) 18h (S)
Przygotowanie projektu zaliczeniowego: 0h (K) 24h (S)
Razem: 40h (K) + 60h (S) = 100h

Type of course

optional courses

Prerequisites (description)

Students enrolling in this course must know the basics of programming (understand what variables, flow control statements, loops, and functions are). A prerequisite is the ability to independently install the Python environment and Jupyter Notebook.

Course coordinators

Maciej Wilamowski

Learning outcomes

KNOWLEDGE
The participant will know how to use the Python language and its basic packages for the preparation, processing, and execution of selected data analyses, particularly in the scope of economic phenomena.
The participant will be familiar with the application possibilities of the presented data transformation and analysis methods.

SKILLS
The student can prepare the Python programming environment along with the necessary packages.
The student can read and transform data that forms the basis of economic analysis.
The participant can read data, determine data quality, perform basic data manipulations, and aggregate data conditionally.
The student can prepare complex data visualizations illustrating socio-economic phenomena.
The student can create econometric analyses in the Python language.

SOCIAL COMPETENCES
The participant understands that proficiency in Python requires continuous practice and improvement of one's skills, and this course provides the ability to seek knowledge.
The student understands that the programming language and the libraries they use are constantly changing.
The student understands that Python programming provides a range of universal competencies, and thanks to the many available libraries, they can apply their skills in many areas of economics as well as other fields of knowledge.

Assessment criteria

Grading will be based on a final exam (open-ended questions) and a term paper/project, the aim of which is to use the skills acquired during the semester by creating a web application.
The final project must be carried out in groups of 2-3 people.
Final exam: 40%
Project (carried out outside of class): 60%

Bibliography

The subject concerns a dynamically changing programming environment. Therefore, the classes will be based on materials prepared, updated, and made available by the instructor. There is no required literature.

Recommended Literature:
Jake VanderPlas, Python Data Science Handbook: Essential Tools for Working with Data, O'Reilly Media, 2016

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 2400-M1ABPAD in USOSweb