Attendance policy:
Students are permitted one unexcused absence during the semester. Any absence may be excused within 7 days after the reason for absence ends, provided appropriate documentation is submitted. Missed classes can be made up by attending a session with another group. This must be arranged in advance with the instructors. Please note: when attending a different group’s session, there may not be enough seats or computers available. For this reason, it is strongly recommended to coordinate with other students beforehand and, if possible, bring your own laptop.

Assessment Plan:

- Tasks: Task sets will be assigned throughout the semester and must be submitted via MS Teams by the posted deadlines. Submissions are mandatory but will not be graded. Instructors may request corrections or schedule a short discussion of your code to verify understanding.

- Final Project and Defense: At the end of the semester, each student must complete and submit a final project and then defend it in an oral session. Both the project and the defense will be graded. Together, they determine the final course grade (no separate grades for project and defense). Detailed instructions will be provided in December.

AI usage policy:
Students are allowed to use AI tools in accordance with the “Wytyczne dotyczące odpowiedzialnego wykorzystania AI w procesie kształcenia w AGH”. However, to prevent misuse of AI-generated solutions, instructors may require students to provide detailed explanations of their code when submitting assignments. This applies in particular to the Final Project.

Anaconda & Jupyter Notebook:
You can use your own computer during the classes, so we encourage you to install Anaconda (which includes Python and Jupyter Notebook) to work comfortably on your machine. See installation tutorial: https://www.youtube.com/watch?v=WUeBzT43JyY.

Tutorials:
1. Python Fundamentals 
2. NumPy - Arrays and Matrix Operations
3. Pandas - Python Data Analysis Library
4. Plotting and Visualization

Labs:
1. Python Fundamentals
2. NumPy 
3. Pi approximation
4. Pandas
5. US Baby Names Analysis
6. Analysis of variance with statsmodels
7. Analysis of Monthly Airline Passenger Numbers with statsmodels
8. Basic of Machine Learning with scikit-learn
9. Parallel Programming