Data Analysis in Python 2100-CB-M-D2PYTH
* Overview and comparison of Python working environments.
* Tools for managing project code.
* Advanced data structures (lists, tuples, dictionaries, sets) and their application to data processing.
* Program testing.
* Extended elements of object-oriented programming.
* Elements of functional programming in data analysis.
* Modules and libraries.
* Processing text files.
* Processing binary files.
* Processing selected popular file formats.
* Elements of program performance analysis.
* Using regular expressions to clean and process data.
* NumPy library.
* pandas library.
* Examples of web scraping.
Course coordinators
Type of course
Mode
Prerequisites
Prerequisites (description)
Learning outcomes
Knowledge:
* Methods for acquiring and analyzing data correctness (K_W05)
Skills:
* Methods for ensuring the security of created software (K_U02)
* Methods for ensuring the quality of created software (K_U06)
Competencies:
* Analyzing the correctness of acquired data (K_K03)
Assessment criteria
Programming projects written during the semester.
Final assessment for higher grades.
Use of AI tools: Level 1 specified in the Resolution of the Teaching Council of WNPiSM No. 29/2025 of May 7, 2025, on detailed rules for the use of artificial intelligence tools in the teaching process – no AI used.
Bibliography
* Matthes, Eric. "Python Crash Course: A Hands-On, Project-Based Introduction to Programming." 3rd ed., no starch press, 2023.
* McKinney, Wes. "Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Jupyter." 3rd ed., O'Reilly Media, 2022.
Additional:
* Danjou, Julien. "Serious Python: Black-Belt Advice on Deployment, Scalability, Testing, and More." no starch press, 2018.