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.
Mode
Prerequisites
Prerequisites (description)
Course coordinators
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.
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.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: