Core python (standard library) - data types and their import functions - operators and statements - functions and classes
Python modules - IDEs and terminal - pip and virtual environments
Data processing (pandas, numpy) - efficient vector processing - working with data frames - saving and writing data - performance tricks
Data visualization (matplotlib) - plot types, sub plots - legends, customizing
Regressions and ML (statsmodels, sklearn) - Statistics in Python - Machine Learning Algorithms
Webscaping (selenium, beautifulsoup, requests) - dynamic vs. static websites - error handling, proxy servers
Text Mining (spacy, sklearn, gensim) - preprocessing, tokenization - sentiment analysis and topic modeling
- Lehrende:r: Niklas Benner