๐ฅ Social Sciences
BeautifulSoup
Description: A library for pulling data out of HTML and XML files.
Use Case: Scraping online data, including social media platforms, news websites, and forums, for social science research on topics like public opinion, media studies, and digital sociology.
Documentation: Beautiful Soup Documentation
GitHub Repository: Beautiful Soup GitHub
Gensim
Description: A robust semantic modeling library, useful for unsupervised topic modeling and natural language processing.
Use Case: Analyzing large collections of text to uncover latent topics, trends, and relationships within social science data, such as policy documents, academic articles, and social media content.
Documentation: Gensim Documentation
GitHub Repository: Gensim GitHub
Matplotlib
Description: A plotting library for creating static, animated, and interactive visualizations in Python.
Use Case: Visualizing data related to social research findings, such as demographic trends, economic data, and survey results.
Documentation: Matplotlib Documentation
GitHub Repository: Matplotlib GitHub
NetworkX
Description: A Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
Use Case: Modeling and analyzing social networks, organizational structures, and communication patterns within social science research.
Documentation: NetworkX Documentation
GitHub Repository: NetworkX GitHub
NLTK (Natural Language Toolkit)
Description: A leading platform for building Python programs to work with human language data.
Use Case: Conducting linguistic analysis, sentiment analysis, and textual data processing for qualitative data analysis in social sciences.
Documentation: NLTK Documentation
GitHub Repository: NLTK GitHub
NumPy
Description: Fundamental package for scientific computing with Python.
Use Case: Performing numerical and statistical analysis on social science data sets, including experimental and survey data.
Documentation: NumPy Documentation
GitHub Repository: NumPy GitHub
Pandas
Description: Data analysis and manipulation library.
Use Case: Organizing, cleaning, and analyzing large datasets in social sciences, such as census data, economic indicators, and longitudinal study data.
Documentation: Pandas Documentation
GitHub Repository: Pandas GitHub
Plotly
Description: An interactive graphing library.
Use Case: Creating interactive and dynamic visualizations for presenting social science research findings, making complex data more accessible and engaging.
Documentation: Plotly Documentation
GitHub Repository: Plotly GitHub
scikit-learn
Description: Machine learning in Python.
Use Case: Implementing machine learning models for predictive analytics, classification, and clustering in social science research, such as analyzing social behaviors, policy impact, and demographic changes.
Documentation: scikit-learn Documentation
GitHub Repository: scikit-learn GitHub
spaCy
Description: An open-source software library for advanced natural language processing.
Use Case: Advanced text analysis in social sciences for entity recognition, dependency parsing, and lemmatization to process interviews, documents, and social media posts.
Documentation: spaCy Documentation
GitHub Repository: spaCy GitHub
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