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Tutorials
Bonnie Wolfe edited this page Mar 19, 2024
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This is a space for curated resources that are important for doing data science at Hack For LA. These will be updated over time.
Resources are arranged in a roughly hierarchical order with the most important core competencies listed first.
- Introduction to Programming with Python
- Introduction to Command Line, Git and GitHub
- Data Analysis With Python: Pandas, Jupyter, EDA
- Data Analysis With R
- Data Visualization: Pandas, Seaborn, Matplotlib, Tableau, Looker
- Data Engineering: SQL, NoSQL
- Beginner Project (Pokemon): Pandas, requests
- Docker: installation (potential standalone guide), building containers, running python from within a container
- Webscraping: simple webapps and javascript for basic applications, python packages (Selenium, Beautiful Soup, Scrapy) for fully automated webscraping.
- Text Analysis: nltk, SpaCy
- Geospatial Data Analysis: GeoPandas, QGIS/ArcGIS
- Data Ops: EC2, Lambda, RDS, Athena/Hive, Flask
- Data Transformations with SQL and Python
- Stats: Logistic/Linear Regression, Experimental Design, Significance Testing, Bayesian Analysis
- Machine Learning/Stats: XGBoost, Random Forest
- Deep Learning: PyTorch, Keras, HuggingFace