Tutorials

This is a space for curated resources that are important for doing data science at Hack For LA. These will be updated over time.

Beginner Tutorials

• ETL/Data Cleaning: Pandas, statsmodels, scikit-learn
• Data Visualization: Pandas, Seaborn, Matplotlib, Tableau
• Data Engineering: SQL, NoSQL
• Data Analysis With R

Intermediate Specialization

• Docker: installation (potential standalone guide), building containers, running python from within a container
• Webscraping: Python (Selenium, BeautifulSoup, Requests), Using APIs
• Text Analysis: nltk, SpaCy
• Geospatial Data Analysis: GeoPandas, QGIS/ArcGIS
• Data Ops: EC2, Lambda, RDS, Athena/Hive, Flask

Advanced Specialization

• Stats: Logistic/Linear Regression, Experimental Design, Significance Testing, Bayesian Analysis
• Machine Learning/Stats: XGBoost, Random Forest
• Deep Learning: PyTorch, Keras, HuggingFace