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.

Resources are arranged in a roughly hierarchical order with the most important core competencies listed first.

Beginner Tutorials

• 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
• Data Engineering: SQL, NoSQL
• Beginner Project (Pokemon): Pandas, requests

Where to practice SQL Queries

• https://sqlzoo.net/wiki/SQL_Tutorial
• https://mystery.knightlab.com/

Intermediate Specialization

• 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

Advanced Specialization

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