Potential Tutorials and Guides #46

Dependency

• Re-evaluate once the tutorial issues are complete.

Overview

Guides and tutorials to be produced to help new data scientists and analysts with tasks assigned to them at Hack for LA.

Action Items

Cop Lead

• Define our top three from the below list
• Send issue back to product team.

Product team

Make issues for the top three

Resources

Tutorials Folder
Docker/Selenium Tutorial

List of potential turorials

• ETL/Data Cleaning: Pandas, statsmodels, scikit-learn GHPAGES-DOCKER needs to be updated - GitHub Pages Ruby Gem devops#143
• Data Analysis: R Create a deployment checklist for deploying a project into production infrastructure devops#145
• Data Visualization: Pandas, Seaborn, Matplotlib, Tableau GHPAGES-DOCKER needs to be updated - Ruby devops#144
• Data Engineering: SQL, NoSQL Pre-work Checklist: DevOps: Cody Johnson devops-security#100
• Docker: installation (potential standalone guide), building containers, running python from within a container Create a Guide/Template: Using Docker knowledgebase-content#68
• Webscraping: Python (Selenium, BeautifulSoup, Requests), Using APIs CONTRIBUTING.md ToC out of date devops-security#90
• Geospatial Data Analysis: GeoPandas, QGIS/ArcGIS 2025 DevOps Community of Practice Agendas devops#148
• Text Analysis: nltk, SpaCy CoP: Data Science: Create Text Analysis Tutorial #153
• Data Ops: EC2, Lambda, RDS, Athena/Hive, Flask CoP: Data Science: Create Data Ops Tutorial #154
• Stats: Logistic/Linear Regression, Experimental Design, Significance Testing, Bayesian Analysis CoP: Data Science: Create Stats Tutorial #155
• Machine Learning/Stats: XGBoost, Random Forest CoP: Data Science: Create Machine Learning Tutorial #156
• Deep Learning: PyTorch, Keras, HuggingFace CoP: Data Science: Create Deep Learning Tutorial #157