Potential Tutorials and Guides

### 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
- [x] 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](https://drive.google.com/drive/folders/1woozct-IcGhMlIhabhXyJXeMTXbsbhN-)
[Docker/Selenium Tutorial](https://drive.google.com/drive/folders/1fWIRwKI6vUj2RT8YPFHXG9eHMdNSfty_)

#### List of potential turorials
- ETL/Data Cleaning: Pandas, statsmodels, scikit-learn hackforla/ops#143
- Data Analysis: R hackforla/ops#145
- Data Visualization: Pandas, Seaborn, Matplotlib, Tableau hackforla/ops#144
- Data Engineering: SQL, NoSQL hackforla/devops-security#100
- Docker: installation (potential standalone guide), building containers, running python from within a container https://github.com/hackforla/knowledgebase-content/issues/68
- Webscraping: Python (Selenium, BeautifulSoup, Requests), Using APIs hackforla/devops-security#90
- Geospatial Data Analysis: GeoPandas, QGIS/ArcGIS hackforla/ops#148
- Text Analysis: nltk, SpaCy #153
- Data Ops: EC2, Lambda, RDS, Athena/Hive, Flask #154
- Stats: Logistic/Linear Regression, Experimental Design, Significance Testing, Bayesian Analysis #155
- Machine Learning/Stats: XGBoost, Random Forest #156
- Deep Learning: PyTorch, Keras, HuggingFace #157