Mastering Python: FastAPI, Docker, Streamlit, PostgreSQL

Begin by setting up a Python development environment using VS Code or PyCharm, install the Python extensions, and create a virtual environment in your project folder. Explore Docker for packaging.

Learn to build a simple Python API with FastAPI, install via pip, and run a uvicorn server. Expose routes like /items/{id} to deploy data science models quickly.

Learn to save and track code changes with GitHub by initializing a repo, staging and committing files, ignoring local caches, and pushing to a remote repository.

Set up a dockerized Postgres database for a university api that lists students and courses. Use docker compose with Postgres 15 alpine, volumes, and environment variables for local development.

Save docker compose and postgres changes with a git commit, then cover Python basics - modules, variables, lists, dictionaries, and a car class for API, test driven development.

Set up app and test folders, create a status endpoint, and test it with pytest and test client. Manage environment and dependencies with a requirements file and run uvicorn.

Connect to a postgres database with psycopg2 and create a /student endpoint to post new student data, verified by a pytest test.

Build and run a FastAPI app inside a Docker container using a Dockerfile and Docker Compose with Postgres, plus Swagger for UI and testing; commit and push changes to Git.

Add and test new endpoints for a FastAPI app: fetch all students and delete a student, implement CRUD progression, and use pytest with Postgres and Docker for automated validation.

Build a Streamlit front end that creates, lists, and deletes students via the university API, while managing changes with git and Docker and running the app on port 8501.