Data science uses algorithms to extract insights from structured or unstructured data. These insights then help predict future services, make informed decisions with the data, optimize operations, etcetera.

Various programming languages are used to extract these insights (R, Python, SQL, etc). Some basic resources to get started with learning these include:

1. R: freeCodeCamp is a non-profit that has some great resources for learning programming. Their R tutorial is short and pretty helpful.

2. Jupyter Notebook: Jupyter Notebook (the name comes from Julia, Python, and R which are the languages it initially started with support for) is an open-source web app that supports multiple languages and allows you to execute code in small cells as compared to just one big file, making experimentation a lot easier. It's also great for collaboration. Its documentation talks more about what Jupyter is and here's a quick tutorial to get started with Jupyter.

3. Python: It's a good idea to start with Python basics before getting into Python for Data Science specifically, just to get the hang of how the programming language's syntax looks and works. You can look more into it here. After that, here's a good tutorial to get started with Python for Data Science. It's a good idea to practice with some project ideas, as you learn the best by doing! Here's some ideas you can look into. If you'd rather read more about it/ read parallel to working on the project: here's a recommendation.