Google Colab provides the ability to create and run Jupyter Notebooks within the Google ecosystem, making it straightforward to collaborate and share them. Jupyter notebooks are rapidly becoming a standard method of creating and documenting reproduceable research, because notebooks can contain text, code, and the output of that code (including other media types, such as images and audio) within a single document. While Colab is not necessarily meant for intensive projects, it does provide an excellent environment for fast prototyping, courses, and other smaller-scale uses. Whereas a full Jupyter environment can support multiple language "kernels," Colab supports only Python at this time. If you would like to use a Jupyter environment for more robust research or computational needs, Jupyter is available for use on Strelka.
Below are some introductory materials covering Jupyter notebooks as well as common tools for data analysis and AI research, such as Python, pandas, and NumPy. Each link is a Colab notebook created as part of an NSF tutorial on supporting data science with Jupyter notebooks, which you can make a copy of to save it in your own Google Drive (please note that while these are excellent introductory materials, they may require some tweaking if you want to run every example yourself, such as downloading example datasets, adjusting paths, etc.):