Tutorials
Below you will find a list of available tutorials with instructions on how to install and use the tools we will be using in class.
Available Tutorials
PDF versions of the tutorials are also available on GitHub: https://github.com/danilofreire/qtm350/tree/main/tutorials.
Additional Resources
Further examples
- testing-ipython.ipynb: A Jupyter Notebook for showing Markdown and code cells
- testing-anaconda.py: A Python script for testing Anaconda installation
Suggested Books
- Python for Data Analysis by Wes McKinney
- Elements of Data Science by Allen Downey
- SQL for Data Scientists by Renee M. P. Teate
- Data Science on the Command Line by Jeroen Janssens
- Docker for Data Science by Joshua Cook
- Pro Git by Scott Chacon and Ben Straub
- Free programming books
Online Courses
Documentation
- Official Python Documentation
- NumPy Documentation
- Pandas Documentation
- Matplotlib Documentation
- SQLite Documentation
- Git Documentation
- GitHub Documentation
- Dask Documentation
- GitHub Co-Pilot Documentation
- Docker Documentation
For any questions or issues regarding these tutorials, please open a GitHub issue, submit a pull request, or create a discussion post.
Please do not forget that, in addition to the tutorials here, the course syllabus also contains a list of recommended weekly readings and additional resources.
I hope you like the tutorials and find them useful!