Welcome to QTM 350! This course introduces key tools in modern data science, focusing on three essential aspects: reliability, reproducibility, and robustness. We will cover command line interfaces, version control with Git and GitHub, and literate programming using Quarto and Jupyter Notebooks. You will also learn about data storage and manipulation with SQL and Pandas, data visualisation using Matplotlib, Seaborn and plotnine, and parallel computing with Dask. We will explore artificial intelligence-assisted programming with GitHub Copilot and finish with Docker and containerisation. Throughout the course, you will learn how to use these tools to improve your data science workflow and create more reliable, reproducible, and robust analyses.
- Danilo Freire
- Email:
danilofreire@gmail.com - Office hours: By appointment at any time (online or in person).
- Email:
By the end of this course, students will be able to:
- Use data science tools for project collaboration and version control
- Apply advanced techniques for data storage, manipulation, and querying
- Create clear data visualisations and write well-documented code
- Use AI tools to help with programming tasks
- Understand the basics of containerisation and parallel computing
This repository is organised as follows:
assignments/: Contains all course assignmentslectures/: Includes lecture materials and codetutorials/: Step-by-step guides for the tools used in the courseREADME.md: This file, providing an overview of the course and repositorysyllabus.pdf: Course syllabus in PDF format
The course website is available at https://danilofreire.github.io/qtm350/.
If you encounter any issues with the course materials or have questions about the content, please:
- Check the course syllabus and this README for relevant information
- Review the lecture materials and tutorials in the repository
- Consult with your classmates or post in the course discussion forum
- Attend office hours or schedule an appointment with the instructor
While this repository is primarily maintained by the course instructor, everyone is welcome to contribute. Please feel free to suggest improvements or report issues by opening a GitHub issue, submitting a pull request, creating a discussion post, or contacting the instructor directly.
This repository is licensed under the MIT License. You are free to use, modify, and distribute the materials as needed, with appropriate attribution to the original source.
We look forward to an engaging and productive semester! Good luck, and happy coding! 😃