QTM 151 - Introduction to Statistical Computing II

Course Overview

Welcome to QTM 151! This course introduces students to data analysis and statistical computing using Python and SQL. Please have a look at the course syllabus and the lecture schedule for more information. A brief overview of the course content is provided below.

Contact Information

  • Name: Danilo Freire
  • Contact:
  • Office Hours: By appointment at your convenience, please email me to schedule a meeting

Teaching Assistants

Repository Structure

While this website provides an overview of the course materials, the primary source of content is the course’s GitHub repository at https://github.com/danilofreire/qtm151. The repository is organised as follows:

Please also refer to the Discussion tab on the repository for any questions or comments: https://github.com/danilofreire/qtm151/discussions.

Note: The links will take you to the respective folders or files in the repository, not on this website. You can find links for the rendered content in the navigation bar above.

Course Content

Lectures

We will meet every Monday and Wednesday from 16:00 to 16:50 in the Anthropology Building, 303. Please check the course schedule for the full list of topics and dates. Lecture materials are available in the lectures folder in the repository.

Each folder contains an HTML presentation or a Jupyter notebook (.ipynb) with code examples and explanations, along with any additional resources or datasets used in the lecture.

Assignments and Quizzes

Throughout the course, students will complete various assignments and quizzes to reinforce their learning. These will be posted in the assignments/ folder as the course progresses. We will also announce these in class and on Canvas. Please refer to the syllabus or the lecture schedule for due dates and submission guidelines.

Tutorials

The tutorials/ tab contains step-by-step guides for various tools and techniques used in the course. Please have a look at these resources to help you get started.

PDF versions of the tutorials are also available in the repository.

Course Requirements

Grading

  • Assignments: 50%
  • Class Quizzes: 30%
  • Final Project: 20%

Course Policies and Expectations

For detailed information on course policies, grading criteria, attendance requirements, and academic integrity guidelines, please refer to the syllabus in the repository or on the course website.

Getting Help

If you encounter any issues with the course materials or have questions about the content, please:

  1. Check the course syllabus for relevant information
  2. Review the lecture materials and tutorials either in the repository or on the course website
  3. Consult with your classmates or post in the course discussion forum
  4. Attend office hours or schedule an appointment with the instructor

Contributing to the Repository

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.

Acknowledgements

This course and its materials have been developed with inspiration from previous version of this course, as well as various open-source communities and educational resources. I am particularly grateful to Alejandro Sánchez Becerra for his teaching materials and guidance. I am also thankful for the contributions of the Python, SQL, and data science communities that make courses like this possible.

License

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! :smiley:

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