DATASCI 101: Introduction to AI Applications

Course Overview

This repository contains the source materials for DATASCI 101: Introduction to AI Applications. The course provides a comprehensive introduction to the fundamental concepts, tools, and techniques used in data science.

Course Information

• Course: DATASCI 101 - Introduction to AI Applications
• Semester: Fall 2026
• Format: Undergraduate-level course
• Schedule: Tuesdays and Thursdays, 2:30pm to 3:20pm
• Location: White Hall, Room 206
• Instructor: Danilo Freire
• Email: danilo.freire@emory.edu
• Teaching Assistants:
  • Tom Suo: tom.suo@emory.edu (in class)
  • Sissi Li: sissi.li@emory.edu (in class)
  • Philip Wang: xipu.wang@emory.edu (grading)
  • Anita Osuri: anita.osuri@emory.edu (grading)

Course Content

The course covers six main modules:

1. Orientation - Introduction to AI and course overview
2. How AI systems are designed - Dataset design, learning paradigms
3. Language and perception - Natural language processing and computer vision
4. Retrieval, generation and pipelines - Modern AI systems and workflows
5. Data ethics and bias - Ethical considerations and bias mitigation
6. Policy, governance and social impact - Regulation, privacy, and societal effects

Course Materials

All course materials, including lecture notes, assignments, and supplementary resources, are available in this repository.

Getting Help

If you have questions about the course materials:

1. Check the course website
2. Review the syllabus and lecture materials
3. Contact the TAs or instructor via email
4. Open an issue on GitHub

Repository Structure

This is the main branch which contains the following files and folders:

• lectures/ - Lecture slides and additional files
• assignments/ - Assignment files
• project/ - Files related to the final project
• grades/ - Reusable Python script for computing final grades from a Canvas CSV export, applying the syllabus weights and rubric. Student data is excluded from version control via a folder-level .gitignore.
• syllabus.qmd - Complete course syllabus in Quarto format
• syllabus-template.latex - LaTeX template for syllabus compilation
• syllabus.pdf - Compiled syllabus document
• .gitignore - Git ignore configuration

Downloading the Repository

The quickest way to download the course materials is a shallow clone, which fetches only the current files without the full history of past changes. This keeps the download small and works well on slow connections:

git clone --depth 1 https://github.com/danilofreire/datasci101.git

If you would like the complete history (for example, to see how the materials evolved over the semester), omit the flag:

git clone https://github.com/danilofreire/datasci101.git

The full clone takes longer and uses more disk space, but it is not required for the course.

Updating Your Copy

When new lectures or fixes are added, pull the latest changes from inside the repository folder:

cd datasci101
git pull

This works the same way on shallow and full clones.

Website

The course website is hosted on the gh-pages branch and includes:

• Course information and syllabus
• Lecture schedule
• Assignment details
• Additional resources

To view the website, switch to the gh-pages branch or visit the GitHub Pages site at https://danilofreire.github.io/datasci101.

Contributing

While this repository is primarily maintained by the course instructor, contributions are welcome. Please feel free to:

• Open an issue to report problems
• Create a pull request with improvements
• Start a discussion about course topics

License

This course material is shared under the MIT License. You are free to use, modify, and distribute the materials with appropriate attribution.

Contact

For questions about course content or materials, please refer to the contact information provided above and in the syllabus.