Our company is interested in building upon our synthetic data application. Impact of this work spans the variety of services we provide in data creation, management, and analysis to potential clients in the social impact sector.

We would like to collaborate with students to simulate different tabular data scenarios by needs specific to each sub-sector we serve, i.e. philanthropy, impact investing, think tank, advocacy, higher education. Students will write code and documentations to explore 2-3 common, tabular dataset scenarios per sub-sector that our company can use as reference points for our application's back-end processing tasks.

This will involve several different steps for the students, including:

• Developing familiarity with Python synthetic data libraries
• Building intuition on dataset structures typical in social impact sub-sectors
• Evaluating then prioritizing options for building the app back-end with a zero-trust architecture model
• Writing clean code and requisite technical documentations

By the end of the project, students should demonstrate:

• Understanding of key Python synthetic data libraries alongside related AI / ML techniques
• Development of data architecture and engineering skills, specifically for building secure and modular back-ends for core data applications

Final deliverables should include:

• Source materials such as the application's back-end code and accompanying documentation guiding our exploration of next steps for direction of the synthetic data app