📅 2023-03-06 — Session: Developed Data Policies for University Collaboration
🕒 17:25–20:25
🏷️ Labels: Data Policies, University Collaboration, Data Management, Public Sector, Interdisciplinary
📂 Project: Business
⭐ Priority: MEDIUM
Session Goal
The aim of this session was to explore and develop data policies for universities, focusing on collaboration with public offices and enhancing data management strategies.
Key Activities
- Estimating Treatment Effects: Explored methods such as matched pairs and DID estimator using Python.
- [[Data Visualization]]: Enhanced scatter plot visualizations using Matplotlib and Seaborn.
- Data Policies: Developed frameworks for data policies in universities, addressing privacy, security, and ethical use.
- Collaboration Strategies: Planned strategies for academic-public partnerships and fostering interdisciplinary collaboration.
- Understanding Bias: Reflected on attenuation bias in regression analysis and methods to mitigate it.
Achievements
- Clarified the process of estimating treatment effects and visualizing data effectively.
- Developed comprehensive data policy frameworks for universities.
- Established strategies for effective collaboration between academia and public offices.
Pending Tasks
- Further refinement of data policy strategies to ensure comprehensive coverage of privacy and ethical considerations.
- Implementation of collaboration strategies in real-world academic settings.