Community Impact Analytics: Bridging Data and Real-World Outcomes Objective: Design and execute a small-scale analytics project that evaluates

Community Impact Analytics: Bridging Data and Real-World Outcomes

1. Objective:
   Design and execute a small-scale analytics project that evaluates and proposes improvements to a real-world process or issue within your community, such as public transportation, local business operations, or community health initiatives.
2. Requirements:
   
   • Problem Definition:Identify a specific, measurable problem or opportunity for improvement in your community. Provide a clear rationale for why this issue is important.
   • Data Collection: Collect primary data by designing surveys, conducting interviews, or recording observations. You may supplement this with publicly available secondary data, but the primary data collection is mandatory.
   • Analytical Methods: Apply foundational analytics techniques (descriptive statistics, data visualization, basic predictive modeling, or clustering) to analyze the data you collected.
   • Interpretation and Insights: Present actionable insights based on your analysis. Discuss the implications of your findings for stakeholders and propose data-driven recommendations.
   • Ethical Considerations:Discuss any ethical concerns associated with your data collection and analysis, including privacy and bias considerations.
3. Deliverables:
   
   • Report: Submit a written report (3,000–4,000 words) detailing your problem definition, data collection process, analysis, findings, and recommendations.
   • Presentation: Prepare a 10-minute presentation summarizing your project for your peers, including visualizations of your findings.
   • Reflection: Include a brief reflection on what you learned through the process, challenges you faced, and how analytics can influence real-world decision-making.
4. Constraints:
   
   • AI tools may be used for specific tasks like data visualization or assisting in analysis but must be disclosed. The project requires human involvement in data collection, problem contextualization, stakeholder interaction, and interpretation of results