Objective:
Develop an analytics-driven business case for improving or solving a specific problem within an organization or industry of your choice. This project should simulate how analytics professionals approach real-world challenges, combining data exploration, stakeholder considerations, and actionable recommendations.
Requirements:

Business Problem Identification: Select a specific problem within a business domain (e.g., customer retention, inventory optimization, market segmentation, or workforce productivity). Define the scope of the problem and its business impact in quantitative terms (e.g., cost, revenue, customer satisfaction).
Data Exploration: Use an open data source or collect secondary data relevant to your chosen problem. Ensure the dataset includes enough complexity (e.g., multiple variables, time series, or categorical and numerical data) to allow for meaningful analysis.
Analytical Techniques: Apply foundational analytics techniques to analyze the dataset. This may include descriptive analytics (summarizing trends and patterns), diagnostic analytics (identifying causes of issues), and exploratory data visualization.
Insights and Recommendations: Develop actionable insights based on your findings. Propose realistic solutions or strategies to address the problem, supported by your analysis.
Business Impact: Estimate the potential business impact of implementing your recommendations. Include a cost-benefit analysis, if possible.
Communication: Create compelling visualizations and deliverables to communicate your findings effectively to a non-technical audience.

Deliverables:

Report: Submit a comprehensive report (3,000–4,000 words) including the problem statement, data source description, methods used, findings, recommendations, and potential business impact.
Dashboard or Visualization: Build an interactive dashboard or series of visualizations summarizing key findings and supporting data-driven decision-making.
Presentation: Prepare a 10-minute presentation aimed at stakeholders (e.g., executives, managers) to pitch your business case and highlight the value of your recommendations.

Constraints:

AI tools may assist with technical tasks such as cleaning data or generating visualizations, but the interpretation, business framing, and recommendation development must reflect your own critical thinking.