This video covers a final data analysis session focused on data modeling and visualization in Tableau.

Project Overview and Preparation

• Capstone Project: The speaker introduced a second capstone project focused on analyzing HIV infection rates in the United States from 2015 to 2024.
• Tools: Participants are expected to use both MySQL (to process and segment the data) and Tableau (for visualization and analysis).
• Requirements: Using a single CSV dataset, students must use SQL queries to create three distinct records: patient data, medical records, and health outcomes.
• Deliverables: Students need to create four separate dashboards covering KPI, demographic, geographic, and patient outcome analyses, along with a PowerPoint presentation outlining the insights.
• Timeline: While participants can work in their established groups, each person must complete the project individually for their portfolio. The speaker suggested a two-week timeframe to complete the project before the group presentation.

Tableau Data Modeling and Visualization

• Data Connection: The session walked through connecting multiple related datasets (order details, orders, pizza types, and pizzas) in Tableau.
• Primary Keys: The speaker emphasized that identifying the primary key—the linking column common across datasets—is essential for data modeling.
• Data Validation: Participants were advised to validate data and ensure column headers are consistent across files, as Tableau requires specific matching to establish relationships.
• Visualization Practice:
  • Pizza Orders: The class practiced visualizing the pizzas with the highest orders by filtering for “top three” using quantity.
  • Sales Trends: The group discussed visualizing sales by month using either quantity or price, noting that a bar chart can sometimes offer a clearer representation than a line chart depending on the data.
  • Operational Analysis: The class analyzed the “best time” to order pizza by identifying hours with the least order volume to suggest when delivery might be most efficient.

Final Guidance

• Data Cleaning: The speaker demonstrated how to clean datasets in Excel by using the “Find & Select” feature to isolate and remove blank rows.
• Continuous Practice: As this was the final class, the speaker encouraged students to continue practicing by downloading real-world datasets from resources like Kaggle.