Business Intelligence Analyst Course

Based on the provided structure, it seems like you are organizing a series of lectures for a course, possibly in data visualization or a related field, using Tableau as the tool. The structure outlines a curriculum with specific topics covered in each lecture, categorized into sections and parts, with a mix of theoretical content and practical activities.

Here's a more detailed breakdown of the lectures based on the provided structure:

1. Introduction to Data Visualization (Lecture 1-15)

   • Overview of data visualization principles.
   • Types of visualizations, best practices, and design considerations.
   • Introduction to Tableau and its interface.
   • Basic concepts in Tableau: worksheets, worksheets, dashboards, data sources.

2. Tableau Basic Functionality (Lecture 16-30)

   • Data import, preparation, and connection.
   • Drag-and-drop mechanics.
   • Basic chart types: bar charts, line charts, scatter plots, etc.
   • Understanding fields and measures.

3. Advanced Chart Types (Lecture 31-45)

   • Advanced visualizations: geospatial analysis, histograms, tree maps.
   • Calculated fields and expressions in Tableau.
   • Data blending and level of detail calculations.
   • Parameter controls for interactivity.

4. Tableau Best Practices (Lecture 46-60)

   • Effective storytelling with data.
   • Design considerations for clarity and effectiveness.
   • Performance optimization in Tableau.
   • Sharing and publishing dashboards.

5. Data Storytelling with Tableau (Lecture 61-75)

   • Narrative techniques to present data insights.
   • Visual storytelling principles.
   • Interactive elements and design improvements for storytelling.

6. Tableau PART (A) (Lecture 76-80)

   • Custom calculations in Tableau.
   • Shared services, extracts, and data management.
   • Introduction to advanced analytics features.

7. Tableau PART (B) (Lecture 81-95)

   • Advanced data management techniques.
   • Troubleshooting common issues in Tableau.
   • LOD Expressions (Level of Detail).
   • Aggregating data with table calculations.

8. Tableau PART (C) (Lecture 96-103)

   • Advanced visualizations and custom visualizations.
   • Parameter actions and set actions for interactivity.
   • Advanced mapping and geographic analytics.
   • Customizing Tableau dashboards with HTML, CSS, and JavaScript.

9. Tableau PART (D) and (E) (Lecture 104-127)

   • More advanced topics such as hierarchical data analysis.
   • Machine learning and predictive analytics in Tableau.
   • Integration with R, Python, or other programming languages for custom analytics.
   • Advanced Tableau capabilities like Tableau Prep Builder (formerly known as Project Maestro).

10. Tableau Activities (Lecture 128-139)

    • Practical exercises where students will work on real datasets to apply the concepts learned.

11. Capstone Project (Lecture 140)

    • A capstone project or case study where students can apply all the skills learned throughout the course to create a comprehensive data visualization and storytelling project that showcases their learning journey from the basics to advanced Tableau skills. Please note that the numbering in your structure seems to skip numbers (e.g., Lecture 50). You might want to adjust the numbering for clarity. For example, Lecture 5 could be renamed to Lecture 46, and so on. Additionally, the Tableau Activities seem to start at Lecture 128, which might be a typographical error, as it follows a consistent pattern up to Lecture 139. This structure provides a comprehensive curriculum for learning and using Tableau effectively.