# COURSE SUMMARY & ENROLLMENT PARAMETERS

**Course:** Data Visualization & AI Analytics
**Duration:** 1 Semester
**Target Level:** Grades 10–11
**Credit Allocation:** 0.5 (semester credit)
**Prerequisite:** None

A business/tech hybrid that turns large datasets into clean charts, graphs, and infographics using spreadsheets and visual dashboards — without writing a single line of database code. Students use AI analytics engines to find patterns and verify insights, applying the **Code Auditor** mindset to data: every chart is audited for accuracy, and every AI-surfaced pattern is validated against the source.

# INSTITUTIONAL RUNWAY & PACING STRATEGY

## The Four Thematic Modules (25% each)

| Module | Thematic Focus | Runtime Share |
|---|---|---|
| Module 1 | Data Storytelling & Chart Design | 25% |
| Module 2 | Spreadsheets & Dashboards | 25% |
| Module 3 | AI Analytics & Pattern Discovery | 25% |
| Module 4 | The Data Storytelling Capstone | 25% |

Each module consumes one quarter of the semester runway, independent of the calendar, ensuring full coverage across 15 or 18 weeks.

## Systemic Buffer Block (The Flex Zone)

A **Systemic Buffer Block** closes the semester. On a 15–18 week schedule, each module runs ~3.5–4.5 weeks; the Flex Zone absorbs the residual 1–3 weeks for the capstone presentation and portfolio polish.

# SYSTEMIC MODULE DECONSTRUCTIONS

## Module 1: Data Storytelling & Chart Design

- **Core Objectives:** Turn raw data into clear, honest charts and infographics.
- **Architectural Concepts:** Chart types, visual encodings, and the integrity of a data story.
- **The AI-Assisted Classroom Workflow Strategy:** Students build charts from a real dataset and audit each for accuracy and clarity.

## Module 2: Spreadsheets & Dashboards

- **Core Objectives:** Use spreadsheets and dashboards to explore and summarize data.
- **Architectural Concepts:** Aggregation, filtering, and interactive dashboard design.
- **The AI-Assisted Classroom Workflow Strategy:** Students build an interactive dashboard and verify every aggregate against the raw data.

## Module 3: AI Analytics & Pattern Discovery

- **Core Objectives:** Use AI analytics engines to surface patterns without database code.
- **Architectural Concepts:** Correlation vs. causation, pattern detection, and insight validation.
- **The AI-Assisted Classroom Workflow Strategy:** Students run AI analytics, then audit and verify each surfaced insight before reporting it.

## Module 4: The Data Storytelling Capstone

- **Core Objectives:** Turn a large dataset into a persuasive, accurate data story.
- **Architectural Concepts:** Narrative structure, evidence synthesis, and presentation.
- **The AI-Assisted Classroom Workflow Strategy:** Students build and present a complete data story, defending every chart and claim.

# PERFORMANCE EVALUATION & GRADING SCHEME

- **System Integration (30%):** Coherence between raw data, dashboards, and the final narrative.
- **Code Auditing Sheets / Data Audits (35%):** Structured logs documenting how AI-surfaced insights were verified against source data.
- **Capstone Portfolio (35%):** A dashboard and data-story presentation assessed on accuracy and clarity.

# PROGRESSIVE ACADEMIC INTEGRITY & AI POLICY

AI is an **analytics engine whose output must be audited**. Students log every AI-assisted analysis and verify patterns against source data. Un-audited, copy-pasted insights a student cannot defend are treated as plagiarism. *You may let the AI find the pattern, but you must own the chart and the conclusion.*