CORE-04 · Foundations

Data Literacy and Evidence-Based Decision Making

Teaches students to consume, verify, and reason about data — not produce it. Every professional role requires reading dashboards, verifying numbers, making data-backed arguments, and handling sensitive information. This course makes students confident data consumers who know when numbers are right, when they're wrong, and when they're being used to mislead.

25 Hours

9 Learning objectives

Evaluate Bloom's ceiling (?)

5 Competencies

What You'll Be Able To Do

Learning Objectives

Objectives

Depth

Read and interpret common dashboard visualizations: line charts, bar charts, funnels, cohort tables, and KPI scorecards Understand
Verify data accuracy before presenting: check denominators, time periods, source definitions, and known data quality issues Apply
Use spreadsheet formulas (VLOOKUP/INDEX-MATCH, pivot tables, conditional formatting) to organize and analyze tabular data Apply
Construct data-backed recommendations by combining quantitative evidence with qualitative context and explicit confidence levels Apply
Calculate and interpret basic business metrics: conversion rates, retention rates, growth rates, ROI, and unit economics Apply
Identify when data is insufficient, misleading, or misinterpreted — including survivorship bias, base rate neglect, and cherry-picking Evaluate
Distinguish correlation from causation using practical workplace examples and identify potential confounders Analyze
Apply appropriate data handling protocols for sensitive information: PII anonymization, PHI awareness, proprietary data safeguards Apply
Read and interpret basic SQL query results: understand what a query returns, what filtering and grouping is applied, and whether it answers the stated question Understand

Levels: Remember · Understand · Apply · Analyze · Evaluate · Create — highest demands most original thinking.

Core Competencies

What You'll Master

Dashboard Interpretation

Reading, questioning, and contextualizing data visualizations.

Data Verification

Catching errors in numbers before they reach stakeholders or influence decisions.

Spreadsheet Analysis

Practical formula use, pivot tables, data organization for analysis.

Evidence-Based Argumentation

Building and defending data-backed recommendations with appropriate caveats.

Data Responsibility

Handling sensitive, confidential, and personal data according to professional and legal standards.

Portfolio Artifact

What You'll Build

Data-Backed Recommendation Memo — Student takes a real or simulated business question, gathers data from provided dashboards and spreadsheets, verifies the numbers (identifying at least one error or misleading pattern in the provided data), and writes a 2-page recommendation memo with explicit confidence levels, caveats, and an acknowledgment of what the data doesn't tell them.

Tools You'll Use

Industry Tools, Not Toy Projects

Google Sheets / Excel

Spreadsheet tools for organizing, analyzing, and verifying tabular data with formulas and pivot tables.

Looker / Metabase

Business intelligence platforms for read-only dashboard exploration and data visualization interpretation.

Claude

AI assistant for analysis assistance, verification of calculations, and exploring data questions.

SQL Playground

Read-only query practice environment for interpreting SQL results and understanding data retrieval.