Data Journalism Course

Lesson 1 • What Data Journalism Is

Defines data journalism and distinguishes it from traditional reporting. Establishes the chapter's core vocabulary and sets expectations for the course.

Lesson 2 • Finding and Evaluating Data Sources

Teaches source assessment criteria: provenance, methodology, and bias. Students apply a source-quality checklist to real datasets.

Lesson 3 • The Data Journalism Workflow

Maps the end-to-end process from story idea to publication. Students use this workflow as a repeatable framework throughout the course.

Lesson 1 • Crowdsourcing and Survey Data

Explores reader-sourced data collection as a reporting tool. Students design a short survey and evaluate its methodological strengths and weaknesses.

Lesson 2 • Working with APIs

Teaches API concepts, authentication, and query construction for journalistic data retrieval. Students pull live data from a public API endpoint.

Lesson 3 • Downloading and Importing Datasets

Covers common file formats and import procedures into spreadsheet and database tools. Prepares students for the cleaning chapter that follows.

Lesson 4 • Public Records and Freedom of Information

Explains how to request government records using freedom-of-information principles. Connects legal access rights to practical newsgathering strategy.

Lesson 1 • Spreadsheet Fundamentals

Reviews cell references, formulas, and data types as a foundation for analysis. Ensures all students share a common technical baseline before advanced functions.

Lesson 2 • Sorting, Filtering, and Grouping

Teaches multi-level sorting, conditional filters, and grouping to isolate newsworthy patterns. Students apply these skills to a real public dataset.

Lesson 3 • Cleaning Dirty Data

Addresses inconsistent formatting, duplicates, and missing values using spreadsheet functions. Clean data is a prerequisite for reliable analysis in later chapters.

Lesson 4 • Pivot Tables and Summary Statistics

Uses pivot tables to aggregate and cross-tabulate data quickly. Students generate summary statistics that form the basis of a short data memo.

Lesson 1 • Standardizing and Normalizing Data

Covers name standardization, date formatting, and unit normalization across records. Consistent formats are required for accurate aggregation and comparison.

Lesson 2 • Verifying Data Against Source Documents

Teaches cross-referencing datasets with original documents and expert sources. Verification prevents publishing errors that damage credibility.

Lesson 3 • Deduplication Strategies

Addresses exact and fuzzy duplicate detection to prevent double-counting in analysis. Students apply deduplication to a messy real-world dataset.

Lesson 4 • Maintaining a Cleaning Audit Trail

Establishes documentation practices for every transformation applied to a dataset. An audit trail enables peer review and post-publication corrections.

Lesson 1 • Descriptive Statistics Essentials

Covers mean, median, mode, range, and standard deviation in journalistic context. Students choose the right measure for different data distributions.

Lesson 2 • Common Statistical Pitfalls

Catalogs misleading chart scales, cherry-picked time frames, and base-rate neglect. Students audit a published story for statistical errors.

Lesson 3 • Rates, Ratios, and Percentages

Teaches per-capita rates, percentage change, and ratio construction for fair comparison. These calculations appear in nearly every data story.

Lesson 4 • Probability and Sampling Basics

Introduces probability concepts, sampling methods, and margin of error for survey data. Students assess whether a sample supports the story's claims.

Lesson 1 • Interactive Visualization Tools

Introduces browser-based tools for creating interactive charts and embeds. Students publish an interactive graphic to a test webpage.

Lesson 2 • Visualization Theory and Chart Selection

Covers perceptual principles and a decision framework for choosing chart types. Correct chart selection prevents misrepresentation of data.

Lesson 3 • Designing for Clarity and Honesty

Addresses color theory, data-ink ratio, and avoiding deceptive design choices. Honest design is a core journalistic obligation.

Lesson 4 • Building Charts with Spreadsheet Tools

Teaches bar, line, scatter, and pie chart construction in standard spreadsheet software. Students format charts to meet newsroom style guidelines.

Lesson 1 • Relational Database Concepts

Explains tables, keys, and relationships as the structural foundation of SQL databases. Understanding schema design is required before writing queries.

Lesson 2 • Basic SQL Queries

Teaches SELECT, WHERE, ORDER BY, and LIMIT clauses for targeted data retrieval. Students query a public dataset and export results for analysis.

Lesson 3 • Joining Multiple Tables

Teaches INNER, LEFT, and anti-joins to combine related tables for richer analysis. Students join two public datasets and identify records unique to each.

Lesson 4 • Aggregation and Grouping in SQL

Covers GROUP BY, COUNT, SUM, AVG, and HAVING for summary analysis. Aggregation in SQL mirrors pivot-table logic from the spreadsheet chapter.

Lesson 1 • Developing a Data-Driven Story Pitch

Teaches hypothesis formation, preliminary data checks, and pitch structure for editors. A strong pitch aligns data findings with audience relevance.

Lesson 2 • Narrative Structure for Data Stories

Applies classic story structures—inverted pyramid, hourglass, and Wall Street Journal formula—to data-driven pieces. Structure guides readers through complex findings.

Lesson 3 • Combining Data with Human Sources

Covers interview strategies that contextualize quantitative findings with expert and affected voices. Data alone rarely tells a complete story.

Lesson 4 • Integrating Visuals into the Story

Teaches placement, captioning, and editorial coordination of charts and maps within articles. Visuals must reinforce, not duplicate, the text.

Lesson 5 • Publishing and Measuring Impact

Covers distribution strategy, reader engagement metrics, and post-publication follow-up. Measuring impact informs future story selection and resource allocation.