Data Journalism
An undergraduate course on data-driven storytelling, covering data collection, analysis, and visualization in the context of journalism and media.
Course Overview
This undergraduate course teaches students to use data as a journalistic tool. Students learn to find, clean, analyse, and visualise data, and to weave quantitative findings into compelling news narratives for digital media.
Learning Objectives
By the end of this course, students will be able to:
- Locate and evaluate public datasets from government portals and open-data sources
- Clean and reshape messy data using OpenRefine and Python/pandas
- Apply basic statistical reasoning to avoid common journalistic errors
- Produce publication-ready charts and maps with Datawrapper and Flourish
- Write data-driven stories that integrate quantitative evidence with narrative
Prerequisites
No programming background required. Basic familiarity with spreadsheets recommended.
Level
Undergraduate
Institution
School of Journalism and Communication, Nanjing University
Offered
Fall 2023, Fall 2024
Required Reading
- Bounegru, L. & Gray, J. (Eds.) (2021). The Data Journalism Handbook 2 — free online.
- Knaflic, C. N. (2015). Storytelling with Data. Wiley.
Recommended Tools
| Tool | Use | Link |
|---|---|---|
| Google Sheets | Spreadsheet analysis | sheets.google.com |
| OpenRefine | Data cleaning | openrefine.org |
| Datawrapper | Charts & maps | datawrapper.de |
| Flourish | Interactive visuals | flourish.studio |
| RAWGraphs | Exploratory charts | rawgraphs.io |
| Google Colab | Python in the browser | colab.research.google.com |
Notable Data Journalism Outlets
- The New York Times — The Upshot
- FiveThirtyEight
- Reuters Graphics
- The Guardian Datablog
- Caixin Data (Chinese context)
Assessment
| Component | Weight |
|---|---|
| Weekly data exercises | 30% |
| Mid-term data analysis | 20% |
| Final data journalism story | 40% |
| Participation & peer review | 10% |
Schedule
| Week | Date | Topic | Materials |
|---|---|---|---|
| 1 | Week 1 | What is Data Journalism? Overview of the field, landmark examples, the data journalism workflow, and ethical responsibilities. | |
| 2 | Week 2 | Finding and Evaluating Data Government open data portals, freedom of information, evaluating source credibility, data provenance. | |
| 3 | Week 3 | Spreadsheet Fundamentals Excel/Google Sheets for journalists: sorting, filtering, pivot tables, basic formulas. | |
| 4 | Week 4 | Data Cleaning with OpenRefine Importing messy data, clustering and merging inconsistent values, transforming columns, exporting. | |
| 5 | Week 5 | Statistical Thinking for Journalists Rates vs. raw counts, percentages, averages vs. medians, correlation vs. causation, misleading statistics. | |
| 6 | Week 6 | Introduction to Python for Journalists Python basics via Jupyter, loading data with pandas, filtering and aggregating. | |
| 7 | Week 7 | Data Visualization Fundamentals Choosing the right chart, visual encodings, accessibility, colour palettes, and design principles. | |
| 8 | Week 8 | Interactive Charts and Maps Building embeddable charts with Datawrapper; choropleth maps and point maps. | |
| 9 | Week 9 | Telling Stories with Data Narrative structures for data stories, anecdote + data, writing headlines and annotations. | |
| 10 | Week 10 | Social Media and Web Data Scraping public web data, working with Twitter/Weibo data, legal and ethical considerations. | |
| 11 | Week 11 | Investigative Data Journalism ICIJ Panama Papers workflow, working with leaked data, verifying large datasets. | |
| 12 | Week 12 | Publishing and Pitching Data Stories Writing pitch letters, preparing interactive pieces for web publication, CMS workflows. | |
| 13 | Week 13 | Final Project Workshop Peer critique of draft data stories; instructor feedback on visualisations and narrative. | |
| 14 | Week 14 | Final Project Presentations Students publish and present their original data-driven news stories. |