Week-by-Week Course Outline
All classes will be taught in a computer lab, and will weave discussion with hands-on practice.
Week 1, September 25: Data — Introduction
- How to think about data as a journalist, and as a programmer.
- Getting comfortable with the most common “structured” data formats: CSV, XML, and JSON.
- Sourcing data.
Week 2, October 2: Analysis – Introduction
- Overview of the types of basic analysis, from counting to correlations, you can do on almost any dataset.
- Analysis using spreadsheet software.
- Analysis using database software.
Week 3, October 9: Visualization — Introduction
- Static vs. interactive visualizations.
- The elemental forms of data visualization.
- Charting tools.
Week 4, October 16: Data, part II – Working with Messy Data
- “Regular expressions”: find-and-replace on steroids.
- Using OpenRefine to clean and transform data.
- Fact-checking your data.
Week 5, October 23: Analysis, part II – Grouped Analysis
- Grouping data using pivot tables in spreadsheet software.
- Grouping data using SQL in database software.
- Outliers, sample size, and the law of large numbers.
No class on October 30
Week 6, November 6: Data, part III – Getting/Making Your Own Datasets
- Joining datasets with spreadsheets and database software.
- Introduction to web scraping.
- Freedom of Information requests, FOIA Machine, and MuckRock.
Week 7, November 13: Visualization, part II – HTML, CSS, and JavaScript
- Building basic charts with HTML and CSS.
- Throwing in some JavaScript magic.
- Using basic JavaScript charting libraries.
Week 8, November 20: Analysis, part III – Complex Analysis
- Distributions.
- Correlations.
- Significance testing.
No class on November 27
Week 9, December 4: Visualization, part III – Mapping
- Common geodata formats: Shapefiles, KML, and GeoJSON.
- Mapping with Fusion Tables.
- Mapping with JavaScript.
Week 10, December 11: Next Steps
- GitHub and the open source community.
- News apps and web apps.
- Class presentations.