Lede Program Coursework

The Lede Program offers an intensive 10-week program in data and computation. During the program, students will complete coursework from several different modules, which have previously included:

Foundations of Computing

During this introduction to the ins and outs of the Python programming language, students build a foundation upon which their later, more coding-intensive sections will depend. Dirty, real-world data sets will be cleaned, parsed and processed while recreating modern journalistic projects. The course will focus heavily on how to use public resources such as Google and StackOverflow to build self-reliance.

  • Focus: Familiarize yourself with the data-driven programmatic landscape
  • Topics & tools include: Python, basic statistical analysis, pandas, CSVs, APIs, git/GitHub, cron, web scraping, StackOverflow, data cleaning, command line tools, and more


In this project-driven course, students refine their creative workflow on personal work, from obtaining and cleaning data to final presentation. Data is explored not only as the basis for visualization, but also as a lead-generating foundation, requiring further investigative or research-oriented work.

  • Focus: Applying your skillset
  • Topics & tools include: visualization, pandas, matplotlib, DataWrapper, Adobe Illustrator, mapping, d3.js, and more


Students learn to leverage map-making through tools such as QGIS, focusing not only on visualization but also geographic analysis.

  • Focus: Mapping and geographic analysis
  • Topics & tools include: QGIS, mapshaper, GeoJSON, topojson, satellite imagery, DataWrapper


Machine learning and data science are integral to processing and understanding large data sets. Whether you’re clustering schools or crime data, analyzing relationships between people or businesses, or searching for a needle in a haystack of documents, algorithms can help. Through supervised and unsupervised learning, students will generate leads, create insights, and figure out how to best focus their efforts with large data sets. A critical eye toward applications of algorithms will also be developed, uncovering the pitfalls and biases to look for in your own and others’ work.

  • Focus: Analyzing your data
  • Topics & tools include: linear regression, clustering, text mining, natural language processing, neural nets, machine learning, scikit-learn