Welcome to Lede 2016: Data Studio


  • Instructor: Jonathan Soma, js4571@columbia.edu
  • Dates: Mondays and Wednesdays, 7/18-8/31 (plus additional presentation days, 8/30 and 9/1)
  • Class: 10am-1pm, Brown Institute
  • Lab: 2pm-5pm, 511B
  • Slack channel: #data-studio

Course Overview

By the end of this course you’ll have the ability to execute data projects from start to finish, and successfully communicate your data findings to a general audience.

Concentration will be on visual presentation, with our major workflows being matplotlib to Adobe Illustrator (static) and Python/pandas to d3 (interactives). We will also be covering several segments of server operation and automated processes that were introduced in the first half of the summer.


Homework will be rolling projects, with one project starting before the previous project is finished.

Course Structure

This course will be projects-based, and will serve to provide solutions to common issues of data projects, along with constant critiques so we know when we’re producing trash versus treasure.

Each session will generally have an information design theme for the first half of the class, then technical support in the second half of the class.


Before class on Monday, each student must pitch a story on GitHub using the Issues section. You will describe your projects and link to or attach your data set. You must also post a progress report on any current open projects you are working on.

We will start with a pitch session in small groups, where you explain your data set and your idea to the other members of your group. You’ll take their feedback and make a comment on GitHub (although you don’t have to act on any of their recommendations!). We might then talk through a few ideas as a class.

Then we’ll have a lecture on the day’s topic with a few examples of data visualization for examples.

After the break, we’ll have progress reports in small groups, where you present problems and solutions you’ve encountered on your project from the previous week (not the one you pitched earlier in the day). You will enter any feedback into the appropriate issue on GitHub.

Your groups will then move to critiques, where you examine a selection of projects - both by students and professionals - and make notes on design decisions.

We’ll open things up to the entire class for highlights, where I select several visuals/projects/issues to be presented and discussed. This could be anything from analysis of visual style to how to get around a common data issue. Several groups will share the thoughts they came up with during the small group session.

Then we’ll have more lecture.


Before class on Wednesday, each student must post a progress report on GitHub on any project they are currently working on.

We’ll start with critiques in small groups, both of your projects as well as professional projects. We’ll then come together as a class to discuss a selection of the projects.

Then there will be lecture, and more lecture.


Work in progress.

Week 1: Introduction to Data Visualization

Design theory: Types of charts, types of data

Technical skills: Exporting matplotlib/pandas to Illustrator