Welcome to Lede 2016’s Fall data visualization course: Storytelling with Data.

By the end of this course you should feel comfortable designing complex data visualizations in a number of technologies, but specifically D3. We’ll be focusing on more artistic expressions instead of the general-purpose bar/line/etc plots.

Details

  • Instructor: Jonathan Soma, js4571@columbia.edu, @jsoma on Slack
  • Dates: Wednesdays, 9/09-12/07
  • Class: 1:30pm-4:30pm, room 607B
  • Lab: Wednesdays, 10am-12pm, 107A (until we find a nicer room)
  • Teaching Assistant: Nicky Forster, @nicky on Slack
  • Slack channel: #storytelling

Course Overview

This is a Fall semester Lede 24 course that adds a visual aspect to the data analysis we worked on all summer. Storytelling changes every year based on what we’re able to cover during Lede 12 - this year we actually spend a good amount of time building traditional D3 graphics (bars, scatters, etc), so I thought it would be interesting to go a little deeper into how exactly D3 and other visual systems work and use it to create some more interesting visuals.

We will also be covering graphic design and data visualization concepts. There will be a little repetition from over the summer, but who really remembers all of that anyway?

Things to bring to class

  • Paper and pen or pencil for sketching
  • Markers or colored pencils to color
  • Your computer for programming
  • Yourself because otherwise it’s going to be difficult to accomplish anything

Software to download/install

You’ll need the following software:

  1. Slack for communication.
  2. A text editor such as Atom, Sublime Text (OS X) or Notepad++ (Windows)

You’ll also probably want to use Chrome, that way you can follow along a little more easily.

Communication

Slack: Our primary means of communication is going to be the Lede Program Slack server in the #storytelling channel.

Email: I’ll also be emailing you, but via email and not the CourseWorks tool. Make sure I have the best email address for you, since I know not everyone loves the @columbia.edu one.

Prerequisites

This class is part of the Lede Program curriculum, so while there aren’t prerequisites we do assume a few things. The ones I can think of are:

  • You’re reasonably adept at programming (e.g. you know what a functions is and what a for loop is)
  • You’re able to come up with project/story ideas on your own
  • You know how to find/clean/work with data, whether programmatically (R, Python) or even through Excel
  • You know that Google is your best friend when it comes to not knowing the answer
  • You have at least one creative bone in your body

You’ll be required to turn in assignments using git and GitHub, but you don’t need to know how to use it yet, you’ll figure it out!

We’ve spent a little time on D3 already, so you might want to check out these tutorials to get up to speed (or a little beyond).

Textbook

You will have two textbooks for the class. One is for simple chart/graphic design and the other is a bit more about being visual.

Book One: How to Present Data

The first is The Wall Street Journal Guide to Information Graphics: The Dos and Don’ts of Presenting Data, Facts, and Figures ($17).

We aren’t going to be assigned readings out of it, you just have to read it twice over the course of the semester. It’s only about 150 pages long, and is a bit more of a reference book, so feel free to get a copy from a library, find an ebook, or share one among your friends.

Book Two: How to Design Visualizations

The second book I’m not assigning: you get to pick it.

This is something you’ll read as we go along, outside of the readings I give you. Part of your homework will be to post summaries of your readings.

As this course really requires you to be creative, instead of a data viz book you might enjoy buying a graphic design book instead. I’ve outlined some recommendations below, but by no means is it an exhaustive list.

Data Viz Books

I highly highly recommend Alberto Cairo’s The Functional Art, and his recent follow-up, The Truthful Art. While no one agrees with everything in a viz book, they’re dense, easy to read and understand, and full of examples. The Functional Art is more useful than The Truthful Art.

If you’d like something a little more classic, Edward Tufte’s The Visual Display of Quantitative Information is good, but don’t buy his other books, it’s just more of the same. There’s also a new data viz book going around that I really do not recommend (I’ll talk about it in class), so if you’re looking at anything else ask me and I’ll give you my Thoughts & Feelings.

Graphic Design Books

On the graphic design side of things, I’d browse Barnes and Noble and pick something you think looks good. You can be hunting for technical skills or inspiration, either one is fine by me. Sit down in the store and read the first chapter or two to see how it feels, or take it home and remember that B&N has a 14-day return policy.

You might pick up The Design of Everyday Things, The Elements of Typograph Style, The Elements of Graphic Design, Making and Breaking the Grid, or whatever else. I really really recommend walking into the bookstore and browsing, though.

Class organization

Each class will be divided into several sections:

  • A design/creative exercise
  • Analyzing/critiquing a mix of professional and personal projects
  • Theory
  • Programming concepts

Sometimes I’ll be lecturing, sometimes you’ll be talking, sometimes you’ll be working in small groups, etc etc etc.

Grading

Your final grade will be Pass/Fail if you’re taking this in the Journalism School. Others will receive a numeric grade.

Classwork: 30%

We’ll be doing work in class, for which you will receive a Pass/Fail participation grade for each separate class.

Homework: 40%

Homework will be a mixture of programming exercises to practice your skills and readings to see why you’d ever want to program anything visual.

Coding exercises will generally be due on Wednesday at midnight [note: that’s after the next class, since lab is Wednesday morning]. If you would like to work on a personal or work-related dataset for your homework that should be fine (and is encouraged).

Readings will be due when they’re due.

Homework assignments will be turned in via CourseWorks or GitHub.

Projects: 30%

Projects are an attempt to show off the skills you’ve learned on a dataset of your own. They are due Friday at midnight, starting on the third week of class.

You can find due dates and more details on the projects page

Weekly Schedule

You can find the weekly schedule on the schedule page