Canvas isn’t always great about how it handles multiple file uploads - especially around deadlines. So a few bits of caution for turning in assignments.

You should check that your submissions have been properly uploaded.

This week, we’ll explicitly talk about scale: what do we do when the data gets big. This problem is pervasive: we’ve been dealing with it all along, and will continue to deal with it with everything else we talk about. This week, we’ll try to get some vocabulary to talk about the problems and their solutions.

This is an unusual week in many ways. The reading (and discussion/seek-and-find) topic is implementation - which is a weird topic since there’s a choose-your-own adventure aspect to it. Everyone has different needs and interests on this one.

But to make things even more unusual, I will be out of town for the class periods. On Monday, we’ll have a guest lecture (Prof. Karen Schloss of the Department of Psychology), and we’ll also have an ICE. On Wednesday, we’ll have a full-period ICE (led by Cat).

Here are some selected student responses that earned kudos. We picked one of each type somewhat randomly (these are not the “best” - they are a random example of something that earned a kudo, which is in itself a random sampling of things that are good).

We are providing these to give you a sense of what your classmates are doing that catches our eyes.

Design Exercise 6 asks that you make 4 visualizations, each telling a different story, from the Census Data data set. This assignment extends DE05: Stories from Data (EDA).

In this design exercise, we’ll continue working with the Census Data and make more visualizations. The idea is to try to “tell stories” with the data (illustrate interesting things). The problem is, we also need to identify the stories that are worth telling, which means we need to do some exploring. So part of this assignment is to try to do “Exploratory Data Analysis” where we make pictures that suggest what the interesting stories are.

This week, we’ll learn about encodings the way we map data to things we see. Encodings give the basics building blocks that we build visualizations from. The key idea is that rather than thinking about chart types, we think about them in terms of building blocks. That way we don’t need to learn zillions of chart types… we learn a few basic building blocks that we can assemble into charts as needed. An advantage to this approach: it lets us reason about why we might make certain choices.

Here is some advice on making good “figures.” It is targeted towards design exercises, but really, they are advice on how to make good figures generally.

These ideas apply more to places where we are asking you to “tell stories” from data. This starts with DE04. If you’re putting a figure in a paper or a slide, you are telling a story as well.

In this assignment, you will make some visualizations from the Census Data. Actual visualizations based on data (unlike last time when we asked you to sketch). You will turn your assignment in using the Canvas survey Design Exercise 04: Pictures from Data (due Fri, Sep 27)