Editing Data Into Insights (Spring 2021) (section)

= Assignments =

There will be multiple types of assignments, designed to encourage learning in different ways.

== Participation ==

This will be a very participatory class, and I expect you to be an active member of our class, engaged in helping us all to gain insight and inspritation. This includes paying attention in class, participating in activities, and being actively engaged in learning, thinking about, and trying to understand the material. 

This also includes doing the readings and watching the videos. To make sure that everyone has an opportunity to participate and to encourage you to do the assignments, I will randomly select students to answer discussion questions or to explain portions of homework assignments and labs. I will keep track of the quantity and quality of your responses and I will make that data available to you to help guide our discussion around grades.

== Discussion Questions ==

This course will have two "modes". For much of the class, we will be reading about theories of communication and rhetoric, about principles of data visualization, etc. For these sessions, you will be required to submit 1-2 discussion questions on Discord on Monday by noon. I will then curate some of these questions (and add some of my own) to use to guide our discussion on Tuesday. I will post the questions on the Etherpad at https://etherpad.wikimedia.org/p/com-495-data-insight

Questions should engage with the readings and either connect to other concepts or to the "real world". Here are some good example questions:

* The readings this week talked a lot about how data visualizations can be misleading. How can we tell when visualizations are intentionally trying to mislead versus when they are just poorly designed?
* I was confused by the reading on counterfactuals. We obviously can't really know what would have happened in different conditions, so why even try?
* Imagine you were asked to create an ad campaign to recruit students to Purdue. What types of appeals would you use and why?

During other weeks, we will be more focused on learning practical skills (mostly data manipulation and visualization in R). On those weeks, discussions will center around identifying places where folks are still confused and students will be randomly selected to share their responses to homework questions.

== Homework/Labs ==

There will be a number of homework assignments. At the beginning of the class, these will be designed to help you to grasp foundational concepts about storytelling, visualization, and data. As the class progresses, more and more of them will be based on learning and developing proficiency in visualizing data in R.

== Exams ==

There will be one in-class exam. It will assess your understanding of core concepts around storytelling and visualization.


== Final Project ==

The main outcome of this course will be your final project, which will be a data presentation, either as a website or a slide deck + presentation. A detailed description of the project is [[{{PAGENAME}}/Final project|at this link]].

There will be a number of intermediate assignments through the semester to help you to identify a dataset, explore the data for insights, and get and give feedback on visualizations and story elements.