# Syllabus

## Course Meeting Times

Lecture / Activity: 2 sessions / week, 1.5 hours / session

## Prerequisites

None.

The course is open to all technical levels and backgrounds. We will prioritize students with a strong background in one or more of the following areas: journalism, software development, data analysis, documentary, visual and performing arts.

## Course Description

We are swimming in data — "Big" and small, global and personal. And we are also facing complicated problems like climate change and inequality whose stories can only be told with data. The need for public understanding of data-driven issues is higher than ever before. But raw data doesn't make a good story… and that's where you come in.

This class is focused on how to tell stories with data to create social change. We will learn through case studies, examples and hands-on work with tools and technologies. We will introduce basic methods for research, cleaning and analyzing datasets, but the focus is on creative methods and media for data presentation and storytelling. We will consider the emotional, aesthetic and practical effects of different presentation methods as well as how to develop metrics for assessing impact.

Over the course of the semester, students will work in small groups to create 5 sketches, each using a different technique for telling a data-driven story. Think about a “sketch” as a half-realized project; where you have implemented just enough of the most important details of the idea in order for us to understand your vision. A sketch is NOT a fully realized presentation of a data story. For the final project, students will have the chance to expand upon one of these sketches to create a fully realized presentation of a data-driven story.

This semester, Spring 2017, will have a special focus on climate change data. Most examples will use data related to this topic, homeworks will be related to it, and sketches and final projects must be connected to it as well. We will take a broad view of that topic, including everything from traditional datasets about the warming oceans, to data on migration caused by the effects of climate change.

## Learning Objectives

- Students will learn techniques for finding a story in data, building a basic set of tool-assisted data analysis skills.
- Students will build things that tell data-driven stories with a rich set of digital and non-digital tools, online and offline.
- Students will practice arts- and rhetoric-based approaches to telling data-driven stories.
- Students will learn to connect data stories to meaningful, situated social action.
- Students will learn basic techniques for measuring the impact of data-driven storytelling.
- Students will learn basic ethnographic and anthropological approaches to identifying and researching audiences.

## Course Requirements

This is a hands-on studio course and we will do a lot of peer production of knowledge, so your participation and presence is essential to making it a success. Each of the main technique-focused sketches will include an introduction to approaches and tools, but then focus on a small group project that you will present for discussion and criticism to the class. The course culminates in a final project that will be a presentation of a data-driven story in some media, for a particular audience with a set of goals. Each project must be done in a group of two or more. I encourage you to work with different people for each project.

Here's a description of these modules and the associated required coursework.

MODULES | REQUIRED COURSEWORK AND GRADING CRITERIA
--- | ---
Finding and Telling Stories with Data In this initial set of classes, we get acquainted with an arc of inquiry that leads from questions and data to visual arguments using data. | Hands-on activities, readings, and discussion of case studies
Charts and Creative Charts This includes traditional charts (bar chart, line chart, etc), novel charting (sankey diagram, tree diagram, etc), and explanatory graphics and infographics. | Small group project sketch, graded on: How centrally the data is featured The appropriateness and effectiveness of your use of the charting technique for the story you are trying to tell
Data Sculptures These are physical objects that represent the data in some way that tells a story. These could be made with cardboard and scissors, or digital design tools and a 3D printer. | Small group project sketch, graded on: How centrally the data is featured The appropriateness and effectiveness of your use of the charting technique for the story you are trying to tell The cohesiveness of your story The appropriateness of the implementation for your audiences
Personal Stories These are evocative images, quotes, and stories that connect your audience to the reality of some dataset. | Small group project sketch, graded on: How centrally the data is featured The appropriateness and effectiveness of your use of the medium for the story you are trying to tell The cohesiveness of your story The appropriateness of the implementation for your audiences
Participatory Data Games These could be anything from online quizzes, to activities where participants represent the data with their bodies. | Small group project sketch, graded on: How centrally the data is featured The appropriateness and effectiveness of your use of the mapping technique The cohesiveness of your story The appropriateness of the implementation for your goals The appropriateness of the implementation for your audience
Maps and Creative Maps This includes traditional map-making, paper-based map creation, and unmapping techniques that break some of the classic rules of mapping (but still use their visual language). | Small group project sketch, graded on: How centrally the data is featured The appropriateness and effectiveness of your use of the mapping technique The appropriateness of the implementation for your goals The appropriateness of the implementation for your audiences
Final Project Studio The final group of classes is set up as a studio, focused on finding and telling your data story in order to create your final group project. The final project will integrate many of the skills and approaches we cover throughout the semester. I anticipate that one of the small projects you do in the technique modules will grow into your final project, but that doesn't have to be the case. The final project is NOT a sketch; you should be creating a real, functioning version of the idea you have in mind. The final module will consist mostly of in-class studio time to work and get feedback on your progress. This final project must gather, analyze, and synthesize various civic datasets into a data-driven presentation. See the Final Project Requirements page for more details. | Small group project, which may iterate on one of your earlier project sketches. It will be graded on: How centrally the data is featured The appropriateness and effectiveness of your use of the mapping technique The appropriateness of the implementation for your goals The appropriateness of the implementation for your audiences The appropriateness of your impact assessment techniques (ie. NOT the outcomes of your impact assessment) Assessments of your contributions by your team members

### Graduate Student Paper Reviews

This class is offered to undergraduates and graduate students, meeting together. Graduate students will be expected to offer more substantial work for all assignments. In addition to this, most classes will include extra readings for graduate students. For each class with such a reading, one graduate student will be picked to present a short review and any meaningful takeaways from that paper. This short presentation can be informal, and should include:

1. A review of the main argument of the reading
2. Your thoughts on any applications or "good to know" takeaways from the reading
3. At least one question to spark discussion about the reading's topic

For graduate students, this presentation will count towards your participation grade.

## Grading

Grading is broken down as described in the table below. Evaluation criteria for each assignment is detailed within the Course Requirements section above.

Group | Weight
--- | ---
In-class participation, attendance, peer-assistance, and graduate student paper reviews | 20%
Initial Assignments Homework 1: Data Visualization blog post Homework 2: Data log blog post | 15%
Technique Sketches with Writeups Charts and creative chart project Data sculpture project Personal story project Maps and creative maps project Participatory game project | 45%
Final Project Data blog posts Sketch presentation Final presentation Methodology and impact narratives Teammate review | 20%

## Course Policies

You are expected to produce your own original work for each assignment. For group projects you are expected to contribute to the team's effort in valuable ways. Raise any concerns about team members on group projects to me privately as soon as possible. All students will be held to the standards of MIT's Academic Integrity Handbook (PDF).

### Plagiarism

Plagiarism — use of another’s intellectual work without acknowledgement — is a serious offense. It is the policy of the CMS/W Faculty that students who plagiarize will receive an F in the subject, and that the instructor will forward the case to the Committee on Discipline. Full acknowledgement for all information obtained from sources outside the classroom must be clearly stated in all written work submitted. All ideas, arguments, and direct phrasings taken from someone else’s work must be identified and properly footnoted. Quotations from other sources must be clearly marked as distinct from the student’s own work. For further guidance on the proper forms of attribution, consult the style guides available in the MIT Writing and Communication Center and the MIT Website on Plagiarism.

### Technology Policy

Laptops, tablet, or cell-phones may be used for note-taking during discussions. That said, there will be "zero-tech" times during classes when you are required to put away / shut-off all technology in order to engage in discussion, criticism, etc.

### Attendance Policy

Email the instructor as soon as you know that you will be absent for any reason. Your participation grade will go down by 15 points with each unexcused absence (out of a total of 50) but there are opportunities to make up some lost points by doing extra credit assignments. If you miss an in-class assignment such as a presentation or group exercise you should be prepared to take a 0 for it.

### Getting Help

I will be available for help over email; no office hours are scheduled. For writing assistance please use the MIT Writing and Communication Center.