Fall 2019 Syllabus
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Course Information
Class: Sept 3rd — Dec 10th | Tues 6–9 pm at SVA DSI
Instructor: Wenfei Xu (wxu8@sva.edu)
Office Hours: Thurs 6–7 pm at DSI by appointment (I’m not there otherwise)
🗓 I’ll have slightly more flexibility if you’d like to meet via Google Hangouts or would like to come up to Columbia.
Course Description
Compelling data projects have a clear story to communicate to the reader. This is a first-principles course in which we will learn the components of speaking in an honest and articulate way with data and design elements. We will unpack each aspect of the data-narrative process: from setting up a testable research question, to deciding which data or data collection techniques to use, to finding relevant methods of analysis, to presenting our results in an engaging and informative way.
This is a data literacy course: The aim is to build intuition around the entire data-as-communication process — equal time will be spent on learning how to implement a good experiment as on how to design a good visualization. Throughout, we will discuss how each step in this process involves decisions (human or not — fallible regardless!) that aim to abstract and essentialize a version and aspect of “reality”. Do these abstractions reflect what they promise? How do we build the critical facilities to discern this?
This course is project and presentation-based. We will work on small assignments that make up a case study in the first half of the course; the second half will consist of creating a data narrative based on your own research interests. Throughout, you will practice presenting your work to each other.
Learning Objectives & Outcomes
The goal of this course is to build the technical and conceptual foundations in research design, data collection and analysis techniques, and information design principles. Through practicing (data collection, analysis, and visualization) and reading (techniques, We will sharpen our critical abilities through presenting and discussing our work with each other.
The core outcomes of this course include your ability to:
- articulate a testable research question and frame its importance in the context of your broader research goals
- identify relevant datasets or appropriate ways of collecting new data in support of your research question
- learn fundamental concepts in statistics
- sketch, design, and refine data visualizations in the service of your research question
- articulate your data narrative and process through blog posts and presentations
Attendance and Participation
Most of what you learn in this course will be through the process of working through and presenting your work, readings, projects, as well as providing constructive feedback to each other. In other words, active participation is a crucial aspect of the class. This means three things: 1) your punctual attendance demonstrates respect for others’ time; 2) be prepared for class so we can have a good conversation; 3) don’t be shy, we’re all just here to help one another.
If you know that you’ll miss any part of class, please email me in advance and arrange to get the class notes from a classmate. Two absences from class will result in a half step reduction in your grade. Four or more absences will result in a failure.
Each week you will have readings assigned and homeworks assigned. I will ask three people every week to lead discussions and I will call on 3–4 groups to talk briefly about their homeworks. I will also be asking questions during your weekly assignment presentations to ensure fair contribution amongst the team members.
I will repost assignments in Canvas, where possible. I will grade your assignments and aggregate them as contributions to your final grade.
Reading Responses
We will have required readings and in-class discussions based on these readings most weeks. Please submit a response to the weekly readings on Canvas (to do: Update this link) by 7 pm on Sunday. Your response should be at least 300 words. Also, include two questions based on the readings you’d like to discuss in class. Each week, we will have three students lead the reading discussions. Presenting students should be prepared to lead a 20 min discussion of the readings that provide a critical overview and
Homework Assignments: Case Study
Homework assignment will be completed in groups of two. Each of the homework assignments takes you through one aspect of the data-narrative process. Every week I will choose three groups to talk briefly (5 min) to talk about their homeworks.
- Week 9: You will collect and summarize all your assignments into one 15 min presentation.
Final Project
The final project will be your own choosing. This will incorporate all the aspects of the data narrative (research design, data collection, data analysis, visualization, presentation). The final project grades will 10% project proposal and 20% final project outcomes. You will work in groups of three and post weekly updates as Medium blog posts.
- Project proposals and presentations are due week 11
- Final project presentations (20 min) are due week 14
- You will have one week to make small revisions to your projects based on presentation feedback by week 15
Grading
Students will be evaluated on effort, personal progress and growth, class participation, assignments, and the final project. It is understood that coding is tough and you may be new to this, you will be graded on your progress throughout the class, your ability to complete assignments on time, your interaction with peer reviewers, and your ability to justify your decisions thoughtfully.
GRADE CALCULATION: Here is a basic breakdown of graded tasks along that trajectory:
- Attendance (10%)
- Weekly reading assignments responses (10%)
- Weekly homeworks that make up one case study (30%)
- Participation: Homework and reading presentations (20%)
- Final project (30%): Proposal: 10% | Project + presentation: 20%
Late assignments will be docked 1 letter grade per day until it reaches a “C”. If the assignment is not submitted it will be given a “0”.
Readings
Readings are assigned to correspond with that week’s assignment and class lesson. Everyone will write individual responses, which we’ll then discuss in class. Some of the academic readings can feel like a lot to wade through — be strategic about how you go through these readings. Here’s a video on strategies for reading academic papers.
As much as possible, I will point you to web resources — I know being a grad student is expensive! However, there are a few texts that I would like for you have absorbed by the end of this course and one in particular that I think is excellent:
Required:
->Cairo, Alberto. The truthful art: data, charts, and maps for communication. New Riders, 2016.
->Gravetter, Frederick J., and Larry B. Wallnau. Statistics for the behavioral sciences. Cengage Learning, 2016.
Optional:
->O’neil, Cathy. Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books, 2016
->Huff, Darrell. How to lie with statistics. WW Norton & Company, 1993.
->Pater, Ruben. Politics of Design: A Not So Global Manual for Visual Communication. Bis Publishers, 2016.
->Yau, Nathan. Data points: visualization that means something. John Wiley & Sons, 2013.
-> Murray, Scott. Interactive data visualization for the web: an introduction to designing with. “ O’Reilly Media, Inc.”, 2017.
-> Computational Statistics in Python
-> Reid, Howard. Introduction to Statistics: Fundamental Concepts and Principles of Data Analysis.
(If for any reason you are unable or uninterested to purchase the books, I will make some copies available. )
Support & Teamwork
In this course, you will be asked to form dedicated support networks consisting of 2–3 people per group. The goal is to bring you together to give each other immediate feedback, inspire one another, and give you a space to grow as colleagues. While you will all be submitting assignments as individuals, it is important that you learn to work together and be willing to help when asked and/or receive help when needed.
This is also a good opportunity to maybe work with some people you don’t know well or to build some more projects with people you already do!
Academic Integrity
All work submitted in this course must be the student or the group’s own work. Where you’ve received help or borrowed chunks of code and/or designs, you must make note of this in your homework. A violation of this policy will result in an F in the course.
Materials
Required:
- Thought sharing — Medium Account, signup here. Send me your username so I can add you to the class site.
- Making charts — Tableau, sign up here
- Code sharing — Github Account, signup here
Coding platforms on the web (i.e. no set-up needed phew)
- Sharing Python-based code— Google Colab
- Sharing Javascript-based code — ObservableHQ
- Testing/sharing front-end code snippets — CodePen
Calendar
Here you will find the links to the week-by-week materials and an overview of the course. I’ll update these as we go along, as we may decide to tweak the pace and content of the course.
Week 1 — Introductions and Narratives with Data (Sept 3)
Slides: here
Class:
->Intro + class logistics
->Lecture: The process of creating “narratives with data”
->Set up tools
->In-class exercise / start HW1
Readings: nothing due week 1
HW1 — Analogue data narratives
Week 2— Research Design and Research Questions (Sept 10)
Class
-> Discussion of HW1
-> Class discussion of readings
-> Lecture: Research design and research questions
-> Case study introduction
Readings:
Mensh, Brett, and Konrad Kording. “Ten simple rules for structuring papers.” (2017): e1005619.
Ranjit Kumar, Research Methodology: A Step by Step Guide for Beginners (Sage 2011), Chapter 4, p 43–58
Mats Alvesson and Jörgen Sandberg, “Research Questions: A Core Ingredient in Developing Interesting Theories” in Constructing Research Questions: Doing Interesting Research (Sage 2013)
(Optional): Elena D. Kallestinova, “How to write your first research paper”, Yale Journal of Biology and Medicine 84 (2011)
(Optional): Shannon Mattern, “Identifying Your Interests and Establishing a Research Plan”
HW2
Watch this space.
Week 3 — Data Collection (Sept 17)
Class
-> Short presentations of HW2
-> Class discussion of readings
-> Lecture + demo: Foraging and home-grown data
-> Guest lecture: Sam Lavigne
Readings
Abdul Latif Jameel Poverty Action Lab, section on “Measurement & Data Collection”
Shannon Mattern, “Methodolatry and the Art of Measure”, Places (Nov 2013)
Pierre Bourdieu, “Public Opinion Does Not Exist” in Communication and Class Struggle ed. Armand Mattelart and Seth Siegelaub (International General 1979), p 124–130
Kate Crawford, “The Hidden Biases of Big Data”, Harvard Business Review (Apr 2013)
(Optional) Michael Crutcher, Matthew Zook, “Placemarks and waterlines: Racialized cyberscapes in post-Katrina Google Earth”, Geoforum 40 (2009)
(Optional) Jim Thatcher, “Living on Fumes: Digital Footprints, Data Fumes, and the Limitations of Spatial Big Data”, International Journal of Communication vol. 8( 2014)
HW3
Watch this space.
Week 4 —Cooking with Data Pt.1 (Sept 24)
Class
-> Short presentations of HW3
-> Class discussion of readings
-> Lecture: Introduction to statistical thinking, central tendencies
Readings:
-> Gravetter, Frederick J., and Larry B. Wallnau. Statistics for the behavioral sciences. Cengage Learning, 2016. Chapters 1 and 3
-> (Optional) Chapters 1 (“The Sample with Built-In Bias”) and 2 (“The Well-Chosen Average”) from How to lie with Statistics
HW4
Watch this space.
Week 5 —Cooking with Data Pt.2 (Oct 1)
Class
-> Short presentations of HW4
-> Class discussion of readings
-> Lecture: Distributions and variance
Readings:
-> Gravetter, Frederick J., and Larry B. Wallnau. Statistics for the behavioral sciences. Cengage Learning, 2016. Chapters 2 and 4
-> (Optional) Chapter 1 (“Bomb Parts: What is a Model?”) from Weapons of Math Destruction
-> (Optional) Chapter 3 (“The Truth Continuum”) until the end of “Mind Bug 3” from The Truthful Art
HW5
Watch this space.
Week 6 —Abstractions: Visual Syntax Principles and Charts (Oct 8)
Class
-> Short presentations of HW5
-> Class discussion of readings
-> Lecture: Translating metrics to visuals
Readings:
-> Chapter 5 (“Basic Principles of Visualization”) and 6 (“Exploring Data with Simple Charts”) in The Truthful Art
-> To do: Add data viz talks
HW6
Watch this space.
Week 7 — Abstractions: More Principles and More Charts (Oct 15)
Class
-> Short presentations of HW6
-> Lecture+ Workshop:
Visualization distributions
In-class visualization workshop
Readings:
-> Chapter 7 (“Visualizing Distributions”) in The Truthful Art
-> To do: Add data viz talks
HW7
Watch this space.
Week 8 — Abstractions: Maps! (Oct 22)
Class
-> Short presentations of HW7
-> Lecture+ Workshop:
Intro to Cartography
In-class mapping workshop
-> Case study presentation debugging and trouble-shooting
Readings:
-> Chapter 10 (“Mapping Data”) in The Truthful Art
-> To do: Add data viz talks
-> Alexa Todd’s Cartography design principles from Maptime PDX (May 14, 2015)
(Optional) “When Maps Lie” CityLab article (June 25, 2015)
(Optional) Alan McConchie and Beth Shachter “Anatomy of Web Map” from Maptime SF
HW8
Watch this space + prepare presentations for week 9
Week 9— Case Study Review and Presentations (Oct 29)
Class
-> HW 2–8 / Case study presentations
Homework
🎃 No assignment this week 🎃
Videos instead of readings this week :)
-> Daniel Goddemeyer & Dominikus Baur. “The hidden ethics of our personal data” from TEDxUCLouvain
-> Jer Thorp. “Make data more human” from TedxVancouver
Week 10 — Revisiting “Narratives with Data” (Nov 5)
Class:
-> Recap ALL the stuff we learned and presentation feedback
-> Lecture: More advanced “narratives with data”
Week 11 — Cool Tools: Analysis (Nov 12)
Class:
-> Project proposals presentations
-> Project progress check-ins
-> Workshop: Python + Notebooks (dataframes, regressions/numpy/scipy, charts?, machine learning?, where to learn more)
Week 12 — Cool Tools: Visualization (Nov 19)
Class:
-> Project progress check-ins
-> Workshop : Javascript + D3/P5 + Notebooks/Sandboxes
Week 13 — *In-class trouble-shooting session* (Nov 26)
Class:
-> Sign up for a 20 min time slot with me to trouble-shoot/debug your projects
Week 14 — Final Presentations (Dec 3)
Class:
-> 20 min for each group
Week 15 — Final Project 1-on-1s (Dec 10)
Class:
-> Sign up for a 20 min time slot with me
