Data, Evidence, and Communication for the Public Good
USC PPD534 / Fall 2020 / 4 units
Email: boeing at usc dot edu
Office hours: Thurs 17:30-18:30, Zoom (you must sign-up in advance online)
Classroom location and meeting times are available online
TA: Kurt Wyatt (email: kdaum at usc dot edu; office hours: Wed 17:00-18:00, Zoom)
This course provides you with a toolkit for telling stories with urban data. It will introduce basic coding, stats, and reasoning with evidence. The course takes a computational social science approach to working with data. It uses Python and Jupyter notebooks to introduce coding and statistical methods that students can reproduce and experiment with in real-time in the classroom. We start the semester with the basics of coding, then move on to data loading and analysis, then on to basic statistics, then hypotheses and the scientific method, and finally a critical assessment of smart cities and urban informatics.
Each week, students will be expected to:
- Complete and be prepared to discuss all assigned readings
- Complete and submit any assigned reading responses
- Attend the lecture
- Complete and submit any assigned group projects
The course has no specific prerequisites. Coding itself will be taught from the ground-up. However, this course requires patience and practice: learning to code will take lots of trial-and-error, self-direction, repetition, and experimentation on your part. You will get out of it what you are willing to put into it. Please note that this syllabus is a living document and may be updated by the instructor during the semester as needed.
- Write simple code to manipulate, analyze, and visualize urban data
- Understand how to use basic descriptive and inferential statistics to evaluate and interpret social science evidence
- Tell stories using a combination of text and visuals about public issues using data and evidence
- Evaluate the politics and ethics underlying how technology and innovation impact cities and planning processes
We are available if you need help throughout the semester and are happy to answer your questions. By email, you should expect a reply typically within approximately one working day. Office hours info is provided at the beginning of the syllabus.
If you have a question about class material, homework, or a coding/data problem, contact the TA first. If you have a specific question for the professor outside of those categories, please sign up for an office hours slot to chat.
Given the nature of this course, we do expect a few things of you before you seek assistance with coding/data questions:
- Close all open programs, restart your computer, then try your task again
- Search google and stackoverflow for the topic/problem (for example, the name of the function you're struggling with or the error message you are seeing)
- Go back through the relevant lecture materials to look for any insights
- Go back through the assigned reading materials to look for any insights
If the above steps haven't solved your problem, send an email (or attend office hours) and include the following information:
- A detailed description of what you're trying to do, why, and how
- A complete minimal reproducible example of your code so far (never send screenshots of code)
- What you've already tried to do to solve your problem and what you have learned from it (specifically, explain the results of steps 1-4 above, including relevant links from stackoverflow etc)
We do not ask for this to be pedantic. Rather, we need it to be able to help you solve your problem.
Coursework will be based on free open-source software.
Copyrighted course reading materials are available via blackboard for enrolled students to download. The course lectures assume that you have read the assigned readings prior to the class session and are now reasonably fluent in their contents and ready to discuss/debate them in class.
Per USC guidelines, for each course unit the university expects 2 hours of out-of-class student work per week. This is a 4 unit course. Therefore, you should expect an average of 8 hours of out-of-class work each week: please budget your time accordingly. The balance will vary from week to week, but will comprise a mix of individual reading, individual writing, and group data/code assignments. See the schedule below for assignment due dates and see the "assignments" folder for instructions. Active participation is expected (and graded) in the classroom.
Final grades will be assigned according to the following, for a total of 1000 points:
- 200 points: 8 individual reading responses × 25 points each
- 300 points: 5 group assignments × 60 points each
- 200 points: midterm exam
- 200 points: final group project
- 100 points: active participation in classroom and team work
Assignments must be submitted via Blackboard by 23:59 pacific time on their due date. Late assignment submissions will be deducted 20% per day. Late final project submissions will not be accepted or graded. It is your responsibility to ensure that all submissions have gone through, so please visually confirm successful submission in the system.
Group assignments leverage your diversity of skills and life experiences. We expect each of you to contribute to your group assignments in whatever way you can create proportional value: some will be better at code, others at writing, etc. We don't intend to hold your hand: coordination and delegation are challenging but these are necessary professional skills. Organize, collaborate, and communicate proactively with one another. There will be a formal group peer review at the end of the semester to help us assign participation grades.
All reading materials are available on blackboard if no link is provided here.
Aug 20 - Welcome/Intro to Computation
Readings to be completed prior to class:
Pre-Survey
Aug 27 - Collecting Data: Census, Websites, Portals
Readings to be completed prior to class:
- Wheelan, Naked Statistics, intro + ch. 1
- Urdan, Statistics in Plain English, ch. 1
- Macdonald, The American Community Survey
- Optional reading about census counts from LA Times
Reading response 1 due the night before class
Group assignment 1 due the following Wed
Sep 3 - Coding Bootcamp I
Readings to be completed prior to class:
- Downey, Think Python, ch. 1-3
Sep 10 - Coding Bootcamp II
Readings to be completed prior to class:
- Downey ch. 4-7
Group assignment 2 due the following Wed
Sep 17 - Data Cleaning and Descriptive Stats
Readings to be completed prior to class:
- Wheelan ch. 2-3
- Urdan ch. 2-3
Reading response 2 due the night before class
Sep 24 - Data Visualization
Readings to be completed prior to class:
- Tufte, Visual Display of Quantitative Information, ch. 1-3
- Rost, blog post on choosing colors
Reading response 3 due the night before class
Group assignment 3 due the following Wed
Oct 1 - Spatial Analysis and Mapping
Readings to be completed prior to class:
- Gimond, Intro to GIS and Spatial Analysis, ch. 1, 2, 9
- Wheelan ch. 5, 7
Reading response 4 due the night before class
Group assignment 4 due the following Wed
Oct 8 - Qualitative Methods in Practice
Readings to be completed prior to class:
- Shah and Corley - Building Better Theory
- Sandercock - Who Knows
- Morgan and Smircich - The Case for Qualitative Research
Oct 15 - Mid-Term Exam
Exam will comprise a mix of multiple choice and short-answer questions, including some asking you to write short snippets of code.
Oct 22 - Social Science and the Scientific Method
Readings to be completed prior to class:
- Okasha, Philosophy of Science, ch. 1-4
Reading response 5 due the night before class
Oct 29 - Hypotheses, Inference, Confidence, Uncertainty
Readings to be completed prior to class:
- Wheelan ch. 8-10
- Urdan ch. 4-7
- Jurjevich et al, Navigating Statistical Uncertainty
- WSJ article (on blackboard) + CityObservatory response
Reading response 6 due the night before class
Nov 5 - Statistical Models
Readings to be completed prior to class:
- Wheelan ch. 4, 11-13
- Urdan ch. 8, 9, 13
- Optional reading about p-hacking
Reading response 7 due the night before class
Group assignment 5 due Nov 15
Nov 12 - Smart Cities, Ethics, and Evidence-Based Planning
Readings to be completed prior to class:
- Okasha ch. 5, 7
- Kitchin, The Ethics of Smart Cities and Urban Science
- Mattern, A City Is Not a Computer
Reading response 8 due the night before class
Nov 18 - Final Group Projects Due
Review the student handbook for expectations on academic integrity. In your reading responses (and all other homework), make sure you do not copy/paste any words, images, code, or other content written by another author (including the author of the piece to which you are responding) without quote marks and citation. If you use someone else's words, you must always use quote marks and cite them. If you refer to their ideas, you must cite them to make it clear whose ideas you're referring to. In a reading response, citing the reading's author inline is sufficient for us to understand the citation, as in the example above. In other contexts, use a formal reference to make your citation clear.
Any student requesting academic accommodations based on a disability or ongoing mental health concern is required to register with Disability Services and Programs (DSP) each semester. A letter of verification for approved accommodations can be obtained from DSP. Please be sure the letter is delivered to the instructor as early in the semester as possible, as the accommodation can only be implemented upon receipt of the letter. Visit DSP's web site for more details. For further support, I encourage you to contact USC Support and Advocacy (uscsupport@usc.edu). To maintain fairness and equality for all students, extensions to due dates are only granted in accordance with these official accommodation letters. If you need to request a one-time emergency extension to an assignment (e.g., due to a major illness or to a death in the family) you must do the following proactively prior to its due date: 1) provide written documentation, such as an official doctor's note, explaining that you are unable to complete the assignment by its due date and 2) work out an extension with the instructor.
Plagiarism, presenting someone else's ideas as your own, either verbatim or recast in your own words, is a serious academic offense with serious consequences. Please familiarize yourself with the discussion of plagiarism in SCampus in Part B, Section 11, "Behavior Violating University Standards" https://policy.usc.edu/scampus/. Other forms of academic dishonesty are equally unacceptable. See additional information in SCampus and university policies on scientific misconduct, https://policy.usc.edu/scientific-misconduct.
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