Spring 2021 - POL 390 D100
Data Visualization and Political Analysis (3)
Class Number: 5288
Delivery Method: Remote
Social data and digital technologies are rapidly transforming politics and society, including election campaigns, how governments make policy, the targeting of consumers, and our interactions and connections with one another. This course offers a hands-on introduction to data science with an emphasis on data visualization for political and social analysis. Students with credit for ECON 334, ECON 387 under the title "Applied Data Analysis", or POL 339 under the title "Politics and Data Science" may not take this course for further credit. Quantitative.
Social data and digital technologies are rapidly transforming politics and society, including election campaigns, how governments make policy, the targeting of consumers, and our interactions and connections with one another. The goal of POL390 is to offer a hands-on introduction to data science with an emphasis on data visualization for political and social analysis. Data science is a growing field and this course will help you develop employable skills. We will use the R programming language for this course, although no previous experience with R is assumed or required. I will also introduce the students to LATEX, a nerdy/awesome typesetting alternative to Microsoft Word, but students are not required to use LATEX for the course.
If you have been thinking of strengthening your quantitative skills but have been intimidated, the pandemic may be a good opportunity to take this course. This version of POL390 has beed designed with the pandemic in mind: the course is somewhat self-paced and can be completed asynchronously. To acknowledge the stress that students are facing, this course has a lighter than normal workload with no mid-term or final exams.
Pre-recorded (asynchronous) lectures will be released on Mondays (“the Monday lecture”) and the class has the option of meeting on Wednesdays for a one-hour lab session to practice using the statistical software program, R (“the Wednesday lab”). During the recorded
Monday lecture, the professor will offer an overview and explanation of the week’s topic. During the Wednesday lab, the professor will offer a practical illustration of week’s topic in R. During the Wednesday lab, students may follow along live and interrupt with questions. The Wednesday labs will give students an opportunity to get hands-on practice doing statistical analyses with the help of the professor and will help the students prepare for their problem sets. Attendance at the Wednesday labs is completely voluntary. All labs will also be recorded and published on Canvas. Because all the materials will be available asynchronously, this course has no conflicts with other courses. In addition to office hours, the professor will available to students to meet by appointment over Zoom.
Students may watch the recorded lectures when it is convenient for them, provided they watch the Monday lecture before the subsequent Wednesday lab. To help motivate students, every week there will be a mandatory quiz (the “participation quiz”) based on the Monday lecture due anytime before the Wednesday lab. The participation quiz is open book and will be easy for students who have watched the lecture. With respect to homework, there will be three problem sets due throughout the course. The problem sets will be based on the materials covered in the lectures and labs. Students will also have a relatively short (max 8 pages) final project due at the end of the term. Students are also encouraged to submit an optional, short (max 1 page) research proposal beforehand. The optional research proposal is designed to encourage students to keep up with the course and to think ahead about their projects. As motivation, students who submit the research proposal will receive a 1% bonus added to their final grade. Students will be required to present their preliminary analysis (students can submit a video presentation) prior to submitting their final research report.
• Lectures: Released Monday at 4:30PM (pre-recorded, available online to watch asynchronously)
• Labs: Wednesday 2:30PM–3:20PM (option to attend synchronous labs, or watch recordings asynchronously)
• Office hours: Wednesdays 3:30-4:20PM, or by appointment
- “Participation” (weekly quizzes based on lecture videos) 20%
- Problem set 1 15%
- Problem set 2 15%
- Problem set 3 15%
- Preliminary Research Presentations 10%
- Final Research Report (max 8 pages) 25%
- Optional Research Project Proposal (max 1 page) Bonus
Imai, Kosuke. 2017. Quantitative Social Science An Introduction. Princeton University Press.
Department Undergraduate Notes:
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.
Each student is responsible for his or her conduct as it affects the University community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html
TEACHING AT SFU IN SPRING 2021
Teaching at SFU in spring 2021 will be conducted primarily through remote methods. There will be in-person course components in a few exceptional cases where this is fundamental to the educational goals of the course. Such course components will be clearly identified at registration, as will course components that will be “live” (synchronous) vs. at your own pace (asynchronous). Enrollment acknowledges that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes. To ensure you can access all course materials, we recommend you have access to a computer with a microphone and camera, and the internet. In some cases your instructor may use Zoom or other means requiring a camera and microphone to invigilate exams. If proctoring software will be used, this will be confirmed in the first week of class.Students with hidden or visible disabilities who believe they may need class or exam accommodations, including in the current context of remote learning, are encouraged to register with the SFU Centre for Accessible Learning (email@example.com or 778-782-3112).