Spring 2020 - POL 804 G100

Topics in Advanced Political Research Design and Methodology (5)

Class Number: 5286

Delivery Method: In Person

Overview

  • Course Times + Location:

    Jan 6 – Apr 9, 2020: Mon, 12:30–2:20 p.m.
    Burnaby

    Jan 6 – Apr 9, 2020: Wed, 12:30–1:20 p.m.
    Burnaby

  • Prerequisites:

    POL 800.

Description

CALENDAR DESCRIPTION:

The specific topics will vary from year to year but they will all focus on approaches (quantitative and/or qualitative) to research design, data collection and analysis in political science. May be repeated for credit if a different topic is taught.

COURSE DETAILS:

Politics and Data Science

Description:

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. How can we use this new data and the digitization of vast public records to understand politics? This course offers a hands-on introduction to data science with an emphasis on data visualization for political and social analysis.

The objective of this course is to introduce students to data science and its use in political research with an emphasis on data visualization. Seminars focus on workflow and project management with RStudio and R Markdown, importing data, tidying data, visualizing data through exploratory analysis and interactive media, and introducing network analysis, text analysis, and machine learning. Through in-class practice, quizzes, homework, and group projects, students will learn to apply data science techniques to political research.

Course Organization:

There will be three hours of seminar per week that involves a mix of lecture, group activities, and analysis. You are required to bring a laptop to all class meetings (note: the library loans out laptop computers for up to four hours)

Grading

  • Participation and Preparation for Class 10%
  • Quizzes 20%
  • Homework Assignments 40%
  • Group Project and Presentation 30%

Materials

REQUIRED READING:

Data Visualization: A Practical Introduction by Kieran Healy. Available in hard copy or here.

R for Data Science (“R4DS” 2016), by Hadley Wickham & Garrett Grolemund. Available in hard copy or here.

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:

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

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS