DS 2001 Data Science Programming Practicum for Social Sciences and Humanities

What is this course about?

Digital traces of our daily lives are increasingly recorded, aggregated, analyzed, and used to shape our experiences, and large-scale computational methods and resources for understanding human behavior are accessible like never before. These data and methods offer the potential for rich insights into society, while simultaneously introducing new ethical and infrastructural challenges. In this practicum we will:

1. Practice the skills you learn in DS2000 using applied examples drawn from the social sciences
2. Read about how data science is impacting society
3. Develop an intuition for computational social science
4. Interrogate the ethical and political ramifications of data science

The practicum will meet once a week, where we’ll discuss short readings and practice programming through hands-on tutorials and assignments. Your grade will be based on reading annotations, programming exercises, a project proposal, and a final project and presentation.

Who is the instructor?

My name is Ryan Gallagher and I’m a Ph.D. candidate in Network Science here at Northeastern. I research online communication on social media platforms like Twitter, Facebook, Reddit, and TikTok. I’m particularly interested in how people amplify their voices online to speak against oppression and marginalization.

If you need to get in touch, here’s how:

Where can I find the syllabus?

The syllabus and all details for the course can be found here.

For links and passcodes to office hour Zoom sessions, see the version of the syllabus posted on Canvas.

What is the schedule for this course?

Remember, all readings are on Canvas and that they are due on the day for which they are assigned (e.g. readings assigned for September 15th should be read in preparation for September 15th’s class).

September 8th - Introduction

Practicum Assignment 1 - Installing Software and Hello World

September 15th - Big Data

Practicum Assignment 2 - Input, Variables, and Food Rescue

• Lazer et al. (2014). “The Parable of Google Flu: Traps in Big Data Analysis.” Science.

• boyd & Crawford. (2011). “Critical Questions for Big Data.” Information, Communication, & Society.

• Foucault Welles. (2014). “On Minorities and Outliers: The Case for Making Big Data Small.” Big Data & Society.

September 22nd - Data Visualization

Practicum Assignment 3 - Boston Neighborhood Demographics

• D’Ignazio & Klein. (2020). “Chapter 3: On Rational, Scientific, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints” in Data Feminism.

• Yau. (2018). “Visualizing Incomplete and Missing Data.”

September 29th - Data Ethics

Practicum Assignment 4 - Bluebike Commuting

• Salganik. (2018). “Chapter 6: Ethics” in Bit by Bit: Social Research in the Digital Age.

October 6th - Categorization

• D’Ignazio & Klein. (2020). “Chapter 4: ‘What Gets Counted Counts’” in Data Feminism.

• Brown (2020). “The changing categories the U.S. census has used to measure race” from Pew Research Center.

October 13th - Location Data

Practicum Assignment 5 - Police Shootings

• Thompson & Warzel. (2019). “Twelve Million Phones, One Dataset, Zero Privacy” in The New York Times.

• Boorstein et al. (2021). “Top U.S. Catholic Church official resigns after cellphone data used to track him on Grindr and to gay bars​​” in The Washington Post.

• Wang. (2021). “For the U.S. Census, Keeping Your Data Anonymous and Useful is a Tricky Balance” in NPR.

October 20th - Algorithmic Oppression

Practicum Assignment 6 - Baseball Leaderboards

• Noble. (2018). “Introduction: The Power of Algorithms” in Algorithms of Oppression: How Search Engines Reinforce Racism.

• Angwin et al. (2016). “Machine Bias” in ProPublica.

October 27th - Algorithm Audits

Practicum Assignment 7 - Midterm Review

• Keegan. (2021). “Facebook Got Rid of Racial Ad Categories. Or Did It?” in The Markup.

• Johnson. (2021). “Twitter’s Photo Crop Algorithm Favors White Faces and Women” in Wired.

• Wall Street Journal. (2021). “Investigation: How Tiktok’s Algorithm Figures Out Your Deepest Desires” in The Wall Street Journal.

November 3rd - Conducting Computational Social Science

Practicum Assignment 8 - Vaccine Hesitancy Tweets

• Salganik. (2018). “Chapter 2: Observing Behavior” in Bit by Bit: Social Research in the Digital Age. Only need to read Sections 2.4-2.6.

• Hoffman et al. (2021). “Integrating Explanation and Prediction in Computational Social Science.” Nature.

November 10th - Replication

• Yong. (2021). “Replication Studies: Bad copy.” Nature.

• King (1995). “Replication, Replication.” Political Science and Politics.

• Anonymous. (2021). “Evidence of Fraud in an Influential Experiment about Dishonesty.”

November 17th - Social Media Data

• Freelon. (2018). “Computational Research in the Post-API age.” Political Communication.

• Pasquetto et al. (2020). “Tackling Misinformation: What Researchers Could Do with Social Media Data.” Harvard Kennedy School Misinformation Review.

November 24th

No class: Thanksgiving break.

December 1st - Social Network Data

• Robins. (2015). “The difference with social networks research” in Doing Social Network Research: Network-based Research Design for Social Scientists.

December 8th - The Pandemic

Due: Jupyter notebook writing methods, analysis, and interpretation

• Buckee et al. (2021). “Thinking Clearly About Social Aspects of Infectious Disease Transmission.” Nature.