Stanford: Shaping the Data Science Revolution

Students learn how to utilize data to tackle social issues in the first course for new Data Science

Data scientists examine enormous volumes of information about the world daily, applying computational techniques to discover fresh perspectives on a problem or occurrence and deciding what to do.

Yet Jeremy Weinstein, a political scientist at Stanford, argues that using data alone is insufficient; it must also be understood in its social and political context. A Bachelor of Science in Data Science and a Bachelor of Arts in Data Science & Social Systems are two new degrees that Weinstein and Stanford University statisticians Guenther Walther and Chiara Sabatti introduced this year.

Combining Engineering and Social Science Viewpoints:

The course combined two mentalities: an engineering mindset centered on learning algorithmic design and optimization and a social science mentality grounded in a knowledge of causal inference.

These viewpoints are interrelated, as Weinstein and Nobles underlined to their pupils. When you ask and answer causal questions about a social problem, you're deepening your comprehension of the underlying causes, which can give you clues about how you can go about solving it, and when you design an algorithmic solution, you then want to know its effect when it's deployed in the world, which brings you back to causal inference, through modules created at Stanford with academics from other professions, students investigated the usefulness of these various approaches.

 The Significance of Inquiries:

Orszag, a Data Science and Social Systems major interested in questions of democracy and government, noted that without the proper research question, one cannot advance. Decrying what issue or situation you want your data to address is tricky.

Ava Kerkorian, a potential Data Science and Social Systems student, and Orszag collaborated to consider increasing voter confidence in the electoral process.

During their study design process, Kerkorian and Orszag reportedly sent ideas back and forth as they tried to determine how to approach such a complicated problem in a focused, scalable, and implementable way.

We had to ask ourselves numerous times throughout this project how we measure trust. How would success appear? What is self-assurance? Are we even certain that this is what we want? Said Kerkorian.

Considering Impact Seriously and Ethically:

The course forced Serena Lee, a data science and social systems major, to reflect critically on what it means to be a responsible data scientist. This lesson taught me that the work starts with how to gather data because that involves a lot of value-laden decisions," Lee said.

These decisions include who to include in the dataset, what questions to ask, how to phrase them, and how far in the past to look at the data. Along with Annie Zhu, she wanted to examine the impact of disinformation based on videos as opposed to misinformation based solely on text for her final project. They specifically suggested looking into the various methods platforms could use to flag potentially hazardous posts.