While data and artificial intelligence are driving many changes to our economic, social, political, financial, and legal systems, we know little about their foundations and governing dynamics. While the recombination and integration of diverse data creates vast new value, we currently have neither theory for how data can be combined nor an industrial policy for how to protect against the personal exposures and abuses that grow in proportion. Many of these issues call for treating data as a first-class citizen and thinking of it as an asset. What is the value of data? and how do we measure it? This course explores these questions from a multidisciplinary perspective that includes computer science, economy, social science, among others. The sessions are organized around broad themes related to the value of data. A typical session will consist of a short introduction by the instructor, followed by a paper discussion (students are expected to read the assigned papers ahead of the class), and finishing with a discussion about individual projects.
The course can be taken pass-fail or for a letter grade (i.e., as an approved elective). For a letter grade, you need to complete an individual, quarter-long project. You also need to attend classes, participate in discussions, and scribe for two/three classes. For pass-fail, you do not need to complete the individual project but all other requirements apply.
In addition to learning about a varied set of topics related to data science and management, these are the specific learning objectives of this class:
To become comfortable discussing papers written in different styles by researchers in diverse research communities.
To become comfortable scrutinizing diverse research methodologies including systems, analytical approaches, and empirical ones.
To learn how to distill the essence of a paper and ask research questions.
To learn how to conduct a literature review.
Instructor: Raul Castro Fernandez (email@example.com)
Lectures: Tuesday and Thursday 9:30am–10:50am (Central Time)
Prerequisites: Permission from the instructor.
Coursework: The bulk of the grade for this class corresponds to an individual, quarter-long project. See grading below.
Canvas Site: Go here
- Course Project (80%). A literature review on the topic of “The Value of Data for X” where you get to choose X in agreement with the instructor. The deliverables are: i) an 8-10 page double-column report (ACM format); ii) a presentation of the main ideas to the class. The project will be evaluated based on the quality of the content, the thoroughness of the literature reviewed, the quality of the systhesis, as well as the presentation quality.
- Class Participation (10%). Read the reading assignments and participate in discussions during class, office hours, and online.
- Class Scribing (10%). Students are responsible for scribing 2 classes. Good scribing should be organized, should capture the content and discussions during the class, and should be useful to other students.
The schedule is available here. Check it out frequently as it’ll likely change a bit throughout the quarter and I will include the readings here.
The schedule combines lectures with discussion-based sessions led by the instructor as well as workshops, where we all work together on an open question. Most lectures and discussion sessions require working on some readings before class. We will cover topics that include: data science, the 4th paradigm of science, data lifecycles, privacy, FAIR principles, the value of data from a macro perspective, the value of data from within an organization, sharing platforms, data markets, personal data markets, privacy, ethics, ownership of data, data unions cooperatives, data dividends, as well as other markets of information and more.
UChicago Health Pact
All students on campus are required to adhere to the guidelines in the UChicago Health Pact in order to promote a safe environment in the classroom.
Secure face coverings must be worn appropriately at all times at all times while in University buildings
Maintain a distance of 6 feet from others
Do not attend and in-person class if you feel unwell or are experiencing COVID-19 related symptoms
The complete text of the UChicago Health Pact along with additional information about COVID-19 protocols can be found here.
Any concerns over inappropriate PPE usage, physical distancing, cleaning/disinfection, or other COVID-19 related public health concerns should be directed to UCAIR. If there is an emergency, call 773-702-8181 or dial 123 on any campus phone.
If you were potentially exposed to COVID-19 or your COVID-19 test results come back positive, reach out immediately to C19HealthReport@uchicago.edu.