This page is subject to change before the start of the course.
Spring 2022 Special Requirement (source)
During this pandemic, it is extremely important that you abide by the public health regulations, the University of Pittsburgh’s health standards and guidelines, and Pitt’s Health Rules. These rules have been developed to protect the health and safety of all of us. Universal face covering is required in all classrooms and in every building on campus, without exceptions, regardless of vaccination status. This means you must wear a face covering that properly covers your nose and mouth when you are in the classroom. If you do not comply, you will be asked to leave class. It is your responsibility have the required face covering when entering a university building or classroom. For the most up-to-date information and guidance, please visit coronavirus.pitt.edu and check your Pitt email for updates before each class.
If you are required to isolate or quarantine, become sick, or are unable to come to class, contact me as soon as possible to discuss arrangements.
Data-driven models have been increasingly used in many domains to assist in human decision-making that has a significant impact on people’s lives – from job hiring and promotion, college admission, judicial decision, to business or public service delivery. The development of decision aids has been made possible both by voluminous data and new data science tools that can exploit complex structures and patterns in data.
This course focuses on both concepts and practice in order to understand and cope with the ethical challenges in data science and data-driven decision making. We will introduce (a) the core concepts of fairness and interpretability/explainability and (b) analytic and technical tools to mitigate emerging problems in the real world.
Topics to be covered:
See the course schedule for weekly topics.
This course will use R and/or Python for computing. GitHub will be used for homework and project assignments, where tools such as Jupyter Notebooks or R Markdown will be used for creating reproducible data science documents.
Students are expected to be familiar with the basics of Probability and Statistics, Data Mining/Machine Learning, and should be comfortable with programming with DM/ML toolkits. Students need to have a willingness to do interdisciplinary research, and be comfortable to learn concepts through reading technical, legal and policy documents.
Grades are based on three major activities listed below. Assignments are due as scheduled, and grades on late work will be decreased by 10% per day late. See the assignment page for more details.
Class participation will be assessed through online quizzes and discussions assigned each week.
This course will use online materials and academic readings. There will be reading assignments over the course of the semester. Links to the electronic copies of these readings will be provided. There are no textbooks.
Readings will be assigned throughout the semester – roughly one reading assignment per week. Each reading assignment is relevant to the weekly topic, and is chosen to help you connect the technical tools to more practical challenges in the real world. The reading assignment is to enrich your data science understanding and problem-solving skills and help you develop project ideas. The tentative list of readings to be assigned is available here.
The reading assignment will be evaluated via quizzes, reading reflection and discussion.
See the university policies page.