Courses
Courses
Data Science for Everyone
Data Science for Everyone (DS-UA 111) is a course that will change your life. It will empower you to understand and use data in a principled way to better explain, make decisions in, and predict outcomes in the world. Students will learn to conduct hands-on research in Python using real-world datasets as instruments to practice and apply principles of scientific thinking and causal inference. This course is open to all students regardless of prior programming or statistics experience. Learn more about the course in Data Science For Everyone course video.
Please see the Data Science For Everyone sample syllabus. Prerequisite: None.
Principles of Data Science (Effective Fall 2023)
Students who take Principles of Data Science (DS-UA-112) will explore the theoretical issues, methods, tools, and problems that relate to data-rich issues in the humanities, social sciences, and sciences. Students will learn the core concepts of inference and computing while working with real data.
Please the Principles of Data Science tentative syllabus. Prerequisite: Data Science for Everyone (DS-UA-111) or department permission.
Causal Inference
We often want to know the relationship between cause and effect. In Causal Inference (DS-UA-201), students will learn to design and conduct experiments, define causation in the context of various liberal arts disciplines, and explore underlying theories, identify preconditions, and understand threats of validity to less-than-robust experiments. By the end of this course, students will be equipped to think about, interpret, and test for possible causal relationships between variables of interest.
Please see the Casual Inference sample syllabus. Prerequisite: Principles of Data Science (DS-UA 112)
Responsible Data Science
The first wave of data science focused on accuracy and efficiency: what can we do with data? The second wave is about responsibility: what should we do and not do? Responsible Data Science (DS-UA-202) tackles issues of ethics and responsibility in data science, including legal compliance, data quality, diversity, and algorithmic fairness, data and algorithm transparency, privacy, and data protection and security.
Please see the Responsible Data Science tentative syllabus. Prerequisite: Principles of Data Science (DS-UA 112)
Advanced Topics in Data Science
Time series, deep learning, and other advanced machine learning topics. Provides the theoretical underpinnings of advanced data science techniques, as well as hands-on activities to build a practical toolkit.
Prerequisite: Principles of Data Science (DS-UA 112) and completion of the probability and statistics requirement.
Practical Training in Data Science
Practical Training in Data Science (DS-UA 204) provides data science students with an opportunity to apply the knowledge gained in their coursework to practical problems in industry. This course is for majors and minors only and is graded on a pass/fail basis.
This course is restricted to data science majors or minors, who must have earned both a 3.0 cumulative GPA and a 3.0 data science GPA and must have completed half of the data science program of study. Does not count toward any major or minor. May be repeated once (taken two times total) for credit. Internship. 2 or 4 points.
This course is traditionally offered in the summer, but it will not be available in summer 2023.
For more information on the majors, minor and which semesters the courses are offered, please visit the CAS Data Science Bulletin.