The UO’s data science program has a data science + domain structure, which means you study core quantitative methods – and apply those methods to your chosen area of emphasis, or what we call a “domain”.
This gives you a strong understanding of how to extract data using quantitative methods such as math, statistics, and machine learning, and how to visually communicate those results in ways that are relevant to your chosen domain. You'll take two to three core courses, providing insight into the basics of the domain. After completing the quantitative skills in the program, you then take four elective domain courses – providing the opportunity to apply those quantitative skills to data sets within the area.
Foundations of Data Science I
DS 101 uses a quantitative approach to explore fundamental concepts in data science. Students will develop key skills in programming and statistical inference as they interact with real-world data sets across a variety of domains. Ethical ramifications of data collection, data-driven decision-making, and privacy will be explored.
Foundations of Data Science II
In DSCI 102, students apply increasingly sophisticated computational and statistical techniques to data across numerous domains. Topics include the normal distribution, bootstrapping, confidence intervals, linear regression, and classifiers. Ethical concerns resulting from use of the techniques in this course will be addressed.
Data Structures for Data Science
DSCI 299 is an experimental version of CS212 Computer Science III; the only difference between CS212 and DSCI 299 is that the programming language used in DSCI 299 is Python. This 3rd course in the CS introductory sequence covers working in Linux, advanced programming using the Python language, and constructing and using basic data structures.
Principles and Techniques of Data Science
DSCI 311 prepares students to successfully apply computational and statistical techniques to upper-division coursework in data science as well as quantitative, data-driven courses in other domains or subject areas. Topics include managing data with software programs, data cleaning, handling text, dimensionality, principal components analysis, regression, classification and inference.
Probability and Statistics for Data Science
In DSCI 345, students will be provided with a foundational basis in probability and statistics for work in data science. The course covers both tools for modeling randomness and calculating properties of those models, and the process of estimating quantities from data. An important thread throughout the course is on simulating data: being able to construct and simulate from sophisticated models for random data generation.
Machine Learning for Data Science
DSCI/CS 372 is an introduction to Machine Learning, the subfield of Artificial Intelligence. This course will introduce the basic ideas and techniques in machine learning. The techniques you learn in this course will serve as the foundation for further study in any machine learning-related area you pursue. Students have to use Python for course projects.
DSCI 411 provides a student the opportunity to apply the theoretical knowledge and techniques acquired during the Data Science degree curriculum to a project involving real data from the student’s domain of specialization. Each student is advised by an individual from the specialization domain and another individual from the DSCI program.
These sample schedules provide a broad outline of your course load – though the specific course outline will be determined by your domain area.