Choosing an Emphasis Domain
Data science undergraduate students can choose from a variety of domain areas from across the university. Domain specialization provides students with the opportunity to apply core data science knowledge and skills to an area of interest. All domain classes count as dual credit for additional majors/minors.
Students are encouraged to select a domain area when declaring their major, or before their second year of study when students typically begin taking classes in their chosen domain area.
Domain Areas
You can choose from ten different domain areas, and we are working across the university to build more.
Accounting Analytics
Data has proliferated in business as organizations generate large volumes of information within their day-to-day operations while increasingly having access to externally created information as well.
Data science applied to accounting data can help organizations understand the implications for decision-making and provide better insights. You might delve into company sales data, purchasing data, contracts, or company disclosures to help solve a variety of business problems.
In the data science domain area of accounting analytics, you will learn to search for relationships between different variables and outcomes they influence, driving business decisions, and informing success.
Accounting Analytics Domain Requirements
Biology
Recent technological breakthroughs in DNA sequencing mean that scientists can characterize an organism’s entire genome in a matter of days. But a great challenge remains in translating that genomic sequence—nature’s data set—into biology.
That translation is fundamentally changing how we study biology.
In the data science domain area of biology, you will find yourself on the cutting edge of the field, working in the acquisition, analysis, and interpretation of data and how it applies to gene function, disease, ecology, and the assembly and characterization of new genomes.
Cultural Analytics
Disciplines like history, English, and philosophy predate the era of computational approaches and large datasets, and their foundational methods are highly attuned to the study of human cultures on the micro level—such as the analysis of a single text or author, historical period, artistic movement, or abstract concept, grounded in a specific place and time. A subset of scholars trained in these disciplines have embraced and, at times, challenged the emerging field of data science, developing a cluster of approaches sometimes called “cultural analytics” to gather, analyze, interpret, and communicate insight about their objects of study on a macroscale.
This domain emphasis is highly interdisciplinary, drawing on courses across numerous disciplines as opposed to one or two fields of inquiry. Yet the core courses are designed to ground students firmly in the study of culture as it both shapes and is shaped by social, political, economic, and ideological forces.
The required library science elective applies a critical framework informed by cultural studies to the collection, organization, storage, and transmission of data, preparing students to curate, interrogate, and responsibly manage humanities datasets. You might, for example, develop a social network analysis of a given historical milieu, mine a large corpus of literary texts for formal patterns, or model the spread of folklore across time and space. Students will also have opportunities to practice conveying their findings to broad audiences through visualization, sonification, data-driven storytelling, and other media forms that draw on established genres, rhetorical traditions, and conventions, and that can be most effectively deployed by data scientists grounded in and attuned to the richness and complexity of human cultures.
Data science majors who concentrate in cultural analytics will be well positioned to minor or double-major in any number of fields, including digital humanities, media studies, folklore and public culture, environmental humanities, English, and more.
Cultural Analytics Domain Requirements
Earth Sciences
Earth sciences encompass the study of climate change, volcanoes, earthquakes, landscape evolution, and the countless geologic processes that shape Earth and other planets over millions of years. The research tools are equally diverse, spanning high performance computing, sensor network development, satellite remote sensing, experimental petrology, geochemistry, and traditional boots-on-the-ground fieldwork.
The accelerating pace of data that describes these geological processes places data science squarely in the middle of an ongoing quantitative revolution. Now is an exciting time to get involved in the discipline.
In the earth sciences domain area, you will develop modern computational skills and apply them to an emphasis (spanning geophysics, geology, paleontology, and environmental geoscience) that suits your interests.
Earth Sciences Domain Requirements
Economics
Technological advances, including product scanners, online retail, internet search engines, and satellite/remote-sensing technology, have led to an explosion in the types and quantity of data available for economic analysis. Economists study economic data using a specialized statistical toolkit known as econometrics, and data science techniques are an increasingly important part of this toolkit.
Researchers use econometrics and data science to study a wide variety of topics, such as the effects of public policy, the evolution of industry, economic forecasting, and the forces driving inequality, economic development, and the environment.
In the economics domain of the data science major, you will learn how to apply modern economic data analysis to a wide range of economic phenomena and to draw conclusions useful for economic decision making.
Geography
Spatial data is integrated into our everyday lives and employed in a range of professions. We are all integrated into a complex web of movement, place, and discovery, whether we’re navigating across town or interpreting maps of election results.
University of Oregon geographers use spatial data technologies to focus on remote sensing of the changing environment, climate-change analysis, web-mapping, cartography and data visualization, spatial cognition, and spatial patterns in public health.
In the data science domain area of geography, you will be studying how spatial data can revolutionize business, nonprofit, and government worlds.
Linguistics
Usage-based linguistics studies is a dynamic, constantly changing system. Much of this work involves working with large collections of text or speech—referred to as “corpora.” Examples of readily available real-world corpora include Amazon product reviews and collections of Twitter messages.
Linguists use corpora to help identify patterns and structures in language, providing insights into how we both acquire and lose language skills, how language use varies across people and contexts, and how real-life speech and language evolve.
In the data science domain area of linguistics, you will learn methods to identify linguistic structures within corpora, gleaning new insights while using the best and latest practices in the field. These methods will allow you to answer basic science questions as well as questions that are of interest to marketing firms, political consulting groups, or other commercial enterprises. For example, you can use the knowledge you acquire in the linguistics domain to explore how the use of a word like “cool” has changed over time (a basic science question) or to identify linguistic strategies associated with leading positive product reviews for different product types (a marketing question).
Linguistics Domain Requirements
Marketing Analytics
Marketing analytics is the practice of measuring, managing, and analyzing marketing performance to maximize effectiveness and optimize return on investment. Data science applied to marketing data can help a business predict consumer behavior, improve decision-making, and gauge the success of marketing investments.
For example, machine learning and statistical techniques can be used to classify data and detect patterns that might predict a campaign’s success.
In the data science domain area of marketing analytics, you will learn how to see the future through the lens of both existing and new methods of predictive analytics.
Marketing Analytics Domain Requirements
Music Technology
Music technology encompasses the application of recent computer technologies to document, shape, and create audio and music. Because almost all sound that we experience today comes to the listener through speakers, headphones, and earbuds, applications of music technology in radio, television, film, and digital gaming are vast. Courses within music technology provide educational opportunities to develop knowledge in these broad areas.
Analyzing our data-rich world to understand it is essential. In the data science domain area of music technology, students will develop skills and knowledge related to the areas of data sonification and data mapping that increasingly are applied in professional work. Data sonification, the mapping data to sonic parameters to articulate features in data, extends to many other fields such as mathematics, ecology, cybersecurity, and astrophysics. Audio production techniques that are necessary to vivify data round out the music technology domain specialization.
The primary coursework emphasizes the development of knowledge about data mapping, the creation of original data sets, and audio production procedures essential to making the final sonified output valuable.
Music Technology Domain Requirements
Physics
The development of new sensors and experiments providing data at ever-increasing densities and rates has greatly aided research in physics, spanning the scales of galaxies and black holes down to bacteria and quarks and leptons. It wouldn’t be an exaggeration to say that physics is awash with data of all kinds.
Beyond efforts to find useful parameterizations of this data, physicists commonly develop predictive computational models to compare with observed data. Once refined, the models themselves provide us with important details about the underlying physical processes, be it the masses of colliding black holes, the interactions between different types of gut bacteria derived from analysis of images of zebrafish, or the fundamental energy of the Higgs boson.
Sociology
Sociology is the analytical study of human groups and societies. Consistent with the broad domain of the discipline, sociologists are omnivores with respect to methodological approaches used to study the social world. They employ a variety of analytical tools such as statistical modeling, network analysis, spatial analysis, demographic methods, data scraping, and computational analysis of social media and text data.
In the data science domain area of sociology, you will learn about how you can use these tools to uncover the hidden patterns of social life, answer questions related to policy, and better understand our social world.