Data Science (BA/BS)

The volume, rate, and importance of data is growing exponentially, with more data being created in the past two years than in all history combined. At the University of Oregon we’re bringing our strengths as a liberal arts university to the field – to not only ask what can we do with data – but what should we do. 

As a data science major at the UO, you will learn key computational, statistical, and inferential data science skills side-by-side with applied knowledge in one of 10 different areas: accounting analytics, biology, earth science, economics, geography, linguistics, marketing analytics, music technology, physics, and sociology. Our unique interdisciplinary program teaches you how to extract knowledge and insight from data, empowering you to make decisions earlier, faster, better. When you graduate, your understanding of applied data science techniques, framed within the liberal arts, will help launch you directly into the workforce. 

Program Learning Outcomes

Upon successful completion of this program, students will be able to:

  • Demonstrate the ability to assess data set quality, identifying and rectifying potential errors in such a way so as to lead to statistically meaningful derived information.
  • Visualize complex data sets using descriptive statistics and graphs.
  • Demonstrate understanding of basic regression, optimization, prediction, simulation, and visualization methods.
  • Use critical thinking skills to translate substantive questions into well-defined statistical or probability problems and choose the appropriate graphical or numerical descriptive and/or inferential statistical techniques for a given problem, leading to actionable, valid, and meaningful conclusions.
  • Develop successful strategies for formulating and testing hypotheses about data.
  • Demonstrate an understanding of ethical, legal, societal, and economic concerns.
  • Apply fundamental concepts of data science (data management, statistical prediction and inference, experimental design, etc.) to applications specific to the chosen specialization domain.

Data Science Major Requirements

Courses used to fulfill the major requirements must be taken for a letter grade and passed with a grade of C- or better.

The data science curriculum combines general principles with domain-specific application.  The curriculum is sub-divided into the following categories with the corresponding requirements: 

Data Science Core Courses
DSCI 101Foundations of Data Science I4
DSCI 102Foundations of Data Science II4
DSCI 311Principles and Techniques of Data Science4
Data Science Capstone Project4
Foundations in Mathematics and Computing
CS 210
CS 211
CS 212
Computer Science I
and Computer Science II
and Computer Science III
MATH 251
MATH 252
Calculus I
and Calculus II
MATH 341
MATH 342
Elementary Linear Algebra
and Elementary Linear Algebra
DSCI/MATH 345MProbability and Statistics for Data Science4
Modeling, Learning and Decision Making
DSCI/CS 372MMachine Learning for Data Science4
Ethics Course
PHIL 223Data Ethics4
Computational and Inferential Depth
Select three courses from the list below:12
Computer Organization
Introduction to Software Engineering
C/C++ and Unix
Applied Cryptography
Operating Systems
Introduction to Networks
Calculus III
Introduction to Proof
Introduction to Mathematical Methods of Statistics I
Introduction to Mathematical Methods of Statistics II
Mathematical Methods of Regression Analysis and Analysis of Variance
Introduction to Mathematical Cryptography
Domain Emphasis22-28
Total Credits90-96

Domain Emphasis

The domain emphasis consists of completing 2-3 courses (8-12 credits) in the domain core, followed by a minimum of 4 courses (16 credits) of domain specialization. For each domain emphasis, a curated list of courses has been developed for both the core and specialization component.  Please see the Concentrations tab for a detailed list of courses that satisfy each available domain emphasis. 

Residency Requirements

At least 34 credits of coursework applied to the major must be taken at the University of Oregon. These credits must include enrollment in DSCI 311, DSCI 345, and DSCI 372.

An essential aspect of the degree in data science is that data science majors develop critical competencies in a domain emphasis of their choosing. The domain emphasis consists of completing 2-3 courses (8-12 credits) in the domain core, followed by a minimum of 3 courses (12 credits) of domain specialization. For each domain emphasis, a curated list of courses has been developed for both the core and specialization component.

Currently, domain emphases have been established for biology, geography, accounting analytics, marketing analytics, and linguistics. The curated list of domain core and domain specialization courses for each domain is outlined below.  

Data Science Domain - 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.

Core Courses:
BA 101ZIntroduction to Business4
BA 215Accounting: Language of Business Decisions4
EC 201Introduction to Economic Analysis: Microeconomics4
BA 240Spreadsheet Analysis and Visualization4
ACTG 350Intermediate Accounting I4
Take two out the following three8
Experimental Course: [Topic] (Accounting Data and Analytics Capstone)
Accounting Data Analytics I
Data Driven Predictive Modeling
Total Credits28

Data Science Domain - 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, microbial ecology, and the assembly and characterization of new genomes.

Core Courses:10
General Biology I: Cells
General Biology II: Organisms
General Biology III: Ecology and Evolution
Select four of the following16
Molecular Genetics
Special Studies: [Topic] (Computational Genomics)
Special Studies: [Topic] (Modeling in Biology: Deterministic Models)
Experimental Course: [Topic] (Data Management and Visualization)
Experimental Course: [Topic] (Modeling in Biology: Stochastic Models)
Experimental Course: [Topic] (Neural Data Analysis)
Population Ecology
Capstone Project
Total Credits26


Data Science Domain - Cultural Analytics

Core Courses - choose 312
Approaches to Comparative Literature
Cultural Studies
Themes in the Humanities
Foundations of the English Major: Text
Introduction to Environmental Studies: Humanities
Introduction to Folklore
Upper division - required4
Electives - choose 312
Literature and Digital Culture
Technologies and Texts Capstone
New Media and Digital Culture: [Topic]
Folklore Fieldwork
Media Technologies and Society: [Topic]
Internet, Society, and Philosophy
Technology Ethics: [Topic]
Total credits28

Data Science Domain - Earth Sciences 

Core Courses:
ERTH 202Earth's Surface and Environment4
PHYS 201General Physics4
or PHYS 251 Foundations of Physics I
ERTH 315Earth Physics4
Choose four of the following:15-16
Geologic Hazards
Field Geophysics
Hillslope Geomorphology
Fluid Dynamics
Mechanical Earth
Fault Mechanics
Data Science Capstone Course
Total Credits27-28

Data Science Domain - Economics

Core Courses:
EC 201Introduction to Economic Analysis: Microeconomics4
EC 311Intermediate Microeconomic Theory4
EC 320Introduction to Econometrics I4
EC 421Introduction to Econometrics II4
Choose three from the following:12
Economic Forecasting
Behavioral and Experimental Economics
Environmental Economics
Health Economics
Issues in Labor Economics
Theories of Industrial Organization
Economics of Globalization
Economic Growth and Development
Total Credits28

Data Science Domain - 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.

UO 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.

Core Courses:
GEOG 181Our Digital Earth4
GEOG 281The World and Big Data4
GEOG 481GIScience I4
Select four of the following16
GIScience II
Remote Sensing I
Remote Sensing II
GIScience: [Topic]
Advanced Geographic Information Systems
Advanced Cartography
Geospatial Project Design
Capstone Project
Total Credits28

Data Science Domain - Linguistics

Usage-based linguistics studies language as 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. So, 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).

Core Courses:8
Introduction to Linguistics Analysis
Introduction to Linguistic Behavior
Morphology and Syntax
Functional Syntax I
Corpus Linguistics
Data Science Capstone Course
Total Credits24

Data Science Domain - 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.

Core Courses:
BA 101ZIntroduction to Business4
BA 215Accounting: Language of Business Decisions4
EC 201Introduction to Economic Analysis: Microeconomics4
BA 240Spreadsheet Analysis and Visualization4
BA 317Marketing: Creating Value for Customers4
MKTG 390Marketing Research4
Pick one of the following:4
Marketing Analytics
Data Driven Predictive Modeling
Total Credits28

Data Science Domain -  Music Technology

Core Courses
MUS 227Elements of Electronic Music4
MUS 447Digital Audio and Sound Design (Core)4
MUS 470History of Electroacoustic Music3
Upper division - required
MUS 448Interactive Media Performance3
MUS 479Data Sonification4
Upper division - choose 26-8
Hip-Hop History, Culture, Aesthetics (Elective)
Electronic Composition
Digital Audio Workstation Techniques I
Audio Effects Theory and Design
Total Credits24-26

Data Science Domain - Physics

Core Courses:
PHYS 251Foundations of Physics I4
PHYS 253Foundations of Physics I4
PHYS 290Foundations of Physics Laboratory1
PHYS 391Physics Experimentation Data Analysis Laboratory4
Choose three of the following:11-12
Digital Electronics
Design of Experiments
Research Project I
Data Science Capstone Project
Total Credits23-25

Data Science Domain - Sociology

Core Courses:
SOC 204Introduction to Sociology4
SOC 310Social Theory4
SOC 311Research Methods4
SOC 412Sociological Research Methods4
SOC 413Sociological Research Methods4
Choose two from the following:8
Urban Sociology
Deviance, Social Control, and Crime
Issues in Environmental Sociology [Topic]
Issues in Urban Sociology: [Topic]
Sociology of Race and Ethnicity: [Topic]
Social Stratification
Political Sociology
Economic Sociology
Total Credits28