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
Foundations in Mathematics and Computing
CS 210
& CS 211
& CS 212		Computer Science I
and Computer Science II
and Computer Science III		12
MATH 251
& MATH 252		Calculus I
and Calculus II		8
MATH 341
& MATH 342		Elementary Linear Algebra
and Elementary Linear Algebra		8
Probability
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
CS 314
Computer Organization
CS 322
Introduction to Software Engineering
CS 330
C/C++ and Unix
CS 333
Applied Cryptography
CS 415
Operating Systems
CS 432
Introduction to Networks
MATH 253
Calculus III
MATH 307
Introduction to Proof
MATH 461
Introduction to Mathematical Methods of Statistics I
MATH 462
Introduction to Mathematical Methods of Statistics II
MATH 463
Mathematical Methods of Regression Analysis and Analysis of Variance
MATH 458
Introduction to Mathematical Cryptography
Domain Emphasis23-28
Total Credits87-92

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
Required
BA 240Spreadsheet Analysis and Visualization4
ACTG 350Intermediate Accounting I4
Take two out the following three8
ACTG 410
Experimental Course: [Topic] (Accounting Data and Analytics Capstone)
ACTG 480
Accounting Data Analytics I
OBA 455
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
BI 211
General Biology I: Cells
BI 212
General Biology II: Organisms
BI 213
General Biology III: Ecology and Evolution
Select four of the following16
BI 320
Molecular Genetics
BI 360
Neurobiology
BI 370
Ecology
BI 399
Special Studies: [Topic] (Computational Genomics)
BI 399
Special Studies: [Topic] (Modeling in Biology: Deterministic Models)
BI 410
Experimental Course: [Topic] (Data Management and Visualization)
BI 410
Experimental Course: [Topic] (Modeling in Biology: Stochastic Models)
BI 410
Experimental Course: [Topic] (Neural Data Analysis)
BI 471
Population Ecology
DSCI 411
Capstone Project
Total Credits26

 

Data Science Domain - Cultural Analytics

Core Courses - choose 312
COLT 301
Approaches to Comparative Literature
COLT 305
Cultural Studies
HUM 300
Themes in the Humanities
ENG 303
Foundations of the English Major: Text
ENVS 203
Introduction to Environmental Studies: Humanities
FLR 250
Introduction to Folklore
Upper division - required4
LIB/DSCI 350M
Electives - choose 312
ENG 250
Literature and Digital Culture
ENG 470
Technologies and Texts Capstone
ENG/CINE 486M
New Media and Digital Culture: [Topic]
FLR 495
Folklore Fieldwork
J 429
Media Technologies and Society: [Topic]
PHIL 123
Internet, Society, and Philosophy
PHIL 423
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
ERTH 353
Geologic Hazards
ERTH 441
Hillslope Geomorphology
ERTH 453
Tectonics
ERTH 454
Fluid Dynamics
ERTH 455
Mechanical Earth
ERTH 456
Signal Processing
ERTH 467
Fault Mechanics
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
EC 422
Economic Forecasting
EC 428
Behavioral and Experimental Economics
EC 434
Environmental Economics
EC 443
Health Economics
EC 451
Issues in Labor Economics
EC 460
Theories of Industrial Organization
EC 482
Economics of Globalization
EC 490
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
GEOG 482
GIScience II
GEOG 485
Remote Sensing I
GEOG 486
Remote Sensing II
GEOG 490
GIScience: [Topic]
GEOG 491
Advanced Geographic Information Systems
GEOG 493
Advanced Cartography
GEOG 498
Geospatial Project Design
DSCI 411
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
LING 301
Introduction to Linguistics Analysis
LING 302
Introduction to Linguistic Behavior
Electives:16
LING 435
Morphology and Syntax
LING 451
Functional Syntax I
LING 493
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
Required
BA 240Spreadsheet Analysis and Visualization4
BA 317Marketing: Creating Value for Customers4
MKTG 390Marketing Research4
Pick one of the following:4
MKTG 415
Marketing Analytics
OBA 455
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
MUS 360
Hip-Hop History, Culture, Aesthetics (Elective)
MUS 445
Electronic Composition
MUS 476
Digital Audio Workstation Techniques I
MUS 483
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
PHYS 432
Digital Electronics
PHYS 481
Design of Experiments
PHYS 491
Research Project I
Total Credits23-25

Data Science Domain - Sociology

Core Courses:
SOC 204Introduction to Sociology4
or SOC 207 Social Inequality
SOC 310Social Theory4
SOC 311Research Methods4
SOC 412Sociological Research Methods4
SOC 413Sociological Research Methods4
Choose two from the following:8
SOC 370
Urban Sociology
SOC 380
Deviance, Social Control, and Crime
SOC 416
Issues in Environmental Sociology [Topic]
SOC 442
Issues in Urban Sociology: [Topic]
SOC 445
Sociology of Race and Ethnicity: [Topic]
SOC 451
Social Stratification
SOC 465
Political Sociology
SOC 467
Economic Sociology
Total Credits28