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:
| Code | Title | Credits |
|---|---|---|
| Data Science Core Courses | ||
| DSCI 101 | Foundations of Data Science I | 4 |
| DSCI 102 | Foundations of Data Science II | 4 |
| DSCI 311 | Principles and Techniques of Data Science | 4 |
| Foundations in Mathematics and Computing | ||
| CS 210 & CS 211 & CS 212 | Computer Science I and Computer Science II and Computer Science III | 12 |
| MATH 251Z & MATH 252Z | Differential Calculus and Integral Calculus | 8 |
| MATH 341 & MATH 342 | Elementary Linear Algebra and Elementary Linear Algebra | 8 |
| Probability | ||
| DSCI/MATH 345M | Probability and Statistics for Data Science | 4 |
| Modeling, Learning and Decision Making | ||
| DSCI/CS 372M | Machine Learning for Data Science | 4 |
| Ethics Course | ||
| PHIL 223 | Data Ethics | 4 |
| 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: Sequences and Series | ||
| 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 Emphasis | 23-31 | |
| Total Credits | 87-95 | |
Domain Emphasis
The domain emphasis consists of completing 2-3 courses (8-15 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 link 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.
- Accounting Analytics
- Biology
- Cultural Analytics
- Earth Sciences
- Economics
- Geography
- Linguistics
- Marketing Analytics
- Music Technology
- Physics
- Sociology
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.
| Code | Title | Credits |
|---|---|---|
| Core Courses: | ||
| BA 101Z | Introduction to Business | 4 |
| BA 215 | Accounting: Language of Business Decisions | 4 |
| or BA 211Z | Principles of Financial Accounting | |
| EC 201Z | Principles of Microeconomics | 4 |
| Required | ||
| BA 169Z | Data Analysis Using Microsoft Excel | 4 |
| ACTG 350 | Intermediate Accounting I | 4 |
| Take two out the following three | 8 | |
| Experimental Course: [Topic] (Accounting Data and Analytics Capstone) | ||
| Accounting Data Analytics I | ||
| Data Driven Predictive Modeling | ||
| Total Credits | 28 | |
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.
| Code | Title | Credits |
|---|---|---|
| Core Courses: | ||
| BI 221Z | Principles of Biology I: Cells | 5 |
| BI 222Z | Principles of Biology II: Organisms | 5 |
| BI 223Z | Principles of Biology III: Ecology and Evolution | 5 |
| or BI 214 | General Biology IV: Biochemistry and Genetics | |
| Electives - Select four of the following: | 16 | |
| Molecular Genetics | ||
| Neurobiology | ||
| Ecology | ||
| Analysis of Neural Data | ||
| Population Ecology | ||
| Deterministic Dynamical Modeling in Biology | ||
| Capstone Project | ||
| Total Credits | 31 | |
Data Science Domain - Cultural Analytics
| Code | Title | Credits |
|---|---|---|
| Core Courses - choose 3 | 12 | |
| 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 - required | 4 | |
| Humanities Research Data Management | ||
or LIB 350M | Humanities Research Data Management | |
| Electives - choose 3 | 12 | |
| 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 credits | 28 | |
Data Science Domain - Earth Sciences
| Code | Title | Credits |
|---|---|---|
| Core Courses: | ||
| ERTH 202 | Earth's Surface and Environment | 4 |
| PHYS 201 | General Physics | 4 |
| or PHYS 251 | Foundations of Physics I | |
| ERTH 315 | Earth Physics | 4 |
| Choose four of the following: | 15-16 | |
| Geologic Hazards | ||
| Hillslope Geomorphology | ||
| Tectonics | ||
| Fluid Dynamics | ||
| Mechanical Earth | ||
| Signal Processing | ||
| Fault Mechanics | ||
| Total Credits | 27-28 | |
Data Science Domain - Economics
| Code | Title | Credits |
|---|---|---|
| Core Courses: | ||
| EC 201Z | Principles of Microeconomics | 4 |
| EC 311 | Intermediate Microeconomic Theory | 4 |
| EC 320 | Introduction to Econometrics I | 4 |
| EC 421 | Introduction to Econometrics II | 4 |
| 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 Credits | 28 | |
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.
| Code | Title | Credits |
|---|---|---|
| Core Courses: | ||
| GEOG 181 | Our Digital Earth | 4 |
| GEOG 481 | GIScience I | 4 |
| GEOG 482 | GIScience II | 4 |
| Select four of the following | 16 | |
| Remote Sensing I | ||
| Remote Sensing II | ||
| GIScience: [Topic] | ||
| Advanced Geographic Information Systems | ||
| Web Mapping | ||
| Advanced Cartography and Geo-Visualization | ||
| Geographic Data Analysis | ||
| Capstone Project | ||
| Total Credits | 28 | |
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).
| Code | Title | Credits |
|---|---|---|
| Core Courses: | ||
| LING 301 | Introduction to Linguistics Analysis | 4 |
| Select two of the following: | 8 | |
| Introduction to Linguistic Behavior | ||
| Phonetics and Phonology | ||
| Morphosyntax | ||
| Electives - Select four of the following: | 16 | |
| Phonetics | ||
| Semantics | ||
| Research Methods for Applied Linguistics | ||
| Statistical Methods in Linguistics | ||
| Advanced Morphology | ||
| Advanced Syntax | ||
| Corpus Linguistics | ||
Data Science Capstone Course | ||
| Total Credits | 28 | |
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.
| Code | Title | Credits |
|---|---|---|
| Core Courses: | ||
| BA 101Z | Introduction to Business | 4 |
| BA 215 | Accounting: Language of Business Decisions | 4 |
| EC 201Z | Principles of Microeconomics | 4 |
| Required | ||
| BA 169Z | Data Analysis Using Microsoft Excel | 4 |
| BA 317 | Marketing: Creating Value for Customers | 4 |
| MKTG 390 | Marketing Research | 4 |
| Pick one of the following: | 4 | |
| Marketing Analytics | ||
| Data Driven Predictive Modeling | ||
| Sports Analytics | ||
| Total Credits | 28 | |
Data Science Domain - Music Technology
| Code | Title | Credits |
|---|---|---|
| Core Courses | ||
| MUS 227 | Elements of Electronic Music | 4 |
| MUS 447 | Digital Audio and Sound Design (Core) | 4 |
| MUS 470 | History of Electroacoustic Music | 3 |
| Upper division - required | ||
| MUS 448 | Interactive Media Performance | 3 |
| MUS 479 | Data Sonification | 4 |
| Upper division - choose 2 | 6-8 | |
| Hip-Hop History, Culture, Aesthetics (Elective) | ||
| Electronic Composition | ||
| Digital Audio Workstation Techniques I | ||
| Audio Effects Theory and Design | ||
| Total Credits | 24-26 | |
Data Science Domain - Physics
| Code | Title | Credits |
|---|---|---|
| Core Courses: | ||
| PHYS 251 | Foundations of Physics I | 4 |
| PHYS 253 | Foundations of Physics I | 4 |
| PHYS 290 | Foundations of Physics Laboratory | 1 |
| PHYS 391 | Physics Experimentation Data Analysis Laboratory | 4 |
| Choose three of the following: | 10-12 | |
| Digital Electronics | ||
| Design of Experiments | ||
| Research Project I | ||
| Total Credits | 23-25 | |
Data Science Domain - Sociology
| Code | Title | Credits |
|---|---|---|
| Core Courses: | ||
| SOC 204Z | Introduction to Sociology | 4 |
| or SOC 207 | Social Inequality | |
| SOC 310 | Social Theory | 4 |
| SOC 311 | Research Methods | 4 |
| SOC 412 | Sociological Research Methods | 4 |
| SOC 413 | Sociological Research Methods | 4 |
| 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 Credits | 28 | |
Four-Year Degree Plan
The degree plan shown is only a sample of how students may complete their degrees in four years. There are alternative ways. Students should consult their advisor to determine the best path for them.
| First Year | |||
|---|---|---|---|
| Fall | Milestones | Credits | |
| MATH 112Z | Precalculus II: Trigonometry | 4 | |
| CS 122 | Introduction to Programming and Problem Solving | 4 | |
| WR 121Z | Composition I | 4 | |
| Core-education course in social science | 4 | ||
| Credits | 16 | ||
| Winter | |||
| CS 210 | Computer Science I | 4 | |
| DSCI 101 | Foundations of Data Science I | 4 | |
| WR 122Z | Composition II | 4 | |
| Core-education course in arts and letters | 4 | ||
| Credits | 16 | ||
| Spring | |||
| CS 211 | Computer Science II | 4 | |
| DSCI 102 | Foundations of Data Science II | 4 | |
| MATH 251Z | Differential Calculus | 4 | |
| Core-education course in social science | 4 | ||
| Credits | 16 | ||
| Second Year | |||
| Fall | |||
| MATH 252Z | Integral Calculus | 4 | |
| CS 212 | Computer Science III | 4 | |
| Domain course | 4 | ||
| Core-education course in arts and letters | 4 | ||
| Credits | 16 | ||
| Winter | |||
| MATH 341 | Elementary Linear Algebra | 4 | |
| Domain course | 4 | ||
| Core-education course in arts and letters | 4 | ||
| Core-education course in social science | 4 | ||
| Credits | 16 | ||
| Spring | |||
| MATH 342 | Elementary Linear Algebra | 4 | |
| Domain course | 4 | ||
| Cultural literacy course | 4 | ||
| Core-education course in arts and letters | 4 | ||
| Credits | 16 | ||
| Third Year | |||
| Fall | |||
| DSCI 311 | Principles and Techniques of Data Science | 4 | |
| DSCI 345M | Probability and Statistics for Data Science | 4 | |
| Cultural literacy course | 4 | ||
| Core-education course in social science | 4 | ||
| Credits | 16 | ||
| Winter | |||
| DSCI 372M or CS 372M |
Machine Learning for Data Science or Machine Learning for Data Science |
4 | |
| PHIL 423 | Technology Ethics: [Topic] | 4 | |
| Electives | 8 | ||
| Credits | 16 | ||
| Spring | |||
| Computational and inferential depth course | 4 | ||
| Electives | 12 | ||
| Credits | 16 | ||
| Fourth Year | |||
| Fall | |||
| Domain course | 4 | ||
| Computational and inferential depth course | 4 | ||
| Electives | 8 | ||
| Credits | 16 | ||
| Winter | |||
| Domain course | 4 | ||
| Computational and inferential depth course | 4 | ||
| Electives | 8 | ||
| Credits | 16 | ||
| Spring | |||
| Domain course | 4 | ||
| DSCI 411 | Capstone Project (or one additional domain specialization course) | 4 | |
| Electives | 8 | ||
| Credits | 16 | ||
| Total Credits | 192 | ||