Data Science
https://datascience.uoregon.edu/
Bill Cresko
Executive Director for the Data Science Initiative
Professor of Biology
541-346-4779
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 “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.
Undergraduate Degree in Data Science
The data science curriculum combines general principles with domain-specific application. The curriculum is sub-divided into the following categories with the corresponding requirements:
Bachelor's Degree in Data Science
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: | 16 | |
| Foundations of Data Science I | ||
| Foundations of Data Science II | ||
| Principles and Techniques of Data Science | ||
Data Science Capstone Project | ||
| Foundations in Mathematics and Computing | ||
| Computer Science I and Computer Science II and Computer Science III | ||
| Calculus I and Calculus II | ||
| Elementary Linear Algebra and Elementary Linear Algebra | ||
| Probability and Statistics for Data Science | ||
| Machine Learning for Data Science | ||
| Experimental Course: [Topic] (Data Science Capstone Project) 1 | ||
| Mathematics Courses: | 16 | |
| Ethics Course: | 4 | |
| Technology Ethics: [Topic] | ||
| 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 | ||
| Modeling, Learning and Decision Making | ||
| Machine Learning for Data Science | ||
| Probability | ||
| Probability and Statistics for Data Science | ||
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 previous section (tracks/concentrations) for a detailed list of courses that satisfy each available domain emphasis.
| 1 | An additional course from the domain specialization list may be taken in place of the capstone project. |
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.
| Code | Title | Credits |
|---|---|---|
| Core Courses: | 8-12 | |
| Introduction to Business | ||
| Accounting: Language of Business Decisions | ||
| Introduction to Economic Analysis: Microeconomics | ||
| Required | ||
| Spreadsheet Analysis and Visualization | ||
| Intermediate Accounting I | ||
| Take two out the following three | ||
| Experimental Course: [Topic] (Accounting Data and Analytics) | ||
| Experimental Course: [Topic] (Accounting Data and Analytics Capstone) | ||
| Experimental Course: [Topic] (Predictive Analytics) | ||
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: | 8-12 | |
| General Biology I: Cells | ||
| General Biology II: Organisms | ||
| General Biology III: Populations | ||
| Select four of the following | 12 | |
| Molecular Genetics | ||
| Neurobiology | ||
| Ecology | ||
| 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 | ||
| Techniques in Computational Neuroscience | ||
| Data Science Capstone Course | ||
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: | ||
| Geologic Hazards | ||
| ERTH 415 | Field Geophysics | 4 |
| ERTH 438 | Geobiology | 4 |
| ERTH 441 | Hillslope Geomorphology | 4 |
| ERTH 453 | Tectonics | 3 |
| ERTH 454 | Fluid Dynamics | 4 |
| ERTH 455 | Mechanical Earth | 4 |
| ERTH 467 | Fault Mechanics | 4 |
| Data Science Capstone Course | ||
Data Science Domain - Economics
| Code | Title | Credits |
|---|---|---|
| Core Courses: | ||
| EC 201 | Introduction to Economic Analysis: Microeconomics | 4 |
| EC 311 | Intermediate Microeconomic Theory | 4 |
| EC 320 | Introduction to Econometrics | 4 |
| EC 421 | Introduction to Econometrics | 4 |
| Choose three from the following: | ||
| 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 | ||
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: | 8-12 | |
| Our Digital Earth | ||
| The World and Big Data | ||
| GIScience I | ||
| Select four of the following | 12 | |
| GIScience II | ||
| Remote Sensing I | ||
| Remote Sensing II | ||
| GIScience: [Topic] | ||
| Advanced Geographic Information Systems | ||
| Advanced Cartography | ||
| Location-Aware Systems | ||
| Geospatial Project Design | ||
Data Science Capstone Course | ||
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: | 8 | |
| Introduction to Linguistics Analysis | ||
| Introduction to Linguistic Behavior | ||
| Electives: | ||
| Morphology and Syntax | ||
| Functional Syntax I | ||
| Functional Syntax II | ||
| Corpus Linguistics | ||
| Experimental Course: [Topic] (Natural Language Processing) | ||
Data Science Capstone Course | ||
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: | 8-12 | |
| Introduction to Business | ||
| Accounting: Language of Business Decisions | ||
| Introduction to Economic Analysis: Microeconomics | ||
| Required | ||
| Spreadsheet Analysis and Visualization | ||
| Marketing: Creating Value for Customers | ||
| Marketing Research | ||
| Pick one of the following: | ||
| Marketing Analytics | ||
| Experimental Course: [Topic] | ||
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: | ||
| Digital Electronics | ||
| Design of Experiments | ||
| Research Project I | ||
Data Science Capstone Project | ||
Data Science Domain - Sociology
| Code | Title | Credits |
|---|---|---|
| Core Courses: | ||
| SOC 204 | Introduction to Sociology | 4 |
| 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: | ||
| Urban Sociology | ||
| Introduction: Deviance, 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 | ||
| Advanced Sociological Methods: [Topic] | ||
Courses
DSCI 101. Foundations of Data Science I. 4 Credits.
This course utilizes 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 and privacy concerns are explored. Sequence with DSCI 102.
DSCI 102. Foundations of Data Science II. 4 Credits.
This course expands upon critical concepts and skills introduced in DSCI 101. Topics include the normal distribution, confidence intervals, regression, and classifiers. Sequence with DSCI 101.
Prereq: DSCI 101, MATH 101 (or equivalent math placement score).
DSCI 196. Field Studies: [Topic]. 1-12 Credits.
Repeatable.
DSCI 198. Workshop: [Topic]. 1-12 Credits.
Repeatable.
DSCI 199. Special Studies: [Topic]. 1-5 Credits.
Repeatable.
DSCI 299. Special Studies: [Topic]. 1-5 Credits.
Repeatable.
DSCI 311. Principles and Techniques of Data Science. 4 Credits.
Intermediate and advanced techniques in data science. Topics include managing data using software programs, data cleaning, handling text, dimensionality, principle component analysis, regression, classification and inference.
Prereq: DSCI 102, CIS 211, MATH 342.
DSCI 345M. Probability and Statistics for Data Science. 4 Credits.
Introduction to probability and statistics, with an emphasis upon topics relevant for data science. Students cannot get credit for both MATH 343 and DSCI 345M/MATH 345M.
Prereq: MATH 342, CIS 211.
DSCI 372M. Machine Learning for Data Science. 4 Credits.
Introduction to Machine Learning, with an emphasis on topics relevant for data science. Multilisted with CIS 372M.
Prereq: CIS 212, DSCI 345M, MATH 342.
DSCI 399. Special Studies: [Topic]. 1-5 Credits.
Repeatable.
DSCI 401. Research: [Topic]. 1-12 Credits.
Repeatable.
DSCI 403. Thesis. 1-12 Credits.
Repeatable.
DSCI 404. Internship: [Topic]. 1-12 Credits.
Repeatable.
DSCI 405. Reading and Conference: [Topic]. 1-5 Credits.
Repeatable.
DSCI 406. Field Studies: [Topic]. 1-12 Credits.
Repeatable.
DSCI 407. Seminar: [Topic]. 1-5 Credits.
Repeatable.
DSCI 409. Practicum: [Topic]. 1-5 Credits.
Repeatable.
DSCI 410. Experimental Course: [Topic]. 1-5 Credits.
Repeatable.