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: 

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. 

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: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.

Core Courses:8-12
General Biology I: Cells
General Biology II: Organisms
General Biology III: Populations
Select four of the following12
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 

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:
Geologic Hazards
ERTH 415Field Geophysics4
ERTH 438Geobiology4
ERTH 441Hillslope Geomorphology4
ERTH 453Tectonics3
ERTH 454Fluid Dynamics4
ERTH 455Mechanical Earth4
ERTH 467Fault Mechanics4
Data Science Capstone Course

Data Science Domain - Economics

Core Courses:
EC 201Introduction to Economic Analysis: Microeconomics4
EC 311Intermediate Microeconomic Theory4
EC 320Introduction to Econometrics4
EC 421Introduction to Econometrics4
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.

Core Courses:8-12
Our Digital Earth
The World and Big Data
GIScience I
Select four of the following12
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).

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.

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

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:
Digital Electronics
Design of Experiments
Research Project I
Data Science Capstone Project

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:
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

Course usage information

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.

Course usage information

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).

Course usage information

DSCI 196. Field Studies: [Topic]. 1-12 Credits.

Repeatable.

Course usage information

DSCI 198. Workshop: [Topic]. 1-12 Credits.

Repeatable.

Course usage information

DSCI 199. Special Studies: [Topic]. 1-5 Credits.

Repeatable.

Course usage information

DSCI 299. Special Studies: [Topic]. 1-5 Credits.

Repeatable.

Course usage information

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.

Course usage information

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.

Course usage information

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.

Course usage information

DSCI 399. Special Studies: [Topic]. 1-5 Credits.

Repeatable.

Course usage information

DSCI 401. Research: [Topic]. 1-12 Credits.

Repeatable.

Course usage information

DSCI 403. Thesis. 1-12 Credits.

Repeatable.

Course usage information

DSCI 404. Internship: [Topic]. 1-12 Credits.

Repeatable.

Course usage information

DSCI 405. Reading and Conference: [Topic]. 1-5 Credits.

Repeatable.

Course usage information

DSCI 406. Field Studies: [Topic]. 1-12 Credits.

Repeatable.

Course usage information

DSCI 407. Seminar: [Topic]. 1-5 Credits.

Repeatable.

Course usage information

DSCI 409. Practicum: [Topic]. 1-5 Credits.

Repeatable.

Course usage information

DSCI 410. Experimental Course: [Topic]. 1-5 Credits.

Repeatable.