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: 

Data Science Core Courses:24
Foundations of Data Science I
Foundations of Data Science 2
Principles and Techniques of Data Science
Probability and Statistics for Data Science
Machine Learning for Data Science
Experimental Course: [Topic] (Data Science Capstone Project) 1
Mathematics Courses:16
Calculus I
Calculus II
Elementary Linear Algebra
Elementary Linear Algebra
Computer Science Courses:12
Computer Science I
Computer Science II
Computer Science III
Ethics Course:4
Technology Ethics: [Topic]
Computational and Inferential Depth:
Select three courses from the list below:12
Intermediate Data Structures
Computer Organization
Intermediate Algorithms
Introduction to Software Engineering
C/C++ and Unix
Experimental Course: [Topic] (Visualization)
Operating Systems
Software Methodology I
Principles of Programming Languages
Introduction to Networks
Database Processing
Introduction to Artificial Intelligence

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
Electives:12
Managing Business Information
Intermediate Accounting I
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
Electives:12
Molecular Genetics
Neurobiology
Ecology
Population Ecology
Techniques in Computational Neuroscience


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
Electives: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 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 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
Electives:
Managing Business Information
Marketing: Creating Value for Customers
Marketing Research
Experimental Course: [Topic] (Marketing Analytics)
Experimental Course: [Topic]