Data Science Undergraduate Major (BS, HBS)
This program is available at the following location:
- Corvallis
Options available:
- Advanced Data Science
- Economics
- Environmental Economics and Policy
- Life Science
- Psychological Science
The Bachelor of Science in Data Science provides a comprehensive curriculum designed to prepare students for careers in the rapidly evolving field of data science. Students will develop a strong foundation in computational and analytical tools, data science methodologies, and their practical applications, while also gaining a comprehensive understanding of the ethical implications involved in data systems and solutions. The major fosters critical thinking, problem-solving, and ethical decision-making, equipping graduates to thrive in a data-centric world.
Major Code: A051
Upon successful completion of the program, students will meet the following learning outcomes:
- Analyze and interpret data using statistical methods, computing skills, and other data science tools and technologies.
- Assess real-world data problems and propose analytical methods to achieve effective and ethical solutions.
- Communicate data findings clearly and effectively to diverse audiences, tailoring messages to ensure understanding and impact.
- Collaborate effectively within diverse teams, valuing different perspectives and fostering civil discourse to enhance problem-solving and project outcomes.
| Code | Title | Credits |
|---|---|---|
| Required Core | ||
| MTH 231 | ELEMENTS OF DISCRETE MATHEMATICS | 4 |
| MTH 251Z | +*DIFFERENTIAL CALCULUS | 4 |
| MTH 252Z | INTEGRAL CALCULUS | 4 |
| MTH 267 | LINEAR ALGEBRA FOR DATA SCIENCE | 3 |
| MTH 301 | +INTEGRAL HISTORIES AND SOCIAL ISSUES IN MATHEMATICS | 3 |
| ST 314 | INTRODUCTION TO STATISTICS FOR ENGINEERS | 3-4 |
| or ST 351 | INTRODUCTION TO STATISTICAL METHODS | |
| ST 411 | METHODS OF DATA ANALYSIS | 4 |
| ST 412 | METHODS OF DATA ANALYSIS | 4 |
| ST 421 | INTRODUCTION TO MATHEMATICAL STATISTICS | 4 |
| ST 422 | INTRODUCTION TO MATHEMATICAL STATISTICS | 4 |
| ST 437 | DATA VISUALIZATION | 3 |
| CS 162 | INTRODUCTION TO COMPUTER SCIENCE II | 4 |
| CS 261 | DATA STRUCTURES | 4 |
| CS 340 | INTRODUCTION TO DATABASES | 4 |
| WR 227Z | +*TECHNICAL WRITING | 3-4 |
| or WR 362 | +*SCIENCE WRITING | |
| DS 101 | +EXPLORING CAREERS IN DATA SCIENCE | 1 |
| DS 201 | +INTRODUCTION TO DATA SCIENCE | 4 |
| DS 231 | PYTHON PROGRAMMING FOR DATA SCIENCE | 4 |
| DS 431 | STATISTICAL LEARNING FOR DATA SCIENCE | 3 |
| DS 495 | +^CAPSTONE AND CAREER: DATA SCIENCE | 3 |
| Electives 1 | ||
| Select two data science courses from the following or complete an option: | 6-7 | |
| METHODS OF DATA ANALYSIS FOR COMPLEX AND NETWORK DATA | ||
| BAYESIAN MODELS FOR DATA SCIENCE | ||
| CAUSAL INFERENCE FOR EXPERIMENTAL AND OBSERVATIONAL DATA | ||
| Select one statistics course from the following or complete an option: | 3-4 | |
| METHODS OF DATA ANALYSIS | ||
| DESIGN AND ANALYSIS OF PLANNED EXPERIMENTS | ||
| SAMPLING METHODS | ||
| R PROGRAMMING FOR DATA | ||
| SURVEY METHODS | ||
| PROBABILITY, COMPUTING, AND SIMULATION IN STATISTICS | ||
| APPLIED STOCHASTIC MODELS | ||
| Select three additional upper-division courses of a data science nature or complete an option 2 | 9 | |
| Remaining Core Ed and Electives | 88-91 | |
| Total Credits | 180 | |
- *
Baccalaureate Core course. Applies to general education requirements for undergraduate students in a catalog year up to 2024-2025
- +
Core Education course. Applies to general education requirements for undergraduate students in catalog year 2025-2026 and beyond
- ^
Writing Intensive Curriculum (WIC) course
- 1
Students may complete an option but it is not required for the major. For students who do not select an option, they must complete the elective requirements for the major
- 2
These may include non-blanket upper-division MTH, CS, BDS courses or other courses approved by the departmental head advisor
Major Code: A051
Degree plans are subject to change and the following is only an example of how students may complete their degree in four years. Students should consult their advisor to determine the best degree plan for them. Contact details for advisors can be found on the Academic Advising page.
| First Year | ||
|---|---|---|
| Fall | Credits | |
| DS 101 | +EXPLORING CAREERS IN DATA SCIENCE | 1 |
| WR 121Z | +*COMPOSITION I | 4 |
| Core Ed: Communication, Media & Society | 3-4 | |
| Core Ed: Arts & Humanities General | 3-4 | |
| Core Ed: Transitions | 2 | |
| Credits | 15 | |
| Winter | ||
| DS 201 | +INTRODUCTION TO DATA SCIENCE | 4 |
| Core Ed: Arts & Humanities Global | 3-4 | |
| Core Ed: Social Science | 3-4 | |
| Elective | 4 | |
| Credits | 16 | |
| Spring | ||
| DS 231 | PYTHON PROGRAMMING FOR DATA SCIENCE | 4 |
| Core Ed: Scientific Inquiry & Analysis | 4 | |
| Core Ed: Difference, Power & Oppression Foundations | 3-4 | |
| Elective | 3 | |
| Credits | 14 | |
| Second Year | ||
| Fall | ||
| MTH 231 | ELEMENTS OF DISCRETE MATHEMATICS | 4 |
| MTH 251Z | +*DIFFERENTIAL CALCULUS | 4 |
| CS 162 | INTRODUCTION TO COMPUTER SCIENCE II | 4 |
| Core Ed: Scientific Inquiry & Analysis | 4 | |
| Credits | 16 | |
| Winter | ||
| CS 261 | DATA STRUCTURES | 4 |
| MTH 252Z | INTEGRAL CALCULUS | 4 |
| WR 227Z or WR 362 | +*TECHNICAL WRITING or +*SCIENCE WRITING | 3-4 |
| Elective | 3 | |
| Credits | 15 | |
| Spring | ||
| MTH 267 | LINEAR ALGEBRA FOR DATA SCIENCE | 3 |
| CS 340 | INTRODUCTION TO DATABASES | 4 |
| ST 314 or ST 351 | INTRODUCTION TO STATISTICS FOR ENGINEERS or INTRODUCTION TO STATISTICAL METHODS | 3 |
| Elective | 4 | |
| Credits | 14 | |
| Third Year | ||
| Fall | ||
| ST 411 | METHODS OF DATA ANALYSIS | 4 |
| ST 421 | INTRODUCTION TO MATHEMATICAL STATISTICS | 4 |
| Elective | 4 | |
| Elective | 3 | |
| Credits | 15 | |
| Winter | ||
| ST 412 | METHODS OF DATA ANALYSIS | 4 |
| ST 422 | INTRODUCTION TO MATHEMATICAL STATISTICS | 4 |
| Elective | 4 | |
| Elective | 3 | |
| Credits | 15 | |
| Spring | ||
| DS 431 | STATISTICAL LEARNING FOR DATA SCIENCE | 3 |
| MTH 301 | +INTEGRAL HISTORIES AND SOCIAL ISSUES IN MATHEMATICS | 3 |
| ST 437 | DATA VISUALIZATION | 3 |
| Option or Elective Course | 3 | |
| Elective | 3 | |
| Credits | 15 | |
| Fourth Year | ||
| Fall | ||
| Option or Elective Course | 15 | |
| Credits | 15 | |
| Winter | ||
| Option or Elective Course | 15 | |
| Credits | 15 | |
| Spring | ||
| DS 495 | +^CAPSTONE AND CAREER: DATA SCIENCE | 3 |
| Elective | 12 | |
| Credits | 15 | |
| Total Credits | 180 | |
- *
Baccalaureate Core course. Applies to general education requirements for undergraduate students in a catalog year up to 2024-2025
- +
Core Education course. Applies to general education requirements for undergraduate students in catalog year 2025-2026 and beyond
- ^
Writing Intensive Curriculum (WIC) course