Data Science Major

In today’s digital age, there is great demand for professionals who can navigate complex questions in data science.

Our data science major and minor are about ethically stewarding and gleaning insights from data. We are committed to developing data scientists who understand the weight of their contributions and recognize the far-reaching impacts of their work beyond just immediate outcomes.

As a graduate of our data science program, you will be prepared to serve, lead, and care for society by gleaning insights from data and responsibly stewarding and communicating them to a variety of audiences.  You’ll be able to create meaningful models, understand the limitations and strengths of the models, communicate results with integrity, and tackle the complex issues your work may entail.

Societal demand

Data Scientists and Statisticians are the 3rd and 4th fastest-growing occupations in the U.S. (Bureau of Labor Statistics, 2023)

Job opportunities

Data scientist roles span across industries from finance, scientific research, non-profit and community organizations, government, health and  technology.

The world needs more Lute data scientists

Our PLU mission and curriculum is well-suited to prepare our data scientists to see from multiple perspectives as they address complex issues.

You can explore data science at PLU in many ways.

From earning a B.S. in data science to minoring in Data Science, taking DATA and STAT courses, engaging in ASA DataFest competitions, and conducting student research to launching careers in data sciences like our successful alumni, and supporting various related programs, including disciplines like sociology, psychology, economics, biology, and beyond!

The Bachelor of Science degree in data science equips students with the knowledge, skills, and habits of mind (e.g., curiosity, skepticism, holding results with intellectual humility) needed to harness the power of data ethically and responsibly. The Bachelor of Science degree is jointly offered by the mathematics and computer science departments.

Learning Outcomes

The primary learning outcomes for the data science major’s curriculum are:

1. Design: Be able to critically analyze a problem and to design, implement, and evaluate a solution that meets requirements. 
2. Communication: Be able to effectively communicate technical concepts in oral and written form. 
3. Application: Be able to apply mathematical or statistical concepts to concrete situations. 
4. Disciplinary Citizenship: Develop collaborative skills and independence; have experience with open-ended inquiry.

64 semester hours

28-32 semester hours of mathematics/statistics, 24-28 semester hours of computer science/data science, plus 4-8 semester hours of supporting courses

• 20 semester hours of required mathematics/statistics courses: MATH 152, 331, MATH/STAT 242*, MATH/STAT 348, MATH/STAT 442**
• 12 semester hours of mathematics/statistics electives from: MATH 253, 318, 422, or MATH/STAT 342.
• 20 semester hours of required computer science/data science courses: CSCI 144, 270, 330, DATA 233, DATA 499A, DATA 499B
• 8 semester hours of electives from: CSCI 367, CSCI 371, CSCI 390
• 4 semester hours of supporting courses from a Domain-Specific Elective. Select at least one option from the list of Domain-Specific Electives that applies data science principles in a disciplinary context or provides deeper study of data science topics.  (See details below.)

* MATH/STAT 145, STAT 231, STAT 232, or STAT 233 may replace MATH/STAT 242.

**ECON 344 may substitute MATH/STAT 442 if it is not also used as the domain-specific elective.

All courses counted toward the major must be completed with grades of C or higher.

A maximum of eight (8) credits at the 300+ level may be double-counted for other major requirements and a maximum of eight (8) credits may be double-counted for other minor requirements. Petitions to substitute courses may be submitted to the Director of Data Science  to address double-counting constraints. Students minoring in statistics may not use any of their “8 additional semester hours of statistics” toward the Data Science major.

Minor in Data Science

The data science minor is ideal for students who would benefit from in-depth experiences managing, analyzing, and visualizing data. The minor is designed for students from virtually any major, although quantitative literacy at or exceeding the PLU MATH 140 (Precalculus) level is required.

20 semester hours

Data science minors must complete a minimum of 20 credit hours in the following areas:

• Computational and Data Science Foundations (8) 
• Statistical Foundations (8)
• Domain-Specific Elective (4)

Domain Specific Elective Course Options

The Domain-Specific Electives give students the opportunity to explore or apply data science in the context of a specific discipline, or to deepen their understanding of data-science related topics beyond the required courses.

To qualify as a Domain-Specific Elective, a course must go beyond introductory topics and techniques to develop advanced statistical expertise for the respective field where at least one of the following are met:

1. Data are not easily collected (e.g., makes use of intricate study design; requires in-depth survey design), OR
2. Data are not easily managed (e.g., data are messy; data set is excessively large; data are not easily synthesized), OR
3. Data are not easily analyzed by selecting routine analyses from a series of menu items (e.g., arguments must be made for appropriate covariates), OR
4. Data are not easily presented (e.g., requires sophisticated visualization techniques)

A list of pre-approved courses is given below.  Students may petition for another course to count as their Domain-Specific Elective using the form given in the Documents Section of this site. 

• BUSA 310: Information Systems and Database Management (4)
• BUSA 467: Marketing Research (4)
• COMA 342: Applied Research (4)
• COMA 461: Advertising, PR + Campaigns (4)
• Selected CSCI 387/388/389/487/488/489: Special Topics in CSCI Courses (4)
• ECON 344: Econometrics (4)
• GEOS ESCI 331: Maps: Computer-Aided Mapping and Analysis (4)
• NURT 318: Research Methods (2) with NURT 319: Healthcare Technology (2)
• POLS 301: Political Science Methods (4)
• PSYC 242: Advanced Statistics and Research Design (4)
• SOCI 232: Research Methods (4)