Data Science

253.535.7400 www.plu.edu/computer-science/data-science/ cs@plu.edu
Laurie Murphy, M.S., Chair

Our society increasingly values decisions that are supported by data. PLU graduates who can enter their vocations and their communities with experience of collecting, managing and analyzing data will be empowered to lead and serve more thoughtfully, skillfully, and rationally.

Data science is emerging as a filed that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data… Data science spans a broader array of activities that involve applying principles for data collection, storage, integration, analysis, inference,  communication, and ethics. — National Academy of Sciences (NAS), 2018

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 level of PLU MATH 140 (Precalculus) is required.

Minor in Data Science

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)

Students may complete requirements for the minor in any order that meets course prerequisites.

A maximum of eight (8) credits may be double-counted for other major and minor requirements, although students minoring in statistics may not use any of their “8 additional semester hours of statistics” toward the Data Science minor.

Students may transfer a maximum of 8 semester hours toward the Data Science minor, unless they have permission from the chair.

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

Computational and Data Science Foundations

8 semester hours

  • DATA 133: Introduction to Data Science I or CSCI 144: Introduction to Computer Science (4)
  • DATA 233: Introduction to Data Science II (4)

Statistical Foundations

8 semester hours

  • Any of MATH/STAT 145, STAT 231, 232, 233, or MATH/STAT 242 (4)
  • MATH/STAT 348: Statistical Computing and Consulting (4)

Domain-Specific Elective

4 semester hours

Select at least one course from the list of electives below that applies data science principles in a disciplinary context or provides deeper study of data science topics. The 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)

Approved courses include:

  • BUSA 310: Information Systems and Database Management (4)
  • BUSA 467: Marketing Research (4)
  • COMA 342: Applied Research (4)
  • CSCI 330: Artificial Intelligence (4)
  • CSCI 367: Databases and Web Programming (4)
  • ECON 344: Econometrics (4)
  • GEOS 331: Maps: Computer-Aided Mapping and Analysis (4)
  • NURS 360: Nursing Research and Informatics (4)
  • POLS 301: Political Science Methods (4)
  • PSYC 242: Advanced Statistics and Research Design (4)
  • SOCI 232: Research Methods (4)

Course Offerings by Semester/Term

  • Fall Semester: DATA 133
  • Alternate Years: DATA 233 (Spring, even years)

Data Science (DATA) - Undergraduate Courses

DATA 133 : Introduction to Data Science I

Introduction to computer programming and problem-solving using real datasets from a variety of domains such as science, business, and the humanities. Introduces the basics of data science concepts through computational thinking, modeling and simulation and data visualization using the Python programming language and R statistical software. Intended for students without prior programming experience. Prerequisite: completion of PLU MATH 140 or an equivalent college-level course with a grade of C or better; or PLU mathematics placement into PLU MATH 151 or a higher numbered PLU mathematics course. (4)

DATA 233 : Introduction to Data Science II

Continuation of DATA 133, topics may include data manipulation, cleaning and visualization techniques, machine learning techniques, natural language processing, databases, text mining, data science ethics/privacy, etc. Students will collaborate with help of version control systems like GitHub. Python is the main programming language used. Prerequisite: DATA 133 or CSCI 144. Recommended: One of MATH/STAT 145, STAT 231, 232, 233, or MATH/STAT 242. (4)

Last Modified: July 10, 2019 at 12:33 pm