Data science is one of the fastest-growing fields in existence. Scientists, businesses, government agencies and various organizations routinely gather huge amounts of data from a variety of sources. Data scientists help transform this information into insights that shape the world, asking and answering questions that influence decisions about healthcare, sustainability, business, security, equity – the list goes on.

Willamette’s data science program helps students gain contemporary computer programming and data analysis skills, either as a major course of study or a minor complementing any undergraduate major. The program also addresses issues such as the ethics of working with data while teaching students how to formulate good questions, design a process for answering them and effectively communicate their findings to a variety of stakeholders.

Students learn two core computer programming languages (R and Python). The R course focuses on introductory statistics, and the Python course focuses on introduction to computer programming. Students also complete electives that advance their knowledge of statistical, mathematical, analytical and machine learning techniques. Both majors and minors apply their skills in the Problem-Solving with Data Analytics class, while majors complete their bachelor’s degree with a capstone internship or research project.

## Program Strengths

Because Willamette is the only private liberal arts university in the Pacific Northwest with a data science major, students get the best of both: the strengths of a small, student-focused college campus, and the opportunities and advantages of a university with two outstanding graduate professional schools.

Willamette is uniquely positioned to provide students with a rich and rigorous education. Our data science degree integrates statistics, math and computer science with ethical inquiry and applied practice, as well as the critical thinking, problem-solving and communication skills that employers value. And our small-class settings allow relationships to flourish, leading to enlightening discussions and highly collaborative student-faculty research.

Willamette’s location across the street from the Oregon State Capitol and Salem Hospital means that internships and other professional development opportunities are always within reach. Undergraduate students also can take further coursework in data science and analytics at the Atkinson Graduate School of Management, one of the top business schools in the Pacific Northwest.

*Students who entered the University in Fall 2019 may elect to pursue the Data Science major contained in this catalog, or the modified major to be introduced in the Fall 2020 catalog.*

*Students who entered the University prior to Fall 2019 may apply to complete the Data Science major, but the correct constellation of courses might not be offered for them to do so.*

## Requirements for the Data Science Major (9 Credits)

### Core Courses (5)

- CS 151 Introduction to Programming with Python (1)
- MATH 239 Statistical Learning with R (1)
- CS 370 Fundamentals of Data Science I (1)
- DATA 375 Problem-Solving with Data Analytics (1)
- DATA 499W Independent Internship or Thesis (1) (to be developed)

### Electives (4):

#### Data Science + Computer Science track

- One elective chosen from the Applied Data Analysis category (1)BIOL 342 Biostatistics (1)
- BIOL 347 Bioinformatics (1)
- CS 475 Fundamentals of Machine Learning (1)
- ECON 350 Introduction to Econometrics and Forecasting (1)
- ENVS 250 Geographic Information Systems (1)
- ENVS 381 Research in Spatial Science (1)
- PHYS 338 Advanced Data Analysis and Simulation (ADAS) (1)
- SOC 341 Methods of Social Survey Design, Sampling, and Data Analysis (1)
- QUAD Center Internship

- Three electives chosen from the Statistical and Mathematical Theory category (3)

#### Data Science + Natural Sciences track

- One elective chosen from the Statistical and Mathematical Theory category: (1)
- Three classes chosen from the Natural Sciences category: (3)

#### Data Science + Social Sciences track

- One elective chosen from the Statistical and Mathematical Theory category: (1)
- Three classes chosen from the Social Sciences Category: (3)

## Requirements for the Data Science Minor (5 Credits)

### Core Courses (4)

- CS 151 Introduction to Programming with Python (1)
- MATH 239 Statistical Learning with R (1)
- CS 370 Fundamentals of Data Science I (1)
- DATA 375 Problem-Solving with Data Analytics (1)

### Elective (1)

Students must choose one from either category:

- Applied Data Analysis
- BIOL 342 Biostatistics (1)
- BIOL 347 Bioinformatics (1)
- CS 475 Fundamentals of Machine Learning (1)
- ECON 350 Introduction to Econometrics and Forecasting (1)
- ENVS 250 Geographic Information Systems (1)
- ENVS 381 Research in Spatial Science (1)
- PHYS 338 Advanced Data Analysis and Simulation (ADAS) (1)
- SOC 341 Methods of Social Survey Design, Sampling, and Data Analysis (1)

- Statistical & Mathematical Theory

# Indicators of Achievement

## Student Learning Outcomes for the Data Science Minor

**Students will develop...**

## Faculty

**Kelley Strawn**, Faculty Associate Dean for Curriculum, Associate Professor of Sociology**Haiyan Cheng**, Associate Professor**James Friedrich**, Professor of Psychology**Donald H. Negri**, Peter C. and Bonnie S. Kremer Chair in Economics; Professor of Economics**Peter Otto**, Professor of Mathematics,**Jed Rembold**, Visiting Assistant Professor of Physics**Rosa León Zayas**, Assistant Professor of Biology

## Course Listings

### DATA 199 Topics in Data Science (.25-1)

A semester-long study of topics in Data Science. Topics and emphases will vary according to the instructor. This course may be repeated for credit with different topics.

**General Education Requirement Fulfillment:**Topic dependent**Prerequisite:**Topic dependent**Offering:**Occasionally**Professor:**Staff

### DATA 299 Topics in Data Science (.25-1)

A semester-long study of topics in Data Science. Topics and emphases will vary according to the instructor. This course may be repeated for credit with different topics.

**General Education Requirement Fulfillment:**Topic dependent**Prerequisite:**Topic dependent**Offering:**Occasionally**Professor:**Staff

### DATA 375 Problem-Solving with Data Analytics (1)

Students will work in teams to apply data analytics tools and skills toward the resolution of a question or problem. Depending on the instructor, this course might organize all projects around a common question, problem, or challenge, or it might allow teams to identify a theme of their own. Teams will work with the instructor to develop a problem-solving strategy that utilizes the data-analytics skills and methods acquired in prerequisite courses, to develop a project plan, and to carry out the project. In most instances, projects will yield a summary essay, research paper, or white paper.

**General Education Requirement Fulfillment:**Math Sciences**Prerequisite:**CS 151, MATH 239, CS 370, and one elective from the Data Science minor applied data analysis or statistical and mathematical theory categories**Offering:**Annually**Instructor:**Friedrich, Strawn, Staff

### DATA 399 Topics in Data Science (.25-1)

A semester-long study of topics in Data Science. Topics and emphases will vary according to the instructor. This course may be repeated for credit with different topics.

**General Education Requirement Fulfillment:**Topic dependent**Prerequisite:**Topic dependent**Offering:**Occasionally**Professor:**Staff

### DATA 429 Topics in Data Science (.25-1)

**General Education Requirement Fulfillment:**Topic dependent**Prerequisite:**Topic dependent**Offering:**Occasionally**Professor:**Staff

### DATA 499W Independent Internship or Thesis

Course description will be posted soon.