Data Science

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 or Fall 2021 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 Bachelor of Science degree (40 semester hours)

  • CS 151 Introduction to Programming with Python (4)
  • CS 370 Fundamentals of Data Science I (4)
  • CS 475 Fundamentals of Machine Learning (4)
  • DATA 403 Introduction to Data Engineering (4)
  • DATA 410 Undergraduate Capstone (4)
  • MATH 239 Statistical Learning with R (4)
  • MATH 280 Math for Data Science (4)
  • One DS Electives 100-level or greater or from approved list (4)
  • Two DS Electives 300-level or greater or from approved list (8)

Approved DS Electives from outside of DS

  • BIOL 342 Biostatistics (4)
  • BIOL 347 Bioinformatics (4)
  • CS 125 Problem Solving with MATLAB (4)
  • CS 241 Data Structures (4)
  • CS 343 Analysis of Algorithms (4)
  • ECON 350 Introduction to Econometrics and Forecasting (4)
  • ENVS 250 Geographic Information Systems (4)
  • ENVS 381 Research in Spatial Science (4)
  • MATH 253 Linear Algebra (4)
  • MATH 266 Probability and Statistics (4)
  • MATH 376 Topics in Mathematics: Probability Theory (topic dependent) (4)
  • PHYS 340 Advanced Data Analysis and Simulation (ADAS) (4)
  • PSYC 253 Research Methods and Analysis II (4)

Requirements for the Data Science Minor (20 semester hours)      

  • CS 151 Introduction to Programming with Python (4)
  • CS 370 Fundamentals of Data Science I (4)
  • MATH 239 Statistical Learning with R (4)
  • Two DS Electives 100-level or greater or from approved list (8)

Approved DS Electives from outside of DS

  • BIOL 342 Biostatistics (4)
  • BIOL 347 Bioinformatics (4)
  • CS 125 Problem Solving with MATLAB (4)
  • CS 241 Data Structures (4)
  • CS 343 Analysis of Algorithms (4)
  • ECON 350 Introduction to Econometrics and Forecasting (4)
  • ENVS 250 Geographic Information Systems (4)
  • ENVS 381 Research in Spatial Science (4)
  • MATH 253 Linear Algebra (4)
  • MATH 266 Probability and Statistics (4)
  • MATH 376 Topics in Mathematics: Probability Theory (topic dependent) (4)
  • PHYS 338 Advanced Data Analysis and Simulation (ADAS) (4)
  • PSYC 253 Research Methods and Analysis II (4)

 

Faculty


Course Listings

DATA 199 Topics in Data Science (1-4)

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. See the New and Topics Courses page on the Registrar’s webpage for descriptions and applicability to majors/minors in other departments.

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

DATA 299 Topics in Data Science (1-4)

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. See the New and Topics Courses page on the Registrar’s webpage for descriptions and applicability to majors/minors in other departments.

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

DATA 375 Problem-Solving with Data Analytics (4)

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: Strawn, Staff

DATA 399 Topics in Data Science (1-4)

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. See the New and Topics Courses page on the Registrar’s webpage for descriptions and applicability to majors/minors in other departments..

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

DATA 429 Topics in Data Science (1-4)

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. See the New and Topics Courses page on the Registrar’s webpage for descriptions and applicability to majors/minors in other departments.

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

DATA 499W Independent Internship or Thesis

Course in development.


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