The Willamette MSCS was designed for students who wish to deepen their knowledge of Artificial Intelligence (AI) by exploring advanced topics in machine learning and data engineering, alongside coursework in critical topics like human factors, cloud computing, and ethics and governance. The program ends with a Capstone project that offers the opportunity for hands-on experience with AI implementation and deployment.
The flexible structure of the program allows students to set their own pace and complete the MS in Computer Science in as few as 12 months (3 semesters), or to add additional time as needed.
Students must complete at least two classes from the Software and Systems category and at least one class from each of the other three categories. 36 credit hours (generally 9 classes) are required for degree completion.
MSCS courses are taught by faculty members in the School of Computing and Information Sciences as well as highly-credentialed adjunct faculty with connections to local industry. The selection of courses varies every program year, with frequent new additions.
Course Descriptions, 2025-2026
Machine Learning Sequence
Python for Machine Learning Applications
Data Science is the study of knowledge extraction from massive amounts of data. It requires an integrated skill set including aspects of mathematics, statistics, and computer science, as well as effective problem solving techniques and domain knowledge. This course will introduce students to this rapidly growing field, with an emphasis on programming techniques in Python. Students will learn the fundamentals of data structures and complexity theory, data wrangling, exploratory data analysis, data visualization, and basic machine learning algorithms such as regression, classification, and clustering. Additionally, students will practice effective communication by framing their analyses within appropriate context and ethical considerations.
- Category: Software and Systems
Applied Machine Learning
Machine learning is becoming a core component of many modern organizational processes. It is a growing field at the intersection of computer science and statistics focused on finding patterns in data. Prominent applications include personalized recommendations, image processing and speech recognition. This course will focus on the application of existing machine learning libraries to practical problems faced by organizations. Through lectures, cases and programming projects, students will learn how to use machine learning to solve real world problems, run evaluations and interpret their results. Requires completion of CS 501
- Category: Algorithms and Complexity
Advanced Machine Learning
This advanced machine learning course is designed to provide students with in-depth exploration of advanced topics, techniques, algorithms, and applications in the field of machine learning. Through a combination of lectures, hands-on exercises and project-based learning, students will gain a comprehensive understanding of machine learning techniques and their applications across domains. Topics may include: training and fine-tuning neural networks, generative networks, natural language processing, latent space representations, and evaluating ethical considerations such as dataset quality and bias. Particular emphasis will be given to current events and recent advances in the field. Requires completion of DATA 505
- Category: Software and Systems
Data Engineering Sequence
Data Ethics, Policy and Human Beings
This course explores the legal, policy, and ethical implications of data. These types of issues arise at each stage of the data science workflow including data collection, storage, processing, analysis and use. Armed with legal and ethical guidelines, students are then confronted with topics including privacy, surveillance, security, classification, discrimination, decisional-autonomy, and duties to warn or act. Using case studies and a lecture-discussion format, the course will address real-world problems in areas like criminal justice, national security, health, marketing and politics.
- Category: Ethics and Policy
Fundamentals of Data Engineering
Data management is core to both applied computer science and data science. This includes storing, managing, and processing datasets of varying sizes and types. This course introduces students to some of the various ways in which data is stored and processed, with particular focus on relational databases and SQL. Over the course of the semester, students will also learn the fundamentals of command-line interfaces, remote connections, and containerization in order to construct a data pipeline to ingest raw data, transform and organize that data into something useful, and then make that polished data available to downstream consumers.
- Category: Software and Systems
Advanced Data Engineering
Data engineers design, implement, and maintain the data pipelines that power modern technology and society. This course builds on previous coursework, focusing on ingesting data from diverse sources, including logs, data streams, and various database types. Students will explore automated orchestration tools for scheduling and managing data pipelines, along with key concepts and technology in data warehousing and lakehouses. Emphasis will be placed on documentation, pipeline maintenance, and development through collaborative, semester-long projects. Requires completion of DATA 503
- Category: Software and Systems
Cloud Application Development
Cloud Application Development covers fundamental cloud computing concepts, cloud infrastructure and virtualization, cloud-native development environments, web application development basics, cloud storage services and databases, cloud security and compliance, deployment strategies, scaling techniques, monitoring, logging, and troubleshooting cloud applications. It ends with a final project where students design and develop a cloud-based web application and present their work.
- Category: Software and Systems
Human-Computer Interaction
The Human-Computer Interaction (HCI) course focuses on sustainable design and green computing, teaching students to apply HCI principles and methods to address practical challenges. Through group project-based learning, students develop information and communication technology (ICT) solutions aligned with United Nations’ Sustainable Development Goals, using techniques like participatory design and usability studies. The course covers sustainable interaction design principles, persuasive design methods, and Green ICT strategies. By integrating user needs with environmental considerations, students learn to create innovative user-centered designs and prototype solutions that support users and conform with sustainability for current and future generations.
- Category: Humans and Design
Data Visualization and Presentation
It’s one thing to conduct an analysis, it’s another to convince someone to change their behavior based on this analysis. In this course, students will study theories of visualization, communication and presentation with the purpose of translating technical results into actionable insight. Using a mix of case studies and code, the course begins with an examination of how to ask good research questions. It then covers the psychology of communication and the construction of compelling visualizations. Finally, students are tasked with writing and presenting their work in a manner suited to a non-technical audience.
- Category: Humans and Design
Computer Science Capstone
This course integrates knowledge from core computer science areas, challenging students to design, develop, and present a comprehensive software or data project. Working in teams, students will apply programming, algorithms, and system design principles to solve real-world problems. Students will learn about the software development lifecycle and practice effective team management skills. The course culminates in a final presentation and demonstration to faculty and peers, showcasing technical expertise and problem-solving skills.
- Category: Software and Systems
Recommended Course Plan for 2025-2026, 12-Month MSCS Completion
Fall:
Python for ML
Human Computer Interaction
Data Ethics and Privacy
Spring:
Cloud Applications for the Web
Data Engineering
Machine Learning
Summer:
Computer Science Capstone
Advanced Data Engineering
Advanced Machine Learning