This course focuses on developing the foundational skills of a modern data scientist including data cleaning, wrangling, visualization, and communication. Students will actively engage with R and RStudio, the most popular programming language and software environment for statistical computing. The course covers basic descriptive statistics (mean, standard deviation, quantiles, correlation) and introduces students to the tools they need to work with large, real-world data sets. Students will also develop the critical thinking skills needed to use data ethically. The course is the first of two in the introductory Data Science sequence, but will also be of interest to any student who wants to better understand the data they meet in everyday life and in the world around them. The course does not assume any previous background in statistics or programming.
This course gives students a solid grounding in the theory and practice of basic inferential statistics: confidence intervals, hypothesis testing (including chi-squared tests and ANOVA), and linear regression. Students will implement these techniques using R, a statistical programming language. The course also introduces the topics from probability theory needed to understand these methods (Law of Large Numbers and the Central Limit Theorem), and introduces students to the computational techniques needed to carry out these tests, including randomization and resampling. Students will develop the skills to write well-defined research questions, test hypotheses, and communicate results in a manner that facilitates action by decision makers.
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.
This project based course provides an overview of modern approaches to analyzing large and complex real world data sets from diverse applications. Students will learn techniques in modeling and predictive methods from selected topics in supervised learning and unsupervised learning. Building off a strong foundation from the generalized linear model framework, students will learn to assess model assumptions and motivate machine learning methods; which may include classification (logistic regression, linear discriminant analysis, naive Bayes, k-means, etc), non-linear and non-parametric methods, support vector machines, decision trees (classification and regression trees, random forests), boosting, neural networks, and additional topics, if time allows. Students will become proficient in implementing these methods using R packages.
In the coming years, scientific telescopes will be collecting vast amounts of data on the observable sky and our place in the cosmos. As a result, astronomy is intersecting with the field of data science like never before. This course will provide students with an opportunity to explore the techniques and applications of data science in modern astronomy. You will work with large datasets to study the evolution of stars and the age of the universe, use signal processing techniques to identify planets orbiting other stars, and employ basic machine learning techniques to categorize galaxy types. Collaborating with peers from various disciplines, you will also learn how to communicate scientific findings effectively through written and oral presentations. While no prior science background is required, proficiency in programming languages like Python or R is a prerequisite, and a solid grasp of algebra and geometry is highly recommended.
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.
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 the various ways in which data is stored and processed including relational databases, file-based databases, cloud-based storage and data streaming. Students will also learn how to access data using Structured Query Language (SQL).
This course is intended for the qualified advanced student who wishes to do an intensive independent study in an area not covered by an existing course in the department. Arrangements for this course must be made with a faculty member before registration
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..
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.
Individualized program of investigative research, in which a student works directly with a Data Science faculty member on their area of research expertise. Nature of participation varies from collaborative research to the design and execution of an independent project. The course provides hands-on experience, which may include literature review, data collection, data management, data analysis, and the synthesis of results in a formal paper and/or oral presentation. May be repeated for credit until a maximum of 8 total credits.
Willamette University