Data Science Specialization Courses
Please note: The most updated information will be in the UW Time Schedule and on home department websites.
Course Descriptions, Prerequisites, Quarter Offered, Number of Credits
Software Development
CSE 583: Software Development for Data Scientists Provides students outside of CSE with a practical knowledge of software development that is sufficient to do graduate work in their discipline. Modules include Python basics, software version control, software design, and using Python for machine learning and visualization. Autumn 4 credits
CHEME 546: Software Engineering for Molecular Data Scientists Introduces basic principles of scientific software development in Python in the context of project-based work for molecular science and engineering spanning to the process scale. Covers command line tools, Python from the perspective of molecular and process engineering methods, software design, and development and collaboration principles (e.g., version control). Offered: jointly with CHEM 546/MSE 546 Winter 3 credits
BIOST 544: Introduction to Biomedical Data Science Provides an introduction to biomedical data science with an emphasis on statistical perspectives, inducing the process of collecting, organizing, and integrating information toward extracting knowledge from data in public health, biology, and medicine. Prerequisites: either BIOST 511 or equivalent; either BIOST 509 or equivalent; or permission of instructor. Autumn 4 credits
Statistics and Machine Learning
CSE 416/STAT 416: Introduction to Machine Learning Provides practical introduction to machine learning. Modules include regression, classification, clustering, retrieval, recommender systems, and deep learning, with a focus on an intuitive understanding grounded in real-world applications. Intelligent applications are designed and used to make predictions on large, complex datasets. Prerequisite: either CSE 123, CSE 143, CSE 160, or CSE 163; and either STAT 311, STAT 390, STAT 391, IND E 315, MATH 394/STAT 394, STAT 395/MATH 395, or Q SCI 381. Spring 4 credits
STATS 435: Intro to Statistical Machine Learning Introduces the theory and application of statistical machine learning. Topics may include supervised versus unsupervised learning; cross-validation; the bias-variance trade-off; regression and classification; regularization and shrinkage approaches; non-linear approaches; tree-based methods; and support vector machines. Includes applications in R. Prerequisite: either STAT 341, STAT 390/MATH 390, or STAT 391; recommended: MATH 208. Spring 4 credits
BIOST 545: Biostatical Methods for Big Omics Data This “hands-on” course introduces statistical methods for high-dimensional omics data, as well as the R programming language and the Bioconductor project as tools to extract, query, integrate, visualize, and analyze real world omics data sets. Prerequisites: BIOST 512, 514, or 517. Not offered 3 credits
BIOST 546: Machine Learning for Biomedical and Public Health Big Data Provides an introduction to statistical learning for biomedical and public health data. Intended for graduate students in SPH/SOM. Winter 3 credits
BIOST 558: Statistical Machine Learning for Data Scientists Bias-variance trade-off; training versus test error; overfitting; cross-validation; subset selection methods; regularized approaches for linear/logistic regression: ridge and lasso; non-parametric regression: trees, bagging, random forests; local regression and splines; generalized additive models; support vector machines; k-means and hierarchical clustering; principal components analysis. Offered: jointly with DATA 558/STAT 558 Spring 5 credits
*CSE 546/STATS 535: Machine Learning – This choice has a number of prerequisites and is part of the advanced data science option. Explores methods for designing systems that learn from data and improve with experience. Supervised learning and predictive modeling; decision trees, rule induction, nearest neighbors, Bayesian methods, neural networks, support vector machines, and model ensembles. Unsupervised learning and clustering. Prerequisites: CSE 312, STAT 341, STAT 391 or equivalent. Spring 4 credits
Stat 535: Covers statistical learning over discrete multivariate domains, exemplified by graphical probability models. Emphasizes the algorithmic and computational aspects of these models. Includes additional topics in probability and statistics of discrete structures, general purpose discrete optimization algorithms like dynamic programming and minimum spanning tree, and applications to data analysis. Prerequisite: experience with programming in a high level language. Autumn 3 credits
*Only STAT 512 (AUT) -513 (WIN) – Statistical Inference (4 credits each). on eScience institute website only STAT 512 – Autumn STAT 513 – Winter 4 credits each
*Only for Adv Data Science – STAT 509 (AUT) – Introduction to Mathematical Statistics: Econometrics I (5 credits) on eScience institute website only Autumn 4 credits
Data Management and Data Visualization
CSE 414: Introduction to Database Systems Introduces database management systems and writing applications that use such systems; data models, query languages, transactions, database tuning, data warehousing, and parallelism. Intended for non-majors. Cannot be taken for credit if credit received for CSE 344. Prerequisites: a minimum grade of 2.5 in either CSE 123, CSE 143, or CSE 163. Winter 4 credits
HCDE 411: Information for Visualization (HCDE 411, 5cr or 511, 4cr) Introduces the design and presentation of digital information. Covers the use of graphics, animation, sound, and other modalities in presenting information to the user; understanding vision and perception; methods of presenting complex information to enhance comprehension and analysis; and the incorporation of visualization techniques into human-computer interfaces. Prerequisite: HCDE 308 and HCDE 310. Winter 5 credits
HCDE 511: Information Visualization Covers the design and presentation of digital information. Uses graphics, animation, sound, and other modalities in presenting information to users. Studies understanding vision and perception. Includes methods of presenting complex information to enhance comprehension and analysis; and incorporation of visualization techniques into human-computer interfaces. “For HCDE PhD only” offered in summer A term. Summer, Winter 4 credits
INFO 474: Interactive Information Visualization Techniques and theory for visualizing, analyzing, and supporting interaction with structured data like numbers, text, and relations. Provides practical experience designing and building interactive visualizations for the web. Exposes students to cognitive science, statistics, and perceptual psychology. An empirical approach will be used to design and evaluate visualizations. Prerequisite: INFO 340 or CSE 154; CSE 123, CSE 143, or CSE 163; and either QMETH 201, Q SCI 381, STAT 220, STAT 221/CS&SS 221/SOC 221, STAT 290, STAT 311, or STAT 390. Autumn 5 credits
CSE 412: Intro to Data Visualization Introduction to data visualization design and use for both data exploration and explanation. Methods for creating effective visualizations using principles from graphic design, psychology, and statistics. Topics include data models, visual encoding methods, data preparation, exploratory analysis, uncertainty, cartography, interaction techniques, visual perception, and evaluation methods. Cannot be taken for credit if credit received for CSE 442. Prerequisite: either CSE 123, CSE 143, or CSE 163. Winter 4 credits
CSE 442: Data Visualization Techniques for creating effective visualizations of data based on principles from graphic design, perceptual psychology, and statistics. Topics include visual encoding models, exploratory data analysis, visualization software, interaction techniques, graphical perception, color, animation, high-dimensional data, cartography, network visualization, and text visualization. Prerequisite: CSE 332. Winter 4 credits
CSE 544: Principles of DBMS Data models and query languages (SQL, datalog, OQL). Relational databases, enforcement of integrity constraints. Object-oriented databases and object-relational databases. Principles of data storage and indexing. Query-execution methods and query optimization algorithms. Static analysis of queries and rewriting of queries using views. Data integration. Data mining. Principles of transaction processing. This choice requires a number of prerequisites, including comfort in Java programming. This choice is part of the advanced data science option. Winter 4 credits
CSE 512: Data Visualization Covers techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science. Topics include data and image models; visual encoding; graphical perception; color; animation; interaction techniques; graph layout; and automated design. Lectures, reading, and project. This choice is part of the advanced data science option. Spring 4 credits
*IMT 562 – Interactive Information Visualization Winter 4 credits