21 courses with the subject SDS, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
SDS 164Data Science 1
Data is the currency of the modern world. At the intersection between statistics and computer science, data science is about gleaning information and making decisions from data. Using data from a variety of contexts and disciplines, students learn to summarize and extract insight from data, create compelling data visualizations, wrangle data, practice literate programming, and explore ethical issues in data science. Offered annually during fall semester, January term, and spring semester. No prior experience with programming is expected. This course cannot be taken after SDS 264. OLE Core Requirement: Quantitative and Computational Reasoning.
A first course in statistical methods, this course addresses study design and its implications as well as exploratory and inferential techniques for analyzing and modeling data. Topics include exploratory graphics, descriptive techniques, randomization tests, statistical designs, hypothesis testing, confidence intervals, and simple/multiple regression. Offered annually during fall semester, January term, and spring semester. Enrollment limited for seniors. STAT 110, SDS 172, and ECON 260 all provide an introduction to statistics and students should not take more than one; they all can serve as a prerequisite for further courses. OLE Core Requirement: Quantitative and Computational Reasoning.
Data collected over space - including county level health information, satellite derived maps of glacier ice, or locations of hawk nests - require special methods for data cleaning, visualization, and modeling. In this course, students build upon foundational concepts of hypothesis testing, regression modeling, and data visualization from prior Statistics and Data Science (SDS) courses, adapting them to the particularities of spatial data using examples from a broad variety of fields. Offered alternate years during January term. Prerequisite: SDS 164 and SDS 172.
This course explores the history, foundational principles, and theory of data visualization. By designing and creating visualizations with a focus on the iterative process, students learn how to clearly and effectively communicate a story with data. The course utilizes multiple forms of visualization, from sketches to static plots to interactive web -based dashboards using professional visualization software. Offered during fall and spring semesters. Prerequisites: SDS 164, SDS 172, AP Statistics, or permission of instructor. OLE Core Requirement: Creativity.
After mastering the foundations of data science - especially data wrangling and visualization - in SDS 164, data science students are ready to explore impactful applications and deeper fundamentals. Programming fundamentals may include iteration, functions, conditions, data types, SQL, regular expressions, version control, and simulation - all in the context of real data. New applications may include text as data, geospatial mapping, networks, and web scraping to produce dynamic graphics - presented with attention to data ethics and reproducible research. Offered annually in fall and spring semester. Prerequisite: SDS 164 and CSCI 121, or permission of instructor.
This course takes a case-study approach to the fitting and assessment of statistical models with application to real data. Specific topics include multiple regression, model diagnostics, logistic regression, experimental design and ANOVA. The approach focuses on problem-solving tools, interpretation, model assumptions underlying analysis methods, and written statistical reports. Offered during fall and spring semesters. Prerequisite: SDS 172, ECON 260 or equivalent preparation (STAT 110 and SDS 164) or (AP Stat and SDS 164), or permission of instructor.
Students work intensively on a special topic in data science. Topics may vary from year to year. May be repeated if topics are different. Offered periodically. Prerequisites: permission of instructor.
The course investigates issues in health-related settings using a quantitative, research-oriented perspective. Course material focuses on global and public health issues, study design, methods for analyzing health data, and communication of research findings. Design topics include controlled trials, case-control, cohort and other observational studies. Methods include survival analysis and causal inference for observational studies. Communication emphasizes writing up findings and interpreting published research. Offered alternate years during January term. Prerequisite: completion of MATH 120 and SDS 272 or permission of the instructor.
Independent work is guided by a faculty member. Students need to have the necessary background, including appropriate coursework, before an independent project is approved. A student may not substitute an IS/IR course for a course regularly offered in a department or program. IS/IR culminates in at least one product (a paper, laboratory report, work of art, etc.). Normally, the student and faculty instructor are expected to meet at least once a week. See catalog for more information under registration and course enrollment. Prerequisites: at least second-year standing; at least 2 relevant courses for 298 (IS); approval from the faculty instructor, the academic advisor, and the department chair or program director.
This course extends and generalizes methods introduced in SDS 272 by introducing generalized linear models (GLMs) and correlated data methods. GLMs cover logistic and Poisson regression, and more. Correlated data methods include longitudinal data analysis and multi level models. Applications are drawn from across the disciplines. Offered annually during fall semester. Prerequisite: SDS 272 and Calculus (MATH 119 or MATH 120) or permission of instructor.
This course is an investigation of modern statistical theory along with classical mathematical statistics topics such as properties of estimators, likelihood ratio tests, and distribution theory. Additional topics include Bayesian analysis, bootstrapping, Markov Chain Monte Carlo, and other computationally intensive methods. Offered alternate years. Prerequisite: SDS 272 and MATH 262.
Data with thousands of dimensions or columns is commonplace across multiple disciplines, including image analysis, natural language processing, genomics, time series, and recommendation systems. In this class students learn, understand, and apply principles and techniques to gain insights from high-dimensional data. Topics include clustering, principal component analysis, multidimensional scaling, non-negative matrix factorization, missing-value imputation for recommendation systems, topic modeling, word embeddings, object recognition using "eigenfaces", non-linear embedding techniques, multiple hypothesis testing, and false discovery rate adjustments. Offered periodically during fall and spring semesters. Prerequisites: MATH 220 and SDS 164.
This course introduces students to the subject of machine learning. The primary focus is the development and application of powerful machine learning algorithms applied to complex, real-world data. Topics covered include linear regression, nearest neighbor models, k-means clustering, shrinkage methods, decision trees and forests, boosting, bagging, support vector machines, and hierarchical clustering. Applications are taken from a wide variety of disciplines, including biology, economics, public policy, public health, and sports. Offered annually during fall and spring semester. Prerequisite: SDS 264 and SDS 272, or permission of the instructor.
Students work intensively on a special topic in data science. Topics may vary from year to year. May be repeated if topics are different. Offered periodically. Prerequisites: SDS 264 and permission of the instructor.
Students work intensively on a special topic in statistics. Topics may vary from year to year. May be repeated if topics are different. Offered periodically. Prerequisites: Permission of instructor.
SDS 389Statistics and Data Science Research Methods0.50
Students focus on writing scientific papers, preparing scientific posters, and giving presentations in the context of a specific, year-long, interdisciplinary research project. In addition, this weekly seminar series builds collaborative research skills such as working in teams, performing reviews of math, statistics, and computer science literature, consulting effectively, and communicating proficiently. Exposure to post-graduate opportunities in statistics and data science disciplines is also provided. Open to students accepted into the Center for Interdisciplinary Research and by permission of the instructor only. This course does not count toward the SDS Major or SDS Concentration. OLE Core Requirement: OLE Experience in Practice.
This course provides a comprehensive research opportunity, including an introduction to relevant background material, technical instruction, identification of a meaningful project, and data collection. The topic is determined by the faculty member in charge of the course and may relate to their research interests. Offered based on department decision. May be offered as a 1.00 credit course or .50 credit course. Prerequisite: determined by individual instructor.
Independent work is guided by a faculty member. Students need to have the necessary background, including appropriate coursework, before an independent project is approved. A student may not substitute an IS/IR course for a course regularly offered in a department or program. IS/IR culminates in at least one product (a paper, laboratory report, work of art, etc.). Normally, the student and faculty instructor are expected to meet at least once a week. See catalog for more information under registration and course enrollment. Prerequisites: at least second-year standing; at least 5 relevant courses for 398 (IR); approval from the faculty instructor, the academic advisor, and the department chair or program director.
