4 courses with the subject DS, 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.
DS 150Data Science and Society
This course introduces students to data science and research design. This course is divided into two parts. During the first half of the semester, students will be introduced to theories of science and how systematic and falsifiable analysis applies to a wide variety of fields of study. During the second half of the semester, students will be introduced to data management, statistical and computer programming software, and econometrics.
Data scientists apply methods from statistics, data analysis, computer science, and machine learning in order to gain insight from data. In Data Science Programming, we focus on developing the programming and machine learning skills necessary to gain such insight. Through experiential learning, we equip students with the fundamental computer problem-solving skills and tools to clean raw data, engineer data features, build statistical and machine learning models, predict unknown values and/or discern patterns, and present data insights. No prerequisites.
Advanced treatment of data science concepts. Through a series of case studies, students explore datasets from a variety of domains and extract meaningful information and insights using mathematical, computational, and other scientific methods and algorithms. Topics include the fundamental algorithms of data science: regression, decision trees, support vector machines, clustering, and neural networks. Through a semester-long project, students demonstrate knowledge of fundamental data science concepts and ability to interpret and communicate effectively the results of the analysis. Prerequisites: DS 256: Data Science Programming and an approved statistics course.
