8 courses with the subject CDS, 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.
CDS 1010Introduction to Programming4
Goals: To help students develop greater precision in their algorithmic thinking by writing moderate-sized programs for a variety of applications, including but not limited to biology, chemistry, economics, literary studies, and mathematics. Content: Students will learn the fundamentals of computer programming (loop structures, if-else statements, Boolean expressions, and arrays) to solve problems from different disciplines. A short introduction to object-oriented programming is also given. This course is taught using Python.
CDS 1020Introduction to Computational Data Science4
Goals: To continue the study of computational techniques using Python, with an emphasis on applications in data science and analysis. Content: This is a continuation of CDS 1010, applying algorithmic thinking to applications in data analysis. Topics include data mining, data visualization, web-scraping.
CDS 1010 and a course in statistics (MATH 1200 or QMBE 1310) with grades of C- or higher. Credits: 4
CDS 1100Introduction to R2
Crosslisted: Also listed as QMBE 1100 Goals: To introduce R and RStudio, in preparation for courses in analytics and economics. By the end of this course, students will be able to import data, understand data types, manage data, and generate basic statistical output. Content: In this course you will learn how to program in R, in preparation for courses that use R for data analysis. You will learn how to install and configure software necessary for a statistical programming environment and learn the basics of importing and managing data.
Crosslisted: Also listed as QMBE 1130 Goals: To introduce basic design principles for data visualization and interpretation. By the end of this course, students will be able to produce appropriate visualizations for a variety of types of data, including multivariate, temporal, text, and spatial data. Students will also learn to interpret data visualizations, use them in discussing the issues of the current world, and discuss the limitations of various visual representations of data. Content: Students will learn the fundamentals of data visualization, including figure design, figure making, and figure review. Students will apply these skills to a domain-specific and data-driven project to produce a poster, infographic, or webpage that conveys the primary conclusions inferred from the data.
CDS 1100/ QMBE 1100, grade of C- or higher, or concurrent enrollment Credits: 2
CDS 3200Elements of Statistical Learning4
Goals: This is a continuation course for MATH 1200, introducing techniques of statistical learning. Content: Supervised learning, with a focus on regression and classification methods. This includes linear and nonlinear models as well as model and feature selection. Some unsupervised learning methods such as principal components and clustering are also discussed.
CDS 1010 or CDS 1030, and MATH 1200 or QMBE 1310 Credits: 4
CDS 5920Seminar in Mathematics/Computational Data Science1
Goals: The student will be introduced to ideas and issues that are outside of the regular undergraduate curriculum, studying how mathematics/computational data science is used in academia and industry. Content: Reviews of current research and projects of various mathematicians/data scientists: junior and senior math/CDS majors, guest lecturers, and department staff. Student presentations of topics from internships, independent studies, or honors projects.
CDS 5930Mathematics/Computational Data Science Seminar Presentation1
Goals: The student will be introduced to ideas and issues that are outside of the regular undergraduate curriculum, studying how mathematics/computational data science is used in academia and industry. Content: Reviews of current research and projects of various mathematicians/data scientists: junior and senior math/CDS majors, guest lecturers, and department staff. Student presentations of topics from internships, independent studies, or honors projects.
Goals: To help students integrate the knowledge and skills attained in the Computational Data Science program. Content: Students will propose, execute, provide feedback on, and communicate about projects that are grounded in their disciplinary courses (the three courses they took that count toward another minor) but have a clear computational data science approach.
