11 courses with the subject DSC, 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.
DSC 291Introductory Research in Data Science
Data Science research under faculty supervision beyond what is offered in existing courses.
Introduction to machine learning, including necessary linear algebra topics, such as vectors, matrices, eigenvalues, eigenvectors, symmetric matrices and singular value decompositions. Considers gradient descent methods to optimize predictive and classification models, dimension reduction, clustering, cross validation, grid search, data visualization and dashboards.
Foundational topics in the field of Data Science Topics to include data cleaning, Structured Query Language (SQL), data manipulation, visualizations, elementary classification, and machine learning.
Introduction to data science with an overview of the basic techniques for making informed, data-driven decisions using the R statistical software. Students learn the basics of R programming and use it to visualize, wrangle, manipulate, and explore data of many types and sizes.
This course introduces undergraduate students to the principles and practices of modern data engineering, with a focus on building scalable, reliable, and efficient data pipelines to support data science and analytics. Using a Data Intelligence Platform such as Databricks , students will gain hands-on experience in ingesting, transforming, integrating, and managing structured and unstructured data. The course emphasizes the mathematical and computational underpinnings of data engineering—including algorithms, data modeling, and optimization—while preparing students to work with enterprise-grade cloud data platforms.
Investigation under faculty supervision beyond what is offered in existing courses. For further information, consult the Directed Individual Studies section in this catalogue.
Hands-on experience in applying data science methods to real-world problems. Students work on projects that involve the full data science workflow: problem definition, data acquisition and cleaning, exploratory analysis, model development, and data communication. Focus is placed on developing skills with emerging tools and platforms used in industry and research.
Academic training and practical experience through work in a private company or public agency. Faculty supervision and evaluation of all study and on-site activity. Written experience report and oral presentation required.
