DSC 201 Applied Data Science
This course introduces and upgrades tools essential for data science, machine learning, fundamentals of artificial neural networks, and effective communication of problems and findings. The study delves into seminal works in recommender systems, linear regression, logistic regression, decision trees, random forest, k-nearest neighborhood, k-means clustering, support vector machine, dimension reduction techniques, and fundamentals of neural networks within a project-based learning environment, examining their implications within the students’ fields of interest. Students will explore data mining, mathematical, and statistical methods while elevating ethics, visuals, and data handling from the introductory course.
Credits
4
Offered
fall semester