IS 675 - Data Science

Credits: [3]

Description: This course is designed to provide an introduction to data science concepts and techniques. The course will include both theoretical foundations of commonly used data science methods as well as hands-on exercises using open source libraries like Python scikit learn. Topics will include techniques such as data preprocessing, classification, clustering, and visualization. Various algorithms on each of these techniques will be covered in the course. Examples of such algorithms include the apriori algorithm for logistic regression, support vector machines, and decision trees for classification; and k-means, DBSCAN, and hierarchical algorithms for clustering, and t-SNE for visualization. Several real-life applications will be discussed for each of these techniques.
Course ID: 100265
Prerequisite: IS 633 or an equivalent
Components: Lecture
Grading Method: A-F