Related Courses


Combinatoric Structures: Representation, analysis, techniques, and principles for manipulation of basic combinatoric data structures used in computer science. Rigorous reasoning is emphasized. (Counts as a CS Background Course for the concentration.)


Intro to Database Systems: CS460 is an undergraduate introduction to the principles of the relational database management systems (DBMSs). The goal of the course is to introduce students to the main issues on the design and implementation of a database system and to present how a system like that can be used effectively.


Advanced Database Applications: The goal of the course is to introduce students to modern database and data management systems. The first part of the course will be focused on efficient query processing and indexing techniques for spatial, temporal and multimedia databases. The next part of the course will cover some recent advances in modern database systems including database security, probabilistic databases, semantic web and databases, and databases on the cloud. Students will have to solve some small written and programming assignments that will help them to understand and digest the covered material.


Data Mining: The goal of this course is to provide an introduction to the main topics in data mining including: frequent-itemset mining, clustering, classification, link-analysis ranking, dimensionality reduction etc. The focus of the course will be on the algorithmic issues as well as applications of data mining to real-world problems. Students will be required to solve small written and programming assignments that will help them better understand the covered material.


Advanced Topics in Data Mining: The general theme of the course was on algorithmic issues related to data mining. The specific focus was on models and algorithms for network data (e.g., social networks, collaboration networks, biological networks, etc).


Tools and Techniques for Data Mining and Applications: The course emphasizes practical skills in working with data, while introducing students to a wide range of techniques that are commonly used in the analysis of data, such as clustering, classification, regression, and network analysis. The goal of the class is to provide to students a hands-on understanding of classical data analysis techniques and to develop proficiency in applying these techniques in a modern programming language (Python).