This course is an introduction to data mining, the area of computer science that deals with the development of efficient and accurate algorithms for extracting information from data.  Topics may include: mining data streams and time series, the MapReduce/Spark model and large scale data analysis, significant patterns extraction, web and social networks analysis, recommendation systems, sampling and hypothesis testing, and dimensionality reduction.

Probability is everywhere in computer science. In networks and systems, it is a key tool that allows us to predict performance, to understand how delay changes with the system parameters, and more. In algorithms, randomization is used to design faster and simpler algorithms than their deterministic counterparts. In machine learning, probability is central to the underlying theory. This course provides an introduction to probability with a focus on computer science applications.

A fundamental problem in computer science is that of organizing data so that it can be used effectively. This course introduces basic data structures and their applications. Major themes are the importance of abstraction in program design and the separation of specification and implementation. Program correctness and algorithm complexity are also considered. Data structures for lists, stacks, queues, dictionaries, sets, and graphs are discussed. This course will provide advanced programming experience. It is not open to students who have previously taken COSC 201.

A continuation of COSC 111. This course will emphasize more complicated problems and their algorithmic solutions. The object-oriented programming paradigm will be discussed in detail, including data abstraction, inheritance, and polymorphism. Other topics will include stacks, queues, linked lists, programming for graphical user  interfaces, and basic topics in probability. A laboratory section will meet once a week to give students practice with programming constructs.

A continuation of COSC 111. This course will emphasize more complicated problems and their algorithmic solutions. The object-oriented programming paradigm will be discussed in detail, including data abstraction, inheritance, and polymorphism. Other topics will include stacks, queues, linked lists, programming for graphical user  interfaces, and basic topics in probability. A laboratory section will meet once a week to give students practice with programming constructs.

This research colloquium will explore the lawsuit alleging anti-Asian American admissions discrimination as a result of affirmative action policies currently pursued by Harvard College. Students will do background readings on the history of affirmative action and explore several of the major lawsuits that attempted to dismantle the policy. The focus of the semester will be on the current lawsuit: its background, principals, allegations, and directions.