A foundations course in data science, emphasizing both concepts and techniques. The course provides an overview of data analysis tasks and the associated challenges, spanning data preprocessing, model building, model evaluation, and visualization. The major areas of machine learning, such as unsupervised, semi-supervised and supervised learning are covered by data analysis techniques including classification, clustering, association analysis, anomaly detection, and statistical testing. The course includes a series of assignments utilizing practical datasets from diverse application domains, which are designed to reinforce the concepts and techniques covered in lectures. A substantial project related to one or more data sets culminates the course.

A first course introducing students to the fundamentals of computational problem solving. Students will learn a systematic approach to problem solving, including how to frame a problem in computational terms, how to decompose larger problems into smaller components, how to implement innovative software solutions using a contemporary programming language, how to critically debug their solutions, and how to assess the adequacy of the software solution. Additional topics include an introduction to object-oriented programming and data structures such as arrays and stacks. Students will complete both in-class and out-of-class assignments.

A second course that delves further into computational problem solving, now with a focus on an object-oriented perspective. There is a continued emphasis on basic software design, testing & verification, and incremental development. Key topics include theoretical abstractions such as classes, objects, encapsulation, inheritance, interfaces, polymorphism, software design comprising multiple classes with UML, data structures (e.g. lists, trees, sets, maps, and graphs), exception/error handling, I/O including files and networking, concurrency, and graphical user interfaces. Additional topics include basic software design principles (coupling, cohesion, information expert, open-closed principle, etc.), test driven development, design patterns, data integrity, and data security.

Rapidly expanding volumes of data from all areas of society are becoming available in digital form. High value information and knowledge is embedded in many of these data volumes. Unlocking this information can provide many benefits, and may also raise ethical questions in certain circumstances. This course provides students with a hands-on introduction to how interactive data exploration and data mining software can be used for data-driven knowledge discovery, including domains such as business, environmental management, healthcare, finance, and transportation. Data mining techniques and their application to large data sets will be discussed in detail, including classification, clustering, association rule mining, and anomaly detection. In addition, students will learn the importance of applying data visualization practices to facilitate exploratory data analysis.