This course provides an overview of the theory and practice of database management. It covers data modeling, database design, relational theory, the SQL language, and properties of relational database systems. While relational database is the main focus, the class will also compare relational databases to non-relational databases from design and usage perspective.

Graph theory is the study of networks of objects with some defined relationship (people and their social relationships, websites and web links, towns and roads). In the case of networks that come from real-world data, techniques from graph theory can help us make conclusions about our data and its structure. This course will examine some of these techniques by applying them to real data using modern software tools for network analysis. We will also cover more generally how computers store and use graph-structured data, and how this can be done efficiently. Prerequisites: MA201. Some Linear Algebra experience encouraged but not required - we will review any necessary Linear Algebra.

Introduction to machine learning and its applications. Topics include artificial neural networks, deep learning, support vector machines, clustering, computer vision, and natural language processing.