The recent explosion of social media and the computerization of every aspect of economic activity resulted in creation of large volumes of mostly unstructured data: web logs, videos, speech recordings, photographs, e-mails, Tweets, and similar. In a parallel development, computers keep getting ever more powerful and storage ever cheaper. Today, we have the ability to reliably and cheaply store huge volumes of data, efficiently analyze them, and extract business and socially relevant information. This course introduces you to several key IT technologies that you will be able to use to manipulate, store, and analyze big data. This course provides an in-depth coverage of special topics in big data from data generation, storage, management, transfer, to analytics, with focuses on the state-of-the-art technologies, tools, architectures, and systems that constitute big-data computing solutions in high-performance networks. Real-life bigdata applications in various domains (particularly in sciences) are introduced as use cases to illustrate the development, deployment, and testing of a wide spectrum of emerging big-data solutions. Also we will focus on data mining and machine learning algorithms for analyzing very large amounts of data or Big data.

The course material will be drawn from textbooks as well as recent research literature. The following topics will be covered this year: Hadoop, Mapreduce, Association rules, Large scale supervised machine learning, Data streams, Clustering, NoSQL systems (Cassandra, Pig, Hive), and Applications including recommendation systems, Web and security.