Course Syllabus

Big data is everywhere. A fundamental goal across numerous modern businesses and sciences is to be able to exploit as many machines as possible, to consume as much information as possible and as fast as possible. The big challenge is how to turn data into useful knowledge. This is far from a simple task and also a moving target as both the underlying hardware and our ability to collect data evolve. In this course, we discuss how to design data systems and algorithms that can scale up and scale out. Scale up refers to the ability to use a single machine to all its potential, to exploit properly the memory hierarchy and the multiple CPU and GPU cores of modern hardware. Scale out refers to the ability to use more than one machine (typically hundreds or thousands) effectively. This is a research-oriented course. Every week we read two modern research papers; one from the scale up area and one from the scale out area. We use examples from several areas, including relational systems and distributed databases, graph processing systems (for social networks), key-value stores, noSQL and newSQL systems, as well as mobile computing. Each student works on two systems projects and (optionally) on a semester-long data systems research project which can be in any of the above areas and based on an open research question. The recorded lectures are from the Harvard John A. Paulson School of Engineering and Applied Sciences course Computer Science 265.

For detailed information, syllabus and other material please check the class website:

http://daslab.seas.harvard.edu/classes/cs265/