Information and Data Sciences
The information and data sciences now draw not just upon traditional areas spanning computer science, applied mathematics, and electrical engineering - signal processing, information and communication theory, control and decision theory, probability and statistics, algorithms but also a range of new contemporary topics such as machine learning, network science, distributed systems, and neuroscience. The result is an area that is new, fundamentally different that related areas like computer science and statistics, and that is crucial to modern applications in the physical sciences, social sciences, and engineering. The Information and Data Science option is unabashedly mathematical, focusing on the foundations of the information and data sciences, across its roots in probability, statistics, linear algebra, and signal processing. These fields all contribute crucial components of data science today.
In addition to a major, the IDS option offers a minor that focuses on the mathematical foundations of the information and data sciences but recognizes the fact that many students in other majors across campus have a need to supplement their options with practical training in data science.
IDS Coursework at a Glance
CS/IDS 178. Numerical Algorithms and their Implementation. This course gives students the understanding necessary to choose and implement basic numerical algorithms as needed in everyday programming practice. Concepts include: sources of numerical error, stability, convergence, ill-conditioning, and efficiency. Algorithms covered include solution of linear systems (direct and iterative methods), orthogonalization, SVD, interpolation and approximation, numerical integration, solution of ODEs and PDEs, transform methods (Fourier, Wavelet), and low rank approximation such as multipole expansions.
Humans can't deal directly with the tremendous volume of data that we are currently collecting. It holds endless potential but also presents a huge challenge. If we are going to make sense of big volumes of data, we need to create new automated pipelines for processing it.