Big Data is an evolving field, whose definition is fluid, and will continue to evolve over the years. Thus, the core of our educational approach is a comprehensive interdisciplinary, multifaceted practical training program.
Core Curriculum
An integrated, multidisciplinary set of courses that prepare the students in the algorithmic, statistical, systems, and scientific aspects of Big Data. This curriculum is an overlay on top of the requirements of the participating departments in a manner that is specific to each department. Please contact the faculty liaisons for detailed information.
Three out of four of the following core courses:
CSE 544 – Data Management. This course focuses on how to use data management systems and how to build them, including recent advances in the field.
- Basic knowledge of data structures (e.g., tree structures)
Background course = CSE 326. - Basic knowledge of the operating system
- Comfortable programming in Java
Background course = CSE 143.
CSE 546/STAT 535 – Foundational Machine Learning
- Linear algebra (eigenvectors, eigenvalues, solving linear systems).
Background course = MATH 318 or 308. - Familiarity with multivariate calculus (partial derivatives, multiple integrals).
Background course = MATH 324. - Fundamental ideas of probability
Background course = STAT 391 or STAT 394-395. - Comfort with basic programming in Java, Python, or R
Background course = CSE 143.
CSE 512 – Data Visualization
- Basic programming expertise; familiarity with or willingness to learn a high-level programming language like Python or JavaScript.
Background course = CSE 143. - Comfort with fundamental data structures and algorithms.
Background course = CSE 332 or CSE 373. - Familiarity with fundamentals of (one or more of) interaction design, computer graphics, statistics, databases or natural language processing a plus, but by no means required.
STAT 509 or STAT 512-513 (a more in-depth version)
- Linear algebra (eigenvectors, eigenvalues, positive definite matrices).
Background course = MATH 318 or 308. - Familiarity with multivariate calculus (partial derivatives, multiple integrals, Jacobians).
Background course = MATH 324. - Fundamental ideas of probability.
Background course = STAT 394-395, or possibly STAT 391. - Familiarity with basic statistical inference (hypothesis tests, estimators, confidence intervals) a plus. Background course = STAT 311.
Additionally, to further expand students’ education and create a campus-wide community, students register for at least 4 quarters in the weekly “Data Science Seminar”, ENGR 591.
Steering Committee
Ginger Armbrust, Oceanography
Magdalena Balazinska, Computer Science & Engineering
David Beck, Chemical Engineering
Andrew Connolly, Astronomy
Tom Daniel, Biology
Ioana Dumitriu, Mathematics
Ione Fine, Psychology
Emily Fox, Statistics
Carlos Guestrin, Computer Science & Engineering
William Noble, Genome Sciences