Elective Requirements
In order to graduate you should plan to take at least four (4) full-credit/3-credit semester electives, and a minimum of 12 credits of electives overall. Courses that count as electives MUST be graded (e.g. not Credit/No Credit, and not audited) and must be at least 2-credits.
Courses must have a clear connection to your data science curriculum – if in doubt, please ask MIDS program staff.
Fall Year 1 Elective Suggestions
In the past, Introduction to Natural Language Processing (IDS 703) was a required course in the Fall of Year 1. Over the past several years, however, many students — especially those with less prior programming training — were finding the semester overwhelming, in large part because of the growing complexity of the material in Natural Language Processing (NLP). With that in mind, starting in 2024 we are encouraging most students to take NLP in Fall of Year 2 after getting more experience with machine learning and Python programming.
This change makes it possible for some students to take an additional elective their first semester. However, in order to avoid replacing one problem with another, we strongly encourage students who do not feel prepared to take NLP in the Fall of Year 1 to limit their choice of electives to a smaller set of classes that are likely to be supportive of the other courses they are taking. In particular, we suggest students consider the following classes:
MATH 730: Probability
Probability models, random variables with discrete and continuous distributions. Independence, joint distributions, conditional distributions. Expectations, functions of random variables, central limit theorem. An assignment will ask the student to relate this course to their research.
MATH 780: Calculus and Probability
Introduction to calculus of real-valued functions with an emphasis on applications to probability. Topics include an introduction to elementary functions, differentiation and applications, integration, and continuous probability distributions.
MATH 712: Multivariable Calculus
Partial differentiation, multiple integrals, and topics in differential and integral vector calculus, including Green’s theorem, the divergence theorem, and Stokes’s theorem.
MATH 718: Matrices and Vector Spaces
Solving systems of linear equations, matrix factorizations and fundamental vector subspaces, orthogonality, least squares problems, eigenvalues and eigenvectors, the singular value decomposition and principal component analysis, applications to data-driven problems.