Note: prerequisites are removed in favor of higher-level coursework in most cases.
Data Science
- Intro to Big Data Systems (CS544)
- gRPC, Docker, Hadoop FS, Kafka, MySQL, Spark, HBase, Cassandra
- Intro to Artificial Intelligence (CS540)
- GANs, CNNs, RNNs, supervised vs unsupervised, Q-learning, A*.
- Numerical Linear Algebra (CS513)
- QR, LU, Schur decomposition, Krylov spaces, GMRES, power method.
- Data Science Programming II (CS320)
- Selenium, Sci-Kit Learn, PyTorch, Tensorflow, Pandas, NumPy, SQL
- Data Science Modelling II (STAT340)
- Monte Carlo, Bayesian inference, ANOVA, bootstrap, cross-validation.
- Applied Multivariate Analysis (STAT456)
- Factor analysis, LDA and QDA, multivariate Gaussian mixtures, PCA.
Computer Science
- Programming III (CS400)
- Inheritance and polymorphism, Git, GUI, data structures.
- Machine Organization and Programming (CS354)
- Virtual address space and memory, the heap and stack, C.
- Intro to Algorithms (CS577)
- Greedy, Divide and Conquer, Dynamic Programming, Network Flow, P vs NP
- Accelerated Honors Computer Graphics (CS559)
- Curves as b-splines, quaternions, 3D scaling, web graphics, GPUs
- Intro to Operating Systems (CS537)
- I/O devices, batch processing and threading, virtual address translation
Other
- Computer Science Education (CS502)
- CS educational pedagogy, practical experience through tutoring students.
- Intro to Geographical Information Systems (GEOG377)
- ArcGIS, vectorization and rasterization, mapping techniques.
- Data and Algorithms: Ethics and Policy (LIS461)
- Ethical, legal and policy issues related to analytics and algorithms