Data Science (DSC)
DSC 381 Probability and Simulation-Based Inference for Data Science 3 Hours
3 Lecture Hours 0 Lab Hours
Introduction to inference, through the simulation process. Explore probability, exponential families, conditional probabilities and Bayes theorem, inference and Maximum Likelihood estimation, confidence intervals, and hypothesis testing (emphasis on simulation). The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
DSC 382 Foundations of Regression and Predictive Modeling 3 Hours
3 Lecture Hours 0 Lab Hours
Introduction to the basics of regression-based modeling. Explore simple and multiple regression, interpretation of models and coefficients, prediction and estimates, regularization processes, and generalized linear models. The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing and Data Science 381.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
DSC 383 Advanced Predictive Models for Complex Data 3 Hours
3 Lecture Hours 0 Lab Hours
Explore advanced techniques used in practice for regression-based models. Examine time series and longitudinal data, repeated and mixed models, spatially correlated data, and Random Forest models. The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing; Data Science 382 and 388G.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
DSC 384 Design Principles and Causal Inference 3 Hours
3 Lecture Hours 0 Lab Hours
Explore the field of "big data" and the rigors of determining applicable design structures from that data. Examine classic design structures, non-typical data structures and novel design processes, and causal inference, and explore data-based decision making. The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing and Data Science 381.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
DSC 385 Data Exploration, Visualization, and Foundations of Unsupervised Learning 3 Hours
3 Lecture Hours 0 Lab Hours
Examine visualization techniques used in practice to discover insights about data. Explore data quality and relevance, data ethics and providence, clustering, dimension reduction, and reproducibility. The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing and Data Science 388G.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
DSC 387 Topics in Statistics for Data Sciences 3 Hours
3 Lecture Hours 0 Lab Hours
Explore topics in data science with a general overview of statistics theory and application. The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Master's
DSC 388 Natural Language Processing 3 Hours
3 Lecture Hours 0 Lab Hours
Explore computational methods for syntactic and semantic analysis of structures representing meanings of natural language, the study of current natural language processing systems, and methods for computing outlines and discourse structures of descriptive text. Only one of the following may be counted: Artificial Intelligence 388, Computer Science 388, Data Science 388, 395T (Topic: Natural Language Processing). Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
DSC 388G Algorithms: Techniques and Theory 3 Hours
3 Lecture Hours 0 Lab Hours
Explore algorithm design and analysis including algorithmic paradigms, maximum flow, randomized algorithms, data structures, NP-completeness and approximation algorithms. Data Science 388G and Computer Science 388G may not both be counted. The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
DSC 388J Optimization 3 Hours
3 Lecture Hours 0 Lab Hours
Explore a background on convex sets and functions, linear programming, convex programming, and iterative first-order and second-order methods. Data Science 388J and 395T (Topic: Optimization) may not both be counted. The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing and Data Science 388G.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
DSC 389 Data Structures 3 Hours
3 Lecture Hours 0 Lab Hours
Examine programming skills, including testing, debugging, and the basics of programming methodology. Explore fundamental concepts in data structures and algorithms. Data Sciences 389 and 395T (Topic: Data Structures) may not both be counted. The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
DSC 391L Principles of Machine Learning 3 Hours
3 Lecture Hours 0 Lab Hours
Examine computing systems that automatically improve their performance with experience, including various approaches to inductive classification such as version space, decision tree, rule-based, neural network, Bayesian, and instance-based methods; as well as computational learning theory, explanation-based learning, and knowledge refinement. Only one of the following may be counted: Artificial Intelligence 391L, Computer Science 391L, Data Science 391L. The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing and Data Science 382.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
DSC 394D Deep Learning 3 Hours
3 Lecture Hours 0 Lab Hours
Explore the basic building blocks and intuitions behind designing, training, tuning, and monitoring of deep networks. Examine both the theory of deep learning, as well as hands-on implementation sessions in pytorch. Explore a series of application areas of deep networks in: computer vision, sequence modeling in natural language processing, deep reinforcement learning, generative modeling, and adversarial learning. Only one of the following may be counted: Artificial Intelligence 394D, Computer Science 394D, Data Science 394D, 395T (Topic: Deep Learning). The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Data Science 381 and 382.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
DSC 394R Reinforcement Learning 3 Hours
3 Lecture Hours 0 Lab Hours
Introduction to the theory and practice of modern reinforcement learning, with emphasis on temporal difference learning algorithms. Only one of the following may be counted: Artificial Intelligence 394R, Computer Science 394R, Data Science 394R, 395T (Topic: Reinforcement Learning). The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
DSC 395T Topics in Computer Science for Data Sciences 3 Hours
3 Lecture Hours 0 Lab Hours
Explore topics in data science with a general overview of computer science application. The equivalent of three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing and Data Science 381.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Master's