Data Science Related Courses

This course is a broad, interdisciplinary look at the field of data science, and how to derive insight from data. It will develop technical skills (including some python programming, statistics, linear algebra, machine learning, data cleaning and visualization) as well as data literacy (mental frameworks for decomposing data science problems, critical thinking about potential conclusions of an analysis, and potential pitfalls of overreliance on unreliable data).

Advanced software development with an object-oriented focus. Design, implementation, and testing of medium-sized programs including a server program.

Understand the fundamental models of machine learning, such as neural networks, decision trees, data mining, clustering, Bayesian learning, ensembles, reinforcement learning, and deep learning. Work with data and machine learning tools in real world applications.

Optimization, problem formulation, and solution algorithms, including simplex and interior point methods. Applications from control, data mining, finance, game theory, learning, network flow, operations research, and statistical estimation.

Concepts and terminology of information retrieval (IR) systems. Design methodologies and issues. Fundamental IR models examined: Boolean, Vector Space, Probabilistic models, and evaluation strategies.

Introduction to core areas of artifical intelligence; intelligent agents, problem solving and search, knowledge-based systems and inference, planning, uncertainty, learning, and perception.

This class introduces students to basic methods and development platforms for Voice User Interfaces (VUI's) including Google Actions, Amazon Alexa Skills, and others. Students will learn principles of effective VUI design, discover the key ways in which voice interfaces differ from visual or menu-based interfaces, and delve into back-end mechanics of Automatic Speech Recognition, Intent Detection, Slot Filling, Dialog State Tracking, Text Generation, and Knowledge Representation.

Understand the fundamental models of machine learning, such as neural networks, decision trees, data mining, clustering, Bayesian learning, ensembles, reinforcement learning, and deep learning. Work with data and machine learning tools in real world applications.

Theory and practice of modern deep learning and associated software frameworks. A broad look at the field, drawing on material from machine vision, machine translation, dynamical systems, audio processing, neural computing and human perception. Supporting mathematical concepts are also covered, including linear algebra, stochastic optimization, and hardware acceleration.

Culminating experience based on skills learned in advanced technical courses. Students work in teams to plan, design, implement, test, and demonstrate a major project.

Culminating experience based on skills learned in advanced technical courses. Students work in teams to plan, design, implement, test, and demonstrate a major project.

Introduction to the analysis and design of feedback systems guaranteed to perform well in spite of model uncertainty.

Mathematical, computational, and philosophical foundations of machine learning, control, and physical modeling. Introduction to system identification, causality, uncertainty, model approximation, and information geometry.

IR modeling, IR query languages, text indexing and searching, retrieval evaluation, query and text operations, parallel and distributed IR, Web searching.

In-depth examination of the mathematical foundations of modern deep learning, surveying current research in the area. Topics include stochastic and distributed optimization, regularization, initialization, network architecture design, and loss function design. Concepts are developed in the context of various application areas including supervised learning, generative modeling, and reinforcement learning.

Learn the python programming language in the Jupyter Notebook format. Using that skill,
learn to create the statistics and visualizations required for univariate and bivariate analyses. Also learn core data cleaning and automation techniques relevant to modern data analytics.

Create all types of machine learning models including regression, classification, clustering, forecasting, and text modeling. Learn automation techniques for machine learning pipelines.

Learn the python programming language in the Jupyter Notebook format. Using that skill, students will learn to create the statistics and visualizations required for univariate, bivariate, and limited multivariate analyses. Learn core data cleaning and automation techniques relevant to modern data analytics.

Create all types of machine learning models including regression, classification, clustering, forecasting, and text modeling. Learn automation techniques for machine learning pipelines. This course is for graduate students.

Intensive overview of natural language processing, including computational techniques, hands-on experience with linguistic technologies and corpora, language modeling approaches, and readings from current research.

Mathematical aspects of data science, including high-dimensional geometry and linear algebra, optimization, and probabilistic modeling.

Theory of probability and stochastic processes, emphasizing topics used in applications. Random spaces and variables, probability distributions, limit theorems, martingales, diffusion, Markov, Poisson and queuing processes, renewal theory, and information theory.

First course in mathematical statistics, focusing on mathematical aspects. Topics include estimation, inference, analysis of variance, regression, multivariate statistics, Bayesian statistics, state estimation, Kalman filtering, time series, GARCH models.

Numerical matrix algebra, orthogonalization and least squares methods, unsymmetric and symmetric eigenvalue problems, iterative methods, advanced solvers for partial differential equations.

Contact the math department for course information.

Mathematics necessary to understand how deep neural networks are formulated and designed. Analyzing the stability, generalizability, and potential extension of neural networks to new datasets.

Representing networks mathematically. Measures and metrics, computer algorithms, random graphs, and large-scale structures. Percolation and network resilience. Dynamical systems on networks.

An experiential learning physics-based approach to data science that includes designing physical experiments, collecting data, and investigating uncertainties, causality and correlation in datasets while developing cross-discipline technical communication skills.

Mathematical, computational, and philosophical foundations of machine learning and its dual problem, control. Introduction to system identification, causality, uncertainty, model approximation, and information geometry.

Graphical displays and numerical summaries, data collection methods, probability, sampling distributions, confidence intervals and hypothesis testing involving one or two means and proportions, contingency tables, correlation and simple linear regression.

Statistical thinking, basic probability and random variables, estimation, uncertainty in estimation, inference and interpretation for the linear model, prediction, model comparison, introduction to Bayesian statistics, binary data, evaluating classification models, data ethics and privacy.

Scientific method, statistical thinking, sources of variation, completely randomized design, ANOVA, power and sample size considerations, multiple testing, randomized complete blocks, factorial designs, interactions. Introduction to statistical software.

Set theory; probability; principles of counting; random variables; mathematical expectation; sampling distributions; point estimation.

R programming skills; data cleaning and wrangling in R; introductory statistical analysis and graphics; simulation of introductory statistical concepts.

The scientific method; conditional probability; Bayes' Theorem; conjugate distributions: Beta-binomial, Poisson-gamma, normal-normal; Gibbs sampling.

Introduction to the basics of the Linux operating system. Command line interface is used to explore Linux/Unix utilities and tools. Fundamental concepts of python programming. Create and run python scripts from the command line. Introduction to database management systems, the basic structure of relational databases, and how to manage databases with Structured Query Language (SQL). Read and write simple and complex SQL statements. Integrate R, Python and SQL skills.

Regression, transformations, residuals, indicator variables, variable selection, logistic regression, time series, observational studies, statistical software.

Utilize statistical and machine learning analytical tools such as regression models, regression trees, time series models, nearest neighbors and neural networks to perform prediction tasks.

Principles of data science; web scraping; data wrangling; exploratory data analysis; data visualization; ethics; version control; data communication.

IID regression, heterogenous variances, SARIMA models, longitudinal data, point and areally referenced spatial data.

Supervised and unsupervised learning; model evaluation; recommendation systems; natural language process and unstructured data; deep learning.

Bayesian analogs of t-tests, regression, ANOVA, ANCOVA, logistic regression, and Poisson regression implemented using Nimble, Stan, JAGS and Proc MCMC.

Multiple linear regression, nonlinear regression, local regression, penalized regression, generalized additive models, logistic regression, discriminant analysis, tree-structured regression, support vector machines, neural networks.