Data Science Related Courses

Introduction to basic concepts in bioinformatics. Standard bioinformatic applications.

In this course, students learn theory and concepts of bioinformatics algorithms that are used in the field. They gain experience implementing these algorithms and applying them.

The overall purpose of this course is to help students develop skills in bioinformatics research techniques, apply these techniques to primary-research questions, and make well-supported scientific conclusions that link basic biology and computational techniques.

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

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.

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

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

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.

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

Machine learning models and other mechanisms allowing computers to learn and find knowledge from data.

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.

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).

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

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 field of data science, with a focus on analytics, using the most popular programming language in the discipline. Topics include introductions to: 1) Python programming, 2) data vizualization, 3) bivarate statistics and multivariate statistics, and 4) data-driven business decision-making.

Students will 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 and bivariate analyses. Students will also learn core data cleaning and automation techniques relevant to modern data analytics.

Machine-learning techniques in Python for predicting numeric and categorical outcomes, clustering and segmentation, and pipelining for data products.

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.

Prepare for Exam SRM and CAS's Exam MAS-I. Understand the basics of several important analytical tools including regression models, generalized linear models, regression trees, time series models, principal component analysis and clustering. Use these models to estimate parameters, determine importance of key variables, perform model selection, perform prediction, and understand key characteristics of the data.

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.

Theory of the Gaussian Linear Model with applications to illustrate and complement the theory; random vectors, multivariate normal, central and non-central chi-squared, t, F distributions; distribution of quadratic forms; Gauss-Markov Theorem; distribution theory of estimates and standard tests in multiple regression and ANOVA models; regression diagnostics; parameterizations and estimability; model selection and its consequences.

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

Fixed effects, random effects, repeated measures, nonindependent data, general covariance structures, estimation methods.

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.

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