Engineering Sciences (Data Science) MS

EAS 502 Introduction to Probability Theory for Data Science

This course provides basic background on probability theory at a beginning graduate level. Topics include introductory probability concepts, discrete and continuous random variables and probability distributions, joint probability distributions, random sampling and data description, point estimation of parameters, random variables, derived probability distributions, discrete and continuous transforms and random incidence. As time permits, the course introduces elementary stochastic processes including Bernoulli and Poisson processes.

EAS 501 Introduction to Numerical Mathematics for Computing and Data Scientists

The aim of this course is:

• To develop the ability to formulate and solve problems using mathematical methods and tools
• To apply knowledge gained in lower level mathematics courses
• To introduce concepts and methods of linear algebra
• To introduce a broad range of numerical methods
• To develop an ability to identify, understand, and solve algebraic equations
• To develop an ability to identify, understand, and solve differential equations
• To develop experience with numerical and symbolic mathematical software and their use in problem solving

EAS 595 Statistical Learning and Data Mining I

An introduction to the mathematical theory and computational methodology at the heart of statistical learning.  Using a Bayesian paradigm, this first semester considers supervised learning, including topics of classification - support vector machines, k-nearest neighbors, Naive Bayes, logistic regression, tree methods and forests, bagging and ensemble methods – as well as Gaussian processes and neural networks, and methods for validation and testing. The R programming language will be used. Students will develop a facility for statistical learning of data; students will become proficient in writing computer code to analyze datasets and draw conclusions from analysis.

This course has both a traditional lecture component, as well as an online computational lab component; labs will be run approximately every third week.

EAS 503 Programming and Database Fundamentals for Data Scientists

This course introduces students to computer science fundamentals for building basic data science applications. The course has two parts. The first part covers the fundamentals of programming with Python and Python libraries for data manipulation, visualization, and machine learning. The second part covers database design and use of databases in applications.

EAS 596 Statistical Learning and Data Mining II

An introduction to the mathematical theory and computational methodology at the heart of statistical learning.  Using a Bayesian paradigm, this second semester considers unsupervised learning, including dimension reduction, clustering, Gaussian mixtures methods, graph models, and model averaging. The course will examine parametric and non-parametric regression, including Gaussian Process regression. The R programming language will be used. Students will develop a facility for statistical learning of data; students will become proficient in writing computer code to analyze datasets and draw conclusions from analysis.

This course has both a traditional lecture component, as well as an online computational lab component; labs will be run approximately every third week.

CSE 574 Intro to Machine Learning

Involves teaching computer programs to improve their performance through guided training and unguided experience. Takes both symbolic and numerical approaches. Topics include concept learning, decision trees, neural nets, latent variable models, probabilistic inference, time series models, Bayesian learning, sampling methods, computational learning theory, support vector machines, and reinforcement learning.

CSE 560 Data Models Query Language

The course focuses on the issues of data models and query languages that are relevant for building present-day database applications. The following topics are addressed: Entity-Relationship data model, relational data model, relational query languages, object data models, constraints and triggers, XML and Web databases, the basics of indexing and query optimization.

EAS 504 Applications of Data Science: Industry Overview

This course will provide students with an overview of data driven analytics in different industry sectors. The class will have a series of visiting lecturers with the faculty member teaching the class providing overview, continuity and grading of homework and term papers.

EAS 560 Data Science Project

This course will provide students with a final integrative project experience. The class will require students to obtain an integrative project experience either in industry or at the university. In either case the students will use the skills acquired during the other classes in executing project goals. Students will provide short reports to supervising faculty to ensure that learning objectives are being met.

Two out of the following courses can be selected as electives.

CSE 531 Algorithms Analysis + Design

CSE 535 Information Retrieval

CSE 546 Reinforcement Learning

CSE 562 Database Systems

CSE 573 Computer Vision             

CSE 586 Large-Scale Distributed Systems

CSE 587 Data Intensive Computing

CSE 601 Data Mining for Bioinformatics  

CSE 633 Parallel Algorithms

CSE 635 Multimedia Information Retrieval

CSE 636 Data Integration

CSE 676 Deep Learning
_{**Students must have successfully completed CSE 574 before taking CSE 676. Cannot be taken in the same semester as CSE 574.}

CSE 740 Machine Learning and Big Data

CSE 674 Advanced Machine Learning

STA 517 Categorical Data Analysis

STA 567 Bayesian Statistics

CDA 609 High Performance Computing

IE 575 Stochastic Methods

IE 535 Human Computer Interaction

EE 634 Principles of Information Theory and Coding

MTH 558/559 Mathematical Finance