Graduate Data Science Courses

CIS431 Modern Applied Statistical Learning (3 credits) Fall

This course is designed to provide students with hands-on, practical experience in statistical learning methods such that they can apply them to solve real-world problems. Students enhance their understanding of statistical analysis and inference while getting trained on industry-standard software packages. Prerequisite: None

CIS441 Cloud Computing and Big Data (3 credits) Fall

In this course, students will learn cloud computing concepts using cloud infrastructure provided by the largest cloud vendors, Amazon (AWS) and Microsoft (Azure). Students will also learn Big Data concepts, including databases, relational and non-relational databases, SQL, etc. Finally, students will get some hands-on experiences with cloud computing and Big Data technologies. Prerequisite: None

CIS536 Applied Machine Learning (3 credits) Spring

This is a required course for the MS in Data Science program. It extends certain topics of CIS431 Modern Applied Statistical Learning and focuses on the theoretical basis as well as applications of the state-of-the-art machine learning algorithms. Students will get familiar with Python machine learning tools and use them for projects. Prerequisite: CIS431

CIS543 Computer Vision and Natural Language Processing (3 credits) Fall

This course covers advanced topics on the latest developments in machine learning, focusing on the application of deep neural networks (deep learning) to computer vision and natural language processing. Students will become familiar with Python deep learning frameworks like TensorFlow and Pytorch and be able to use them for projects. Prerequisite: CIS536

STA401 Regression Analysis (3 credits) Fall

This course covers topics including simple and multiple linear regression models, logistic, autocorrelation and nonlinear regression, inference about model parameters and predictions, diagnostic and remedial measures about the model, independent variable selection, and multicollinearity. Students will understand the principles for applied regression model-building techniques in various fields of study. Prerequisite: None

DAS421 Sample Survey and Customer Analytics (3 credits) Fall

This course will introduce students to the methods, tools and techniques of survey sampling, survey designs, and marketing analytics and will demonstrate how to practically apply these analytics to real-world business decisions. Hands-on experience with various analytical tools and software is a key component of the course. Prerequisite: None

DAS422 Exploratory Data Analysis and Visualization (3 credits) Spring

In this course students will learn techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science. R and other statistics applications (such as Python) are used. The course is designed for both students interested in applying visualization in their work, and students interested in building better visualization tools and systems. Prerequisite: None

DAS441 Data Mining for Business (3 credits) Spring

This course seeks to equip students with a solid understanding of opportunities, techniques, and critical challenges in using data mining and predictive modeling in a business setting. The focus is to enable students to develop the ability to translate business challenges into data mining problems and apply predictive modeling technologies to improve business decisions. Prerequisite: None

DAS561 Capstone Project (3 credits) Fall

Students are required to take this capstone course in their final semester of the Data Science Master program. Students will use Python, R, and/or other specialized analysis tools to synthesize concepts from data analytics and visualization as applied to industrial problems. Instructed by a faculty mentor, students will develop comprehensive problem-solving capabilities in data science from problem definition stage through the delivery of a solution through this capstone project. Prerequisite: Department approval

STA411 Statistical Inference (3 credits) Fall

This course will introduce the underlying theories and methods of statistical data display, analysis, inference, statistical decision-making, and ANOVA. The course will cover topics including basic concepts of probability, maximum likelihood estimation, sufficiency, completeness, ancillary, unbiasedness, consistency, efficiency, asymptotic approximations, ANOVA, and regression. Prerequisite: None

STA421 Design and Analysis of Experiments (3 credits) Spring

In this course students learn how to use the methods of statistical design of experiments (DOE) in order to design efficient experiments, analyze results correctly and present them in a clear fashion. Statistical DOE is used widely in both industry and academia. Graduate and undergraduate students from any field of science or engineering can use the methods learned in the course in their projects and research. Prerequisite: None

STA441 Survival Analysis (3 credits) Spring

This course introduces basic concepts and methods for analyzing survival time data obtained from following individuals until occurrence of an event or their loss to follow-up. Students will learn the characteristics of survival (time to event) data and building the link between distribution, survival, hazard functions, non-parametric, semi-parametric, and parametric models, and two-sample test techniques. During the class, students will also learn how to use R to analyze survival data. Prerequisite: STA411

STA531 Multivariate Analysis (3 credits) Fall

This course will introduce and explore multivariate data and its related inference techniques. It will cover topics including advanced linear algebra, multivariate normal distribution, principal components, factor analysis, discriminant function, cluster analysis, Hoteling’s T2 and MANOVA. This course helps students develop and sharpen their mathematical and statistical skills by practicing the statistical techniques in an applied context. Prerequisite: STA411

STA535 Bayesian Analysis (3 credits) Spring

This course will introduce Bayesian statistical inference. It will cover priors, posteriors, Bayes rule, Bayesian inference for one and two parameter problems, Bayesian testing and model diagnostics, Bayesian computation, hierarchical Bayesian methods, and model comparisons. Prerequisite: STA411

STA545 Nonparametric Statistics (3 credits) Spring

Students will learn the applications of nonparametric statistical methods rather than mathematical development. The basic concepts in nonparametric analysis will be introduced, as well as computational and computer competence, in applied nonparametric statistics. Topics include paired and independent samples, structured data, survival analysis, linear and logistic regression, categorical data, and robust estimation. These new methodologies are examined and applied to simulated and real datasets using R. Prerequisite: STA411

MS in Data Science  ›  Graduate Data Science Courses