Data Science Course Descriptions

  • Undergraduate Level Courses

CIS102 Introduction to Computing (3 credits) Fall

This course emphasizes programming methodology, procedural abstraction, an introduction to object-oriented programming in Python, as well as an integrated lab component of hands-on lab experiences conducted during lectures and integrated throughout the course. Prerequisite: None

 

CIS103 Web Development (3 credits) Spring

This course focuses on concepts and programing skills for web application development. It starts with the front-end by an introduction to networking basics, HTML, CSS, and JavaScript. In the second half of course, students learn the back-end programing and APIs. Students will build style interactive sites as projects. Prerequisite: CIS102

CIS104 Essentials for Software Development in Data Science (3 credits) Spring

This course is a hands-on lab computer classes which helps students to understand the full stack software development process, recognize the basic hardware and networking infrastructure for software development, get familiar with certain useful tools for software development, set up local development environment on a laptop/desktop, develop web software application using Python flask framework for data science, and deploy and run a web application in Amazon AWS cloud computing environments.

CIS105 Data Structure and Algorithms (3 credits) Spring

This course focuses on program design, analysis, and verification with an introduction to the study of data structures and algorithm design that are important in the construction of sophisticated computer programs. Topics include algorithm complexity analysis, elementary data structures, (including arrays, stacks, queues, and lists), advanced data structures (including hashes, trees, and graphs), their implementation, algorithms used to manipulate these structures, and their application to solving practical data science problems. Prerequisite: CIS102

CIS121 Java Programming (3 credits) Fall

This course emphasizes on the main principles of object-oriented software design and programming with Java. Students also learn how to use Java library packages and classes, as well as selecting appropriate algorithms and data structures to solve a given problem. With integrated labs and projects, students have opportunities to practice skills in designing object-oriented software solutions to problems from various application areas. Prerequisite: None

CIS221 Database Systems (3 credits) Fall

This course is an introduction to database system concepts and techniques. Topics covered include database environments; ER models; relational data models and relational algebra; schema refinement and normal forms; transactions; SQL; NoSQL and Mongo DB; XML and related technologies. Prerequisite: CIS102

CIS241 Practical Data Analytics Using Python (1 credit) Fall

This course provides students with hands-on experience to solve basic data collection, data cleaning, data visualization and analytics problems using Python programing language and relevant packages/toolkits. Using real-world datasets, students will learn and practice programing and analytic skills to collect and to explore data, to raise questions and to test assumptions. Students also learn about ethical practices when using data. Prerequisite: CIS102

 

CIS242 Computational Analysis and Practical Programming (1 credit) Spring

This one-credit course is an intermediate course of numerical coding. Students will be trained in solving mathematical problems by writing efficient codes in Python that execute given numerical algorithms. Prerequisite: CIS102, STA101, MAT105 & MAT103

CIS331 Data Mining (3 credits) Fall

This course offers the students the opportunity of learning fundamental data mining concepts and algorithms. Some of the topics covered in this course include data preparation; similarity and clustering; near duplicates detection; item sets and association rule mining; recommender systems; outlier analysis; time series mining; model evaluation. Prerequisite: CIS105 & STA101

CIS335 Machine Learning and Artificial Intelligence (3 credits) Spring

This course introduces the use of statistical learning algorithms that allows computers to help making decisions and predictions, and performing tasks that traditionally require human cognitive abilities. Some of the machine learning algorithms covered in the course include logistic regression, k-nearest neighbors, k-means, decision trees, random forests, gradient boosting, principal component analysis, hierarchical clustering, support vector machines, naïve Bayes, etc. An introduction to the deep learning algorithms with appropriate use case scenarios will also be covered toward the end of the course. Some basic ideas and intuition behind modern machine learning methods will be introduced. Students will get familiar with Python machine learning tools and use them for projects. Prerequisite: CIS105 & STA101

CIS341 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: CIS102

CIS351 Introduction to Bioinformatics (3 credits) Spring

This course provides an introduction to the principles and practical approaches of bioinformatics as applied to genes and proteins. The course enables students to broadly understand the type of mathematical and algorithmic reasoning that lies behind various important bioinformatics tools, and to gain some working knowledge in using certain biological databases and on-line bioinformatics algorithms. Prerequisite: CIS102 & BSC101

DAS151A Real-time Data Analytics A (1 credit) Fall

This course introduces the student to tasks, roles, responsibilities, and career opportunities in Data Sciences, and crossing over with Biomedical Sciences, by working on actual projects with partners including NASA, United Nations, USGS and many more. Summer internships may be available based on year-long performance Students develop professional skills while working on locally-oriented projects that have relevance to the larger global community, such as urban management and sustainable resources. Prerequisite: None

DAS151B Real-time Data Analytics B (1 credit) Spring

This course continues development of student skills and projects from DAS151A. The student to tasks, roles, responsibilities, and career opportunities in Data Sciences, and crossing over with Biomedical Sciences, by working on actual projects with partners including NASA, United Nations, USGS and many more. Summer internships may be available based on year-long performance. Students develop professional skills while working on locally-oriented projects that have relevance to the larger global community, such as urban management and sustainable resources. Prerequisite: DAS151A

DAS152A Applied Real-time Data Analysis A (1 credit) Fall

This course introduces the student to tasks, roles, responsibilities, and career opportunities in Data Sciences, and crossing over with Biomedical Sciences, by working on actual projects with partners including NASA, United Nations, USGS and many more. Summer internships may be available based on year-long performance. Students develop professional skills while working on locally-oriented projects that have relevance to the larger global community, such as urban management and sustainable resources. Prerequisite: DAS151B

DAS152B Applied Real-time Data Analysis B (0 credit) Spring

This course introduces the student to tasks, roles, responsibilities, and career opportunities in Data Sciences, and crossing over with Biomedical Sciences, by working on actual projects with partners including NASA, United Nations, USGS and many more. Summer internships may be available based on year-long performance. Students develop professional skills while working on locally-oriented projects that have relevance to the larger global community, such as urban management and sustainable resources. Prerequisite: DAS152A

DAS321 Sample Survey and Customer Analytics (3 credits) Spring

The course introduces basic sample survey theory and method, questionnaire design, data collection, survey data analysis for customer questionnaires. Students use R or SAS to implement designs and analyses of survey data. Prerequisite: STA101

DAS341 Business Data Analytics (3 credits) Fall

This course introduces core statistical techniques of data retrieval, analysis and modeling used by business professionals to make an efficient data-driving decision. It also covers the topics of effective interpretation of data and statistical results in business world. Prerequisite: STA101 or consent of instructor

DAS342 Health Data Analytics (3 credits) Fall

This is an introduction to health care data analytics concepts and methods. Topics include the creation of datasets, the structure of datasets, an introduction to data warehousing and working with large databases, an introduction to public health and healthcare datasets, methods for descriptive analytics and predictive analytics. Prerequisite: STA101

DAS345 Introduction to Computational Biology (3 credits) Fall

Students will have opportunities to perform data management and statistical analysis in biomedical sciences and public health. Various types of data analytic, its advantages and disadvantages in biomedical sciences and public health will be discussed. Accompanied by hands-on experience, students will apply data analysis to address issues in areas such as public health program effectiveness, patient safety, health care utilization, and health care costs. Prerequisite: BSC101 & STA101

DAS351 Data Science Internship (3 credits) Summer

Internships provide entry-level, off-campus career-related experience. Internships may also be used as an opportunity to explore career fields. This course provides students with a supervised, practical learning experience in a work setting that is relevant to their program of study. Through course assignments and workplace tasks and projects the student will apply, connect, and extend in-class academic theory and skills for a professional development. Prerequisite: Permission form

DAS451 Senior Project (4 credits) Spring

In this project-oriented course, students will work in small groups to solve real-world data analysis problems and communicate their results. Innovation and clarity of presentation will be key elements of evaluation. Students will have an option to do this as an independent data analytics internship with an industry partner. Prerequisite: Upon advisor approval

DAS461 Directed Study: Career Development (2 credits) Fall/Spring

This intensive laboratory course will focus on data analysis projects with real data selected by the students. The core skills are oriented around framing research questions, having these guide data management, visualization, selection of modeling techniques to the result analysis and presentation. R or other statistical programming language will be applied. This course is intended to assist students explore their career direction and development. Prerequisite: Upon advisor approval 

MAT103 Linear Algebra (4 credits) Fall

This is an introductory linear algebra course intended for students in science, engineering, and other related areas. Students will learn basic concepts and tools in linear algebra as well as practice writing numerical codes in Python to execute key algorithms such as Gaussian Elimination and LU factorization. Prerequisite: None

MAT105 Calculus I (4 credits) Fall

This course is the first part of Calculus course covering topics such as limits, derivatives, and integration of single-variable functions. Application and execution of these mathematical tools to real-world problems with theoretical derivation or numerical coding is also introduced. This course is intended for students in science, engineering, economics, and computer science, among other disciplines. Prerequisite: None

MAT106 Calculus II (4 credits) Spring

This course is the second part of Calculus course covering topics such as advanced techniques of integration, polar coordinates, infinite sequences and series, and multiple integrals. Application of these mathematical tools to real-world problems is also introduced. In addition, students will practice simple numerical coding to execute algorithms learned from the course. Prerequisite: MAT105

MAT207 Calculus III (3 credits) Spring

This course covers techniques of limits and continuity of multivariable functions, partial derivatives, directional derivatives, the gradient, extreme values, multiple integration, the calculus of vector valued functions, line and surface integrals, Green’s Theorem, and Stokes’s Theorem. Besides lectures, students are required to take the lab and discussion session. Prerequisite: MAT106

 

MAT311 Matrix Analysis (3 credits) Spring

This is a second, upper–level course in linear algebra. Students will gain an adequate understanding of matrix theory and linear algebra so that they can use the concepts in applications. We will study determinants, vector spaces, linear transformations, singular value decompositions, least squares, linear equations, eigenvalues, canonical forms, and QR decompositions. Prerequisite: MAT103

STA101 Introduction to Statistics (3 credits) Spring 

This course is an introductory course in statistics intended for students in a wide variety of areas of study. The goal is to teach basic knowledge in statistical concepts and establish understanding of basic statistical methods. Students will also learn simple R codes to execute those methods to gain experience in statistical computing. Prerequisite: None

STA205 Statistical Computing & Graphics (3 credits) Spring

Statistical computing and graphics is an essential part of data analyst job. In this course, students learn how to collect, process, analyze, and present data through statistical programming in R. They will learn the practice of data cleaning, reshaping of data, basic tabulations, and aggregations in order to be able to produce high quality visualizations. In addition to regular numerical data, students will also have opportunity to practice basic skills extracting, analyzing, and visualizing text data, which is a major component of data sources to answer business and social questions nowadays. Prerequisite: STA101

 

STA202 Introduction to Probability (3 credits) Fall

This course is a basis for statistics. Topics include discrete and continuous random variables, conditional probability and independent events, special discrete and continuous random variables, expectation, variance, laws of large numbers and the central limit theorem. Prerequisite: STA101 & MAT106

 

STA211 Statistical Theory and Methods (3 credits) Spring

This course is intended for majors in data science. In this course, student will learn moment generating function,  order statistics, sampling distributions, central limit theorem, quality of estimators, interval estimation, maximum likelihood, large-sample theory, introduction to hypothesis testing, Bayesian estimator, linear models, and ANOVA. Prerequisite: STA202

STA305: Advanced Statistical Computing and Graphics (3 credits) Fall

This course covers advanced topics in statistical computing with cases studies. Students will have opportunities to practice statistical programing in both R and Python. Some topics covered include interactive data visualization, statistical simulations, bootstrapping, Monte Carlo methods, parallel programing for data science, hypothesis testing and power analysis. Prerequisite: STA205

STA311: Applied Regression Analysis (3 credits) Fall

This course is a comprehensive course in the theory and methods of fitting multiple linear regression and related techniques of statistical modeling, estimation, and inference. Prerequisite: STA101 & MAT103

STA321 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: STA211 or consent of instructor

STA331 Multivariate Analysis (3 credits) Fall

The goal of this course is to help students develop the statistical skills to approach and analyze multivariate data correctly in an applied context. Topics include linear algebra, the multivariate normal distribution, principle components, factor analysis, discriminant function, cluster analysis, Hotelling’s T2 and MANOVA. Prerequisite: STA211 or consent of instructor. Prerequisite: STA211 or consent of instructor

STA335 Bayesian Analysis (3 credits) Spring

This is an advanced undergraduate/master level course that introduces the Bayesian approach to statistical inference for data analysis. Students will learn the theory of Bayesian inference, and data analysis using statistical software (mainly R) will also be emphasized. Topics include priors, posteriors, basics of decision theory, Markov chain Monte Carlo, Bayes factor, empirical Bayes, Bayesian linear regression and generalized linear models, hierarchical models. Prerequisite: STA211 or consent of instructor

STA341 Survival Analysis (3 credits) Fall

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. We will begin this course from describing the characteristics of survival (time to event) data and building the link between distribution, survival, and hazard functions. After that, we will cover 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: STA211 or consent of instructor

 

STA345 Nonparametric Statistics (3 credits) Spring

This course will provide students with the basic theory and computing tools to perform nonparametric tests including the sign test, Wilcoxon signed rank test, and Wilcoxon rank sum test, as well as the corresponding nonparametric point and interval estimation. Additional nonparametric tests such as Kruskal-Wallis and Friedman tests for one-way and two-way analysis of variance, multiple comparisons, dispersion, and independence problems will also be covered. Other topics include estimation methods for nonparametric density, regression, and computing as they relate to nonparametric statistics and bootstrapping. Prerequisite: STA211 or consent of instructor

STA371 Optimization (3 credits) Spring

This is an introduction of numerical methods for continuous multivariate optimization (unconstrained and constrained). Topics include line-search and trust-region strategies; gradient descent, Nesterov acceleration, stochastic gradient, momentum; conjugate-gradient, Newton-Raphson, quasi-Newton, and large-scale methods; primal and dual in convex optimization; linear programming; quadratic programming; augmented Lagrangian methods; sequential quadratic programming. Prerequisite: STA211 or consent of instructor

ECO101 Principles of Economics (3 credits) Fall

This course is designed to introduce students to the basic principles of economics, including both microeconomics and macroeconomics. The part about microeconomics includes the concepts of scarcity and opportunity cost, consumer and producer behaviors, market structures, market failures, government roles and government failures, welfare, exchange, and comparative advantages. While in the part about macroeconomics, students will learn measures of national income, income growth and inequality, unemployment, inflation, money supply, banking and financial institutions, and fiscal policy. Prerequisite: None

ECO211 Microeconomics (3 credits) Spring

This course is an intermediate course on Microeconomics. It introduces the optimization methodology for how society addresses the economic problem of resource scarcity and its efficient allocation.  In addition, the course explores what happens to the market when the government tries to play a role in the distribution of resources. Throughout the course students will study how households and firms make choices so as to best allocate the resources available to them in various structures of market. Prerequisite: ECO101

ECO343 Health Economics (3 credits) Fall

This course is designed to introduce students to basic health economic terminology, concepts, theories, procedures, and methods that are widely used in health-related industries. Students will have opportunities to develop relevant analytical and modeling skills via case study and real-world examples to address current economic problems and issues in the healthcare industry. Prerequisite: ECO101

BUS211 Marketing in Creative Industries (3 credits) Fall

This course introduces the concept of creative industries and its main characteristics as opposed to non-creative industries. By reviewing current marketing studies of creative industries, this course introduces a general managerial model whose fundamentals are value, experience, and creativity. It further analyzes custom experiences, the process of product value creation and delivery, the business side of marketing as well as the management of the multi-media and multi-channel marketing in today’s environment. Lastly, the organizational issues such as property rights and ethical law are discussed.  Prerequisite: None

BUS311 Business Finance (3 credits) Spring

This course attempts to develop a framework which will provide students with an overview of financial systems, and the main concepts and principles of investments.  Students who master the course material will acquire the analytical tools and financial theory necessary for making good investment decisions and understand the paradigms by which financial securities are valued from the perspective of a portfolio manager.  This course can also serve as a preparation course for students interested in taking the CFA or FRM tests in the near future. Prerequisite: ECO211

BUS335 Pricing and Revenue Management (3 credits) Fall

This course provides an introduction to both the theory and the practice of revenue management and pricing; the course develops a set of methodologies that students can use to identify and develop opportunities for revenue optimization in different business contexts including show business, media, health care, transportation, and hospitality industries, etc. The course places particular emphasis on discussing quantitative data-driven models and their implementations. Prerequisite: ECO101 & MAT103

MAT101 Applied Math (3 credits) Fall

This course serves general-education purpose for students that are not majored in science. The course focuses on introducing new mathematical concepts, tools and techniques that can be applied to understand or solve real-world problems in daily life. A few examples of topics include finance, investment, measurement, management etc. Prerequisite: None

MAT104 Applied Calculus (3 credits) Fall

This course is a one-semester introductory calculus course covering basic analytic geometry of graphs of functions, limits, continuity, derivatives, integration and applications to the biomedical science and other disciplines. Prerequisite: three years of high school mathematics (including trigonometry and logarithms) or a pre-calculus course. Prerequisite: None

STA102 Statistics in Real Life (3 credits) Spring

The course introduces basic probability theory, essential statistical techniques and methods of data analysis that are commonly encountered in real life applications. Prerequisite: None

Graduate Level 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