Data Science 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