Courses

MS-523. Behavioral Research Methods. 3 Credits.
This course will guide the marketer through both quantitative and qualitative techniques for maximizing brand and customer relationships in an integrated-marketing environment. It will cover the following topics: Sampling techniques used in marketing: how and why to sample, types of sampling. The measures of central tendency and dispersion: how to develop and assess these measures to better understand potential data issues before analysis. Graphical representation of marketing data: the use of bar charts, pie charts, line charts, and other methods for showing consumer data and purchase data. Important distributional properties of marketing data: the central-limit theorem and the normal distribution. Marketing test design and analysis: sample-size estimation and test assessment via hypothesis testing. Full factorial test design: the rules of test design. Market research survey design and execution: types of surveys, types of questions, and test planning. Research-analysis methods: choice modeling/conjoint analysis, rank correlations. Types and usage of syndicated data: Nielsen, IRI, Simmons, and other data sources. Sizing a market: how to assess opportunities in the marketplace via online research and online services. ROI analysis: the various methods of calculating return on marketing investment, campaign management spreadsheets, calculations, marketing goals.

DS-650. Data Law Ethics and Business Intelligence. 3 Credits.
The increasing use of big data in our society raises legal and ethical questions. Business intelligence is the process of collecting and transforming raw data into meaningful and useful information for business purposes. This course explores the issues of privacy, data protection, non-discrimination, equality of opportunities and due process in the context of data-rich environments. It analyzes ethical and intellectual property issues related to data analytics and the use of business intelligence. Students will also learn the legal obligations in collecting, sharing, and using data, as well as the impact of algorithmic profiling, industrial personalization, and government. This course also provides an understanding of the important capabilities of business intelligence, the technologies that enable them, and the management of business intelligence. Prerequisites: DS-510, DS-520.

DS-510. Introduction to Data Science. 3 Credits.
Data Science is a set of fundamental principles that guide the extraction of valuable information and knowledge from data. This course provides an overview and develops student’s understanding of the data science and analytics landscape in the context of business examples and other emerging fields. It also provides students with an understanding of the most common methods used in data science. Topics covered include introduction to predictive modeling, data visualization, probability distributions, Bayes’ theorem, statistical inference, clustering analysis, decision analytic thinking, data and business strategy, cloud storage and big data analytics.

DS-520. Data Analysis and Decision Modeling. 3 Credits.
This course will provide students with an understanding of common statistical techniques and methods used to analyze data in business. Topics covered include probability, sampling, estimation, hypothesis testing, linear regression, multivariate regression, logistic regression, analysis of variance, categorical data analysis, Bootstrap, permutation tests and nonparametric statistics. Students will learn to apply statistical techniques to the processing and interpretation of data from various industries and disciplines.

DS-542. Python in Data Science. 3 Credits.
The course introduces Python programming for statistical analyses and managing, analyzing, and visualizing data. Topics include numeric and non-numeric values, arithmetic and assignment operations, arrays and data frames, special values, classes, and coercion. Students will learn to write functions, read/write files, use exceptions, measure execution times, perform sampling and confidence analyses, plot a linear regression. Students will explore tools for statistical simulation, large data analysis and data visualization, including interactive 3D plots. Prerequisites: DS-510, DS-520.

DS-600. Data Mining. 3 Credits.
Data mining refers to a set of techniques that have been designed to efficiently find important information or knowledge in large amounts of data. This course will provide students with an understanding of the industry standard data mining methodologies, and with the ability to extract information from a data set and transform it into an understandable structure for further use. The topics covered include decision trees, classification, predictive modeling, association analysis, statistical modeling, Bayesian classification, anomaly detection and visualization. The course will be complemented with hands-on experience of using advanced data mining software to solve realistic problems based on real-world data. Prerequisites: DS-510, DS-520.

DS-665 Advanced Machine Learning. 3 Credits.
This course builds upon foundational machine learning concepts and explores advanced algorithms, techniques, and applications. Topics include ensemble learning (boosting, bagging, and stacking), deep reinforcement learning, transformer models, Bayesian networks, Gaussian processes, adversarial learning, and meta-learning. The course also covers interpretability and explainability in machine learning, fairness and bias mitigation, and scalability for big data applications. Students will engage in hands-on programming assignments and a research-driven project to apply advanced machine learning techniques to real-world datasets.
Prerequisites: DS-630 Machine Learning

DS-702 Practicum in Data Science. 3 Credits.
This practicum provides students with hands-on experience in applying data science techniques to solve real-world problems. Students will work on an industry-related project or research problem, applying data collection, preprocessing, feature engineering, model selection, and evaluation techniques. Topics include end-to-end machine learning pipelines, model deployment, and ethical considerations in data science applications. Collaboration with industry partners or faculty-led research initiatives may be included. Students are expected to document and present their findings through reports and presentations.
Prerequisites: DS-510, DS-520, DS-600

DS-703 Practicum in Statistics. 3 Credits.
This practicum course focuses on the application of statistical methodologies in data analysis and decision-making. Students will explore advanced statistical techniques such as multivariate analysis, Bayesian inference, time-series modeling, and survival analysis. Emphasis will be placed on designing experiments, hypothesis testing, and applying statistical models to real-world datasets. The course includes hands-on projects, where students analyze datasets, visualize statistical insights, and communicate their findings effectively.
Prerequisites: DS-520 Data Analysis & Decision Modeling

DS-770 Topics in Data Science. 3 Credits.
This course covers emerging and specialized topics in data science, adapting to the latest trends and research advancements. Topics may include but are not limited to ethical AI, explainable AI (XAI), generative AI, quantum computing in data science, spiking neural networks, federated learning, and graph neural networks. The course content will vary each semester based on faculty expertise and industry relevance. Students will engage in discussions, literature reviews, hands-on implementations, and a final project that applies the selected topic to a real-world data science problem.

DS-630. Machine Learning. 3 Credits.
Machine learning is the field of study that gives computers the ability to learn from experience without being explicitly programmed. This course covers the theory and practical algorithms for machine learning from a variety of perspectives. Topics include decision tree learning, parametric and non-parametric learning, Support Vector Machines, statistical learning methods, unsupervised learning, reinforcement learning and the Bootstrap method. Students will have an opportunity to experiment with machine learning techniques and apply them to solve a selected problem in the context of a term project. The course will also draw from numerous case studies and applications, so that students learn how to apply learning algorithms to build machine intelligence. Prerequisites: DS-510, DS-520, DS-542.

DS-631. Deep Learning Algorithms. 3 Credits.
Machine learning is the science (and art) of programming computers, so they learn from data. It is the field of study that gives computers the ability to learn from experience without being explicitly programmed. This course covers the theory and practical algorithms for neural networks and deep learning. Major topics neural networks, convolutional neural networks, recurrent neural networks, reinforcement learning, and implementation of deep learning in TensorFlow. Students will have an opportunity to experiment with advanced machine learning techniques (especially using Python) and apply them to solve selected problems in the context of a term project. Prerequisites: DS-630.

DS-800. Forecasting Methods for Business Decisions. 3 Credits.
This course will prepare leaders for different forecasting methods and analytical tools to get them prepared for the business decisions. Forecasting methods will be evaluated according to the conditions such as under uncertainty, under risk, and so on.

DS-801. Advanced Data Structures & Algorithms. 3 Credits.
This course explores core data structures and algorithms used in everyday applications, the trade-offs involved with choosing each data structure, along with traversal, retrieval, and update algorithms. It will cover linked lists, stacks, queues, binary trees, and hash tables. Prerequisites: DS-630.

DS-802 Natural Language Processing
This course explores the fundamental concepts of NLP and its role in current and emerging technologies. Students will gain a thorough understanding of modern neural network algorithms for the processing of linguistic information. By mastering cutting-edge approaches, they will gain the skills to move from word representation and syntactic processing to designing and implementing complex deep learning models and other language understanding tasks.

DS-703 Optimization and Computational Linear Algebra
In this course, students will learn about the theory and practical aspects of many fundamental tools from matrix computations, numerical linear algebra and optimization. In addition to classical applications, most examples will particularly focus on modern large-scale machine learning problems. Implementations will be done using MATLAB/Python.

DS-804 Advanced Optimization
The course covers mathematical programming and combinatorial optimization from the perspective of convex optimization, which is a central tool for solving large-scale problems. The course is dedicated to the theory of convex optimization and its direct applications. Besides, it focuses on advanced techniques in combinatorial optimization.

DS-805 Research Seminar in Forecasting
This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Forecasting. Topics of special interest vary from trimester to trimester.

DS-806 Research Seminar in Unstructured Data Analysis
This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Unstructured Data. Topics of special interest in Unstructured Data analysis are presented by faculty and students under faculty direction. Topics of special interest vary from semester to semester.

DS-860. Ph.D. Qualifying Exam. 2 Credits.
The Ph.D. Qualifying Exam is designed to assess students’ mastery of core data science concepts, research methodologies, and their ability to critically analyze and synthesize knowledge from various domains. The exam covers foundational and advanced topics in machine learning, data mining, statistics, optimization, and research methodologies. Successful completion of this exam is required for students to advance to doctoral candidacy. The exam format may include written, oral, and applied problem-solving components.
Prerequisites: Completion of core Ph.D. coursework and approval from the Ph.D. Program Committee.

DS-870. Ph.D. Dissertation Proposal. 2 Credits.
This course guides students through the process of developing and defending their dissertation proposal. Students will refine their research questions, review relevant literature, design their methodology, and outline their dissertation structure. The course emphasizes the formulation of a rigorous research plan, ethical considerations, and feasibility of the study. Students will be required to present and defend their proposal before their Dissertation Committee for approval before proceeding with their dissertation research.
Prerequisites: Successful completion of the Ph.D. Qualifying Exam (DS-860) and approval from the Dissertation Committee.

DS-871 and 872. Dissertation Seminar I & II. 3 Credits each.
These course sections will guide and assist in the development of the dissertation proposal, writing dissertation chapters, design, data analysis, preparing articles for publication, developing research proposals for professional conferences and other professional arenas. Emphasis will be placed on individual student work with their Mentor and Dissertation Committee members.

DS-873 and 874. Dissertation Seminar III and IV. 3 Credits each.
In these course sections, doctoral students work individually with their Mentor and Dissertation Committee members on the completion of their dissertation. To be deemed acceptable, the dissertation must be evidence that the student has pursued a program of relevant educational knowledge in the field of educational leadership in a higher education or K-12 school system setting. Students must maintain continuous enrollment in this course until they have completed and defended their dissertation. Students must have their dissertation proposal approved by the Doctoral Committee for Research Involving Human Subjects before registering for this course.