Courses
GE-801. Curriculum Development and Instruction. 3 Credits.
This advanced course concentrates on how curriculum is developed and implemented in organizational settings. The course is based on theoretical research, current societal issues, and school-based needs for accountability-based education with specific strategies to foster learning, interventions, personalization and mastery of the curriculum. The students will analyze current curriculum standards and educational reform movements.
GE-809. Research Design and Methods. 3 Credits.
This course will actively engage in the development and implementation of a draft of the formal research proposal. They will be required to complete the research process by utilizing both a quantitative and qualitative approach toward their respective research topic. All required components of the research proposal outline must be included, as described in the American Psychological Association Manual current edition.
GE-826. Analysis and Interpretation of Assessment Data. 3 Credits.
This course will prepare teacher leaders and administrators to analyze, manage, interpret and make decisions based on the data that is commonplace in America’s schools. Prerequisites: GE-801
GE-829. Using Technology to Improve Curriculum Design. 3 Credits.
This course focuses on the role of the educational leader in utilizing emerging technologies to achieve and enhance school reform. This course will enable students to plan for the integration of emerging technologies into the design of the curriculum, instruction, research, and assessment. Students will study contemporary technology issues and implications in the use of information and multimedia technologies in teaching and learning, communications, and management. Students will research legal and ethical considerations in the planning, funding, professional development needs, and evaluation related to the use of educational technology. Prerequisites: GE-801
MS-523. Behavioral Research Methods. 3 Credits.
This course will guide the marketer through both quantitative and qualitative techniques for maximizing the 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 prior to 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 understanding of the industry standard data mining methodologies, and with the ability of extracting information from a data set and transforming it into an understandable structure for further use. 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-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 tool 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 be covered 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-871 and 872. Dissertation Seminar I & II. 4 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 II. 4 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 successfully completed and defended their dissertation. Students must have their dissertation proposal approved by the Doctoral Committee for Research Involving Human Subjects prior to registering for this course.