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The Masters of Data Analytics Course Descriptions

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Core Courses:

IA 510 - Database Modeling, Design and Implementation

This course is focused on key concepts of database modeling, design, and management, utilizing a variety of relational database management systems. Students will acquire understanding of proper data modeling approaches, grounded in underlying rationale for creating well-designed and efficient data repositories. They will be introduced to the variety of modeling and implementation approaches, and will gain understanding of unique advantages leading to the prevalence of the relational database model in today's systems. Students will learn to properly utilize basic methods and techniques for conceptually envisioning as well as designing databases which include Entity-Relationship (ER) modeling, relational modeling, normalization, and Structured Query Language (SQL).

IA 520 - Optimization Methods for Analytics

Optimization is a structured approach to determining the best values for a set of decision possibilities given constraints and an objective expressed as a function of these decision variables. This course focuses on the design, development, and analysis of optimization models while using canned software to solve them. The students will solve a wide variety of optimization problems applicable to a wide variety of industries: manufacturing, distribution, health care, finance, marketing, etc. Students will develop optimization models using Microsoft Excel.
Prerequisites: An undergraduate course in probability and statistics, and an undergraduate course in introductory computer science or programming.

IA 530 - Probability and Statistics for Analytics

Probability theory is presented as a mathematical foundation for statistical inference. Axiomatic probability is introduced; standard discrete and continuous probability distributions are presented. Joint distributions and transformations are discussed. Probabilistic convergence concepts are introduced. The key objectives of this course are to formulate statistical models and find optimal solutions for statistical problems in economics, business, engineering, and science, have a global overview of the interplay between probability and statistics as well as master the art of writing statistical proofs well, consistent with the written tradition of the discipline, and have the skills to communicate statistical ideas effectively.

IA 640 - Information Visualization

The science of Information Visualization (InfoVis) seeks to understand the best way to achieve synergistic interaction of the human visual perception system and data. Data visualizations focus on two general application areas: (1) Enhancing the ability of the visual system to discover structure in the data leading to new insight and knowledge, and (2) Taking advantage of the visual display to support rapid diffusion of complex information throughout the organization achievable by the visualization applications. This class will study the techniques, systems, software, algorithms, and design principles that allow for maximal information transmission and knowledge discovery when working with complex data sets. Students will learn the key principles involved in information visualization through a project driven course, with students gaining background skills in design and application of innovative visualizations.

IA 650 - Data Mining

Recent advances in information technology, together with the growth of the Internet have resulted in an explosion of data collected, stored, and disseminated. Because of its massive size, it is difficult for analysts to sift through the data even though it may contain useful information. Data mining holds great promise to address this problem by providing efficient techniques to uncover useful information hidden in large data repositories. Awareness of the importance of data mining is becoming widespread. Industry is creating more job opportunities for people who have interdisciplinary data analytic skills. They key objectives of this course are to teach the fundamental concepts of data mining and provide extensive hands-on experience in apply the concepts to real-world applications.

Students will have opportunities to learn both domain and technical knowledge to face the big data challenges in industry. The core topics to be covered include classification, clustering, association analysis, and anomaly/novelty detection. This course consists of about 13 weeks of lecture, followed by 2 weeks of project presentations by students who will be responsible for developing and/or apply data mining techniques to applications such as intrusion detection, Web usage analysis, financial data analysis, text mining, bioinformatics, systems management, Earth Science, and other scientific and engineering areas. At the end of this course, students are expected to possess the fundamental skills needed to conduct their own research in data mining or to apply data mining techniques to their own research fields.

Elective Courses (non-exclusive, other courses can be taken after consulting with an advisor):

IA 605 - Data Warehousing

This course examines how data warehouses are used to successfully gather, structure, analyze, understand, and act on information. The components and design issues related to data warehouses and business intelligence techniques for extracting meaningful information from data warehouses are emphasized. The emphasis is on proper modeling techniques as well as the techniques for Extraction, Transformation and Loading (ETL) process. Various software tools will be used to demonstrate design, implementation, and utilization of data warehouses.

IA 505 – Tabular Data Analytics

Proper utilization of modern methods and tools for analyzing data in tabular form is a critical component of effective and timely creating and use of organizational intelligence in variety of fields of human endeavor: management in variety of organizational settings, social science, health care, engineering etc. This course focuses on critical skills and tools for using the spreadsheet software packages for the purpose of conducting a variety of analytics tasks and operations to improve gathering, generation and presentation of organizational intelligence. Focus is on proper data gathering and preparation, followed by the use of key analysis, grouping and summarization tools as well as data presentation and visualization by creating spreadsheet based dashboards, charts and scorecards using advanced tools and methods.

IA 630 - Modeling for Insight (pre-requisite: Spreadsheet Analytics)

Although mathematical models have a long and compelling history of application in science and engineering, they are becoming increasingly important in the world of business. Some problems are well described by statistical (curve fitting models), but analyzing a business problem generates significant complexities that are often not well described by simply analyzing the historical data. In particular, to be able to answer questions of 'what if...?' often requires an understanding of system behaviors when we specifically to to depart from previous (historical) practices. The critical contribution of these models is that they may allow the analyst to arrive at compelling insights to contribute to development of a reasoned action plan. This class will enable students to develop familiarity and facility in generating insightful models via modeling in realistic situations. Key skills to be developed include recognizing the key problem, developing a model structure for an unstructured problem, and intelligent analysis and interpretation of model results. Additionally, students will gain experience in the iterative process that is required to develop useful models for unstructured problems.

This course will make extensive use of Excel for building and analysis of models and will leverage skills developed in prerequisite courses in spreadsheet modeling. Students will be expected to use Excel and Risk Solver Platform. This course will be delivered primarily in the style of a studio with minimal theory, but with repeated practical exercises. Each exercise will require a written report, and every second exercise will require a presentation.

IA 670 - Geospatial Systems

Geographic Information Systems (GIS) are software tools designed to capture, store, analyze and display geographically referenced data. With the widespread use of mobile devices, satellite remote sensing and rapid development of data collection platforms, and sensors, GIS is emerging as an adaptable framework for organizing and analyzing these disparate datasets. Combined with other technologies such as web development, cloud computing and location based services, these Geospatial Systems are being used in many fields to allow rapid decision-making support and unprecedented access to data. This advanced course in GIS is designed to provide the tools necessary to analyze geospatial data use a wide variety of spatial analysis functions and geo-statistical data exploration tools. Software methods will rely primarily on ArcGIS 10.2 though other software packages such as R will also be used.

CS 549 - Computational/Machine Learning

Computational learning studies algorithmic problems for inferring patterns and relations from data. This course describes the mathematical foundations of learning and explores the important connections and applications to areas such as artificial intelligence, cryptography, statistics, and bioinformatics. A list of relevant topics may include perceptron and online learning, graphical models and probabilistic inference, decision tree induction and boosting, analysis of Boolean functions, sample complexity bounds, cryptographic and complexity hardness, and reinforcement learning. Basic ideas from computer science and mathematics are employed to describe the main ideas and major developments in computational learning. Students are expected to learn and explore recent research ideas in the area. Prerequisites or co-requisites: CS541 and CS547, or consent of the instructor.

CS 551 - Artificial Intelligence

This course is an introduction to the computational study of intelligent systems. Topics include heuristic search, knowledge representation, automated reasoning, knowledge-based systems, reasoning under uncertainty, planning, and intelligent agents. Additional topics may be drawn from machine learning, neural networks, computer vision, and natural language understanding. AI programming techniques and methods will also be covered throughout the course. Prerequisites: CS344 or equivalent or consent of the instructor.

CS 559 - Human Computer Interaction

This course provides an introduction to the field of human-computer interaction (HCI). This discipline focuses on the design, evaluation and implementation of interactive computing systems from a user's point of view. The course will give a broad overview of the ideas, techniques, and tools in the subject, with a systematic approach to designing visual interfaces and evaluating their effectiveness. Case studies of existing interfaces, technologies, and data display methods will be discussed and critiqued. Topics include: programming and command languages; menus and forms graphical user interfaces, computer-supported cooperative work, information search and visualization; input/output devices; and display design. A collaborative course project will explore issues in HCI and design.

EC 611- Econometrics

This course is an entry-level graduate econometrics course, focusing mainly on time-series and panel data techniques. It is entry-level in the sense that students are not presumed to have any prior acquaintance with econometrics beyond EC311, although they should have sufficient statistical and computing background. and coursework in linear algebra and calculus including some optimization. Students also need to be somewhat familiar with some statistical software such as STATA or R or SAS. The course attempts to serve two types of audiences. For those who wish to pursue applied data analysis in the real world, it presents a wide array of problem instances and tools appropriate for those instances. The course also serves as a stepping stone for those interested in knowing the field more intimately. introducing them to a fair amount of theory and giving them a tour of a small selection of classic and contemporary papers written in Econometrics. Prerequisites: Calculus I. II and a course in linear algebra.

EE 501 - Digital Signal processing

An introduction to discrete-time signal processing. Topics include: A review of orthogonality, Fourier series, Fourier transforms and sampling theory. Smoothing, interpolation, D/A conversion. Digital filters, windows. Design of non-recursive filters, recursive filters. Correlation and spectra of random signals, spectral estimation. Substantial in depth investigation of advanced topics will be required. Prerequisite: EE321.

ES 505 - Design of Experiments and Analysis of Data

Modern techniques for the analysis of data and for the planning of experiments in research and in manufacturing. Includes use of software to design factorial and response surface method experiments, interpret the results, and fit data to equations. Prerequisites: MA232 or MA239 or MA339

EE 574 - Pattern Recognition

Bayes decision theory, discriminant functions and decision surfaces. Supervised learning, parametric methods, Parzan windows, nearest neighbor classification, Fisher's linear discriminant. Unsupervised learning and clustering. Neural networks, single-layer perceptron convergence algorithm, gradient descent training, generalizations to multilayer feed-forward networks. Classifier complexity and sample size. Hopfield networks for auto-associative memory, unsupervised and self-organizing networks. Prerequisite: MA/STAT381 or equivalent.

ME 529, Stochastic Processes for Engineers

Review of the theory of probability. Stochastic processes. Stationary and non-stationary processes. Time averaging and ergodicity. Correlation and power spectrum. Langevin's equation and Markov processes. Poisson and Gaussian processes. Response of linear systems. Approximate methods for analysis of nonlinear stochastic equations. Introduction to stochastic stability. Mean square and almost sure stability analysis. Random vibrations, turbulence and other applications to engineering problems.

OM 680 - Strategic Project Management

Project management from a decision-making perspective and how projects can be used to implement organizational strategy. The course follows the project life cycle model from project initiation to implementation to termination. Topics covered include project selection organizational strategy, planning, conflict resolution, budgeting, scheduling (PERT and CPM), resource allocation, information management, control, auditing, and termination procedures. In addition, there is a special section on information technology (IT) project management standards and techniques. Computer applications, case studies and student project teams will be an integral part of the course.

EC 611 – Econometrics

This course is an entry-level graduate econometrics course, focusing mainly on time-series and panel data techniques. It is entry-level in the sense that students are not presumed to have any prior acquaintance with econometrics beyond EC311, although they should have sufficient statistical and computing background. and coursework in linear algebra and calculus including some optimization. Students also need to be somewhat familiar with some statistical software such as STATA or R or SAS. The course attempts to serve two types of audiences. For those who wish to pursue applied data analysis in the real world, it presents a wide array of problem instances and tools appropriate for those instances. The course also serves as a stepping stone for those interested in knowing the field more intimately. introducing them to a fair amount of theory and giving them a tour of a small selection of classic and contemporary papers written in Econometrics. Prerequisites: Calculus I. II and a course in linear algebra.

MK 696 – Marketing Research Methods

Intended to equip the student with a thorough knowledge of an arsenal of research methods, including the assumptions, methodology, and limitations of these methods. Enhances students' ability to conceptualize and operationalize a research question. Some statistical content is included as an introduction to data analysis. Applications of these methods are discussed within the context of research problems faced by both academic researchers and practitioners (e.g., managers, engineers, economists, marketing researchers, information system designers). A research project will be an integral part of the course.


IA690 Capstone Project

This course is based on a semester-long sponsored project that utilizes a variety of expertise areas, methods, and skills in data analytics. Students participating in this course will be divided into inter-disciplinary teams charged with planning, designing, and implementing an analytics solution for the organization that sponsors the project. In addition to the continuous interaction with the sponsoring organization representatives, students will be required to report and consult with the faculty project supervisor on a regular basis. Depending on the nature of the capstone and its sponsorship, projects could be on-site fieldwork intensive. Final deliverables include written reports and oral