Introduction to programming and computing methods within the context of traditional industrial engineering problem solving. Students will be exposed to classic industrial engineering problem scenarios. Basic techniques within object-oriented programming, including designing classes, using objects, creating methods, looping and decision constructs, arrays, and file handling, will be used to facilitate solving these problems. Pre- or Corequisite: (MATH 2445 or MATH 2514 or MATH 2554) and INEG 2103. (Typically offered: Fall and Spring)

A continuation of INEG 2214. Review of fundamental computing methods and exposure to advanced use of computing libraries. Developing and implementing algorithms using computing methods to solve illustrative and practical problems of interest to industrial engineers. Students will use existing computing libraries, data structures, and programming interfaces to implement software using problem-based learning. Prerequisite: INEG 2214. (Typically offered: Fall and Spring)

Applications to industrial engineering of descriptive statistics, single-population point and interval estimation, single-population hypothesis testing, two-population point and interval estimation, two-population hypothesis testing, goodness-of-fit testing, contingency table testing, linear regression, correlation, design of experiments, and analysis of variance. Introduction to statistical quality control. Use of modern statistical analysis software is emphasized. Corequisite: Drill component. Prerequisite: INEG or DTSC students only. Pre- or corequisite: INEG 21003 or DASC 25904. (Typically offered: Fall and Spring)

Applications to engineering problems of two-population point and interval estimation, two-population hypothesis testing, linear regression, correlation, design of experiments, analysis of variance, and nonparametric statistics. Introduction to statistical quality control. Corequisite: Drill component. Prerequisite: INEG 23104. (Typically offered: Fall and Spring)

Applications to engineering problems of data summary and presentation, random variables and probability distributions, point and interval estimation, hypothesis testing, linear regression, and design of experiments. Not for credit toward the Bachelor of Science in Industrial Engineering. Corequisite: Drill component. Prerequisite: MATH 25004. (Typically offered: Fall, Spring and Summer)

Introduction to modeling and analysis of deterministic operations design and planning problems using formal optimization algorithms and software. Identification and formulation of appropriate applications, linear programming, sensitivity, network flows/transportation/assignment problems, shortest paths, and integer linear programming. Prerequisite: (INEG 2214 or CSCE 2004 or DASC 1204) and (MATH 2574 or DASC 2594). (Typically offered: Spring)

The development and use of discrete-event simulation models for the analysis and design of systems found in manufacturing, distribution, and service contexts. Coverage includes conceptual modeling, model translation to computer form, statistical input models, random number generation and Monte Carlo methods, experimentation and statistical output analysis, and queuing analysis. Includes the use of modern computer simulation languages. Corequisite: Drill component. Prerequisite: INEG 2223 or CSCE 2004 or DASC 1204. Pre- or Corequisite: INEG 2333 or STAT 3003. (Typically offered: Fall)

An introduction to the basic principles of database modeling and technologies for industrial engineers. Coverage includes analyzing user requirements, representing data using conceptual modeling techniques (e.g. UML, ERD), converting conceptual models to relational implementations via database design methodologies, extracting data via structured query language processing, and understanding the role of database technology in industrial engineering application areas, implementing database applications. Pre- or corequisite: INEG 22203. (Typically offered: Fall and Spring)

The course focuses on the principles, theory, design, and implementation of data mining algorithms for large-scale data. Topics include foundations of data mining; preprocessing; mining frequent patterns, associations and correlations; supervised learning including decision tree induction, naïve Bayesian classification, support vector machine, logistic regression, Bayesian network, and K-nearest neighbor learning; unsupervised learning including K-means clustering, hierarchical clustering, density-based clustering, and grid-based clustering; outlier analysis; graph mining; scalable and distributed data mining. Prerequisite: (INEG 23303 and INEG 22203) or (CSCE 20104 and INEG 33103) or (INEG 23104 and INEG 22203) or INEG 33303. (Typically offered: Fall)

This application-oriented course is driven by real problems arising from industry and focuses on problem solving using both modern and classic statistical methods. For both senior undergraduate and graduate students, the main goal of this course is to provide a comprehensive introduction to those most popular statistical learning methods and tools (such as R and Apache Spark) which are widely used in industry today. Prerequisite: INEG 23303 or INEG 23104 or INEG 33303. (Typically offered: Spring)

Reinforcing important computer programming methods using industrial engineering-based applications. Students will utilize Microsoft Excel and Visual Basic for Applications to develop custom solutions to challenging industrial engineering problems. Emphasis on computational proficiency and computing productivity in a spreadsheet-based setting. Prerequisite: (INEG 22104 or DASC 12004) and INEG 23104. (Typically offered: Fall)

Introduction to the application of statistical methods to problems in economics. Prerequisite: ((ECON 21003 and ECON 22003) or (ECON 21403)) and ((MATH 22003 or (MATH 24004 or higher)) and (BUSI 10303 or MATH 21003 or INEG 23104 or STAT 30133). (Typically offered: Spring)

The application of forecasting methods to economics, management, engineering, and other natural and social sciences. The student will learn how to recognize important features of time series and will be able to estimate and evaluate econometric models that fit the data reasonably well and allow the construction of forecasts. Prerequisite: ((ECON 21003 and ECON 22003) or (ECON 21403)) and (MATH 22003 or (MATH 24004 or higher)) and (BUSI 10303 or MATH 21003 or INEG 23104 or STAT 30133). (Typically offered: Fall)

Review of system analysis, probability, random variables, stochastic processes, auto correlation, power spectral density, systems with random inputs in the time and frequency domain, and applications. Prerequisite: ELEG 31204. Pre- or Corequisite: MATH 26004. (Typically offered: Spring)

Introductory programming course for students majoring in computer science or computer engineering. Software development process: problem specification, program design, implementation, testing and documentation. Programming topics: data representation, conditional and iterative statements, functions, arrays, strings, file I/O and classes. Using C++ in a UNIX environment. Corequisite: Lab component. Prerequisite: MATH 24005 or MATH 24004 with a grade of C or better, a College of Engineering (ENGR) student, a Computer Science Minor (CSCE-M), or a math major (MATHBS or MATHBA). (Typically offered: Fall and Spring)

This course continues developing problem solving techniques by focusing on fundamental data structures and associated algorithms. Topics include: abstract data types, introduction to object-oriented programming, linked lists, stacks, queues, hash tables, binary trees, graphs, recursion, and searching and sorting algorithms. Using C++ in a UNIX environment. Corequisite: Lab component. Prerequisite: CSCE 20004 with a grade of C or better. (Typically offered: Fall and Spring)

Cluster computing solves problems too large in terms of memory or run time for a single workstation. Common approaches to these problems combine the resources of multiple computers to collectively find the solution. High performance computing is quickly expanding to areas including: chemistry, physics, mathematics, engineering, bio-informatics, finance, logistics, etc. (Typically offered: Irregular)

This course studies the principle methods used in the solution of programming contest problems, e.g., data structures strings, sorting, machine arithmetic and algebra, combinatorics, number theory, backtracking, graph traversal, graph algorithms, dynamic programming, grids, and computational geometry. Prerequisite: CSCE 2014. (Typically offered: Irregular)

The course focuses on the principles, theory, design, and implementation of data mining algorithms for large-scale data. Topics include foundations of data mining; preprocessing; mining frequent patterns, associations and correlations; supervised learning including decision tree induction, naïve Bayesian classification, support vector machine, logistic regression, Bayesian network, and K-nearest neighbor learning; unsupervised learning including K-means clustering, hierarchical clustering, density-based clustering, and grid-based clustering; outlier analysis; graph mining; scalable and distributed data mining. Prerequisite: (CSCE 31903 or CSCE 319H3 or DASC 21003) or (CSCE 20104 and INEG 23303 and INEG 23104) or (CSCE 20104 and STAT 30133 and STAT 30043)). (Typically offered: Fall)
This course is cross-listed with INEG 41403.

Introduction to tools and techniques for distributed data computing and management, big data analytics, scalable machine learning, and real-time streaming data analysis. Students cannot receive credit for both CSCE 42703 and CSCE 52703. Prerequisite: CSCE 31903 or CSCE 319H3 or DASC 21003, each with a grade of C or better. (Typically offered: Irregular)

Introduction to database management systems, architecture, storage structures, indexing, relational data model, E-R diagrams, query languages, SQL, ODBC, transaction management, integrity, and security. Prerequisite: CSCE 3193 or CSCE 3193H with a C or better. (Typically offered: Spring)

Introduction to intelligent agents, AI languages, search, first order logic, knowledge representation, ontologies, problem solving, natural language processing, machine vision, machine learning, and robotics. Prerequisite: CSCE 31903 or CSCE 319H3 or DASC 21003, each with a grade of C or better. (Typically offered: Irregular)

An introductory course in biostatistics emphasizing methods for collecting, graphing, and understanding data. Special emphasis is placed upon available methods for both exploratory and confirmatory data analysis. Particular attention is given to statistical methods for data sets with discrete variables. Pre- or Corequisite: MATH 24004. Corequisite: Lab component. (Typically offered: Spring)

Introduction to the statistical software SAS, including its use for common statistical analyses. A practical complement to the statistical methodology covered in STAT 30043. (Typically offered: Fall and Spring)

Describing Data, Basic Probability, Random variables, Uniform, Normal and Binomial Distributions, Sampling Distributions, Confidence Intervals, Hypothesis testing, Correlation and Regression, Contingency table, Comparing two populations, ANOVA. Prerequisite: MATH 24004. (Typically offered: Fall and Spring)

A calculus-based introduction to probability. Discrete probability spaces and counting techniques, discrete and continuous probability distributions, random variables, random samples, law of large numbers, central limit theorem. Prerequisite: MATH 25004. (Typically offered: Fall, Spring and Summer)
This course is cross-listed with MATH 30103.

Provides an in depth look at the theory and practice of applied modeling of temporal data for data science, including model building, selection, autocorrelation, autoregression and moving averages, and prediction for correlated data. Students will gain experience using statistical software to learn from data using applied time series and models. Prerequisite: DASC 3213 or approval by the instructor. (Typically offered: Fall)

Provides an introductory look at the theory and practice of applied Bayesian modeling for data science: including model building, selection, regularization, classification and prediction. Students will gain experience using statistical software to learn from data using applied Bayesian models. Prerequisite: DASC 3213 or approval by the instructor. (Typically offered: Spring)

Chi square tests. Kolmogorov-Smirnov goodness-of-fit tests, the Mann-Whitney and Wilcoxon 2-sampling tests, and various nonparametric measures of association. Prerequisite: STAT 2303 or STAT 2823 or departmental consent. (Typically offered: Fall, Spring and Summer)

Considers optimum techniques of simple random, stratified random, cluster, systematic and multistage sampling from finite populations subject to cost precision constraints. Wide range of applications. (Typically offered: Fall, Spring and Summer)

A hands-on introduction to R software, a free and open-source computing environment used for data manipulation and analysis across a broad spectrum of subject areas. Intended for new users. Content begins with simple data manipulation, then complex data structures and common statistical procedures are covered. (Typically offered: Fall)

Statistical tools to analyze univariate and multivariate categorical responses. Emphasis is given to Generalized Linear Models, including logistic regression and loglinear models. Prerequisite: STAT 30043 or departmental consent. (Typically offered: Spring)

Systems of linear equations, vector spaces, linear transformations, matrices, and determinants. Only one of MATH 3083 and MATH 3093 will count for credit. Prerequisite: MATH 2554 or MATH 2043, with a grade of C or better. (Typically offered: Fall, Spring and Summer)

Provides an understanding of a diverse set of problems, as well as algorithms for solving them and implementing the algorithms using high performance computing resources and environments. The emphasis is on problem solving and offers multiple projects concerning applications in science and engineering. Prerequisite: MATH 3083 or MATH 3093. (Typically offered: Spring)

Numerical methods for problems of linear algebra, including the solution of very large systems, eigenvalues, and eigenvectors. Prerequisite: MATH 3083 or MATH 3093. (Typically offered: Spring)

General iterative techniques, error analysis, root finding, interpolation, approximation, numerical integration, and numerical solution of differential equations. Prerequisite: MATH 2584. (Typically offered: Fall)

This course presents the fundamental concepts used in developing information systems. It provides a framework for students to use throughout their software development coursework. Also includes management of information systems concepts. This course requires extensive use of computer systems. Prerequisite: ACCT 20103, MATH 20503 and ISYS 21003, each with a grade of C or better. (Typically offered: Fall and Spring)

Principles of design and development of windows and web applications using cutting edge visual development tools. The programming language will be a modern language used widely in industry, and the focus will be on its use in client-server, web, and/or mobile applications. Pre- or Corequisite: ISYS 3293. Prerequisite: ISYS 2263 or CSCE 2014 with a grade of "C" or better. (Typically offered: Fall and Spring)

Introductory study of business analytics, visualization, and systems to provide analytics-based information derived from data within and/or external to the organization. Business analytics used to support management in the decision making. Application of tools in business analytics, problem solving, visualization, and decision making. Prerequisite: Walton College Majors: BUSI 10303 with a grade of C or better; Non-Business Majors: INEG 23104 or STAT 30133 or MATH 21003 each with a grade of C or better. (Typically offered: Fall)

Introduces student to centralized information system design and implementation for business applications. In-depth study of logical systems modeling; physical file management; and software requirements. Pre- or Corequisite: ISYS 3393. Prerequisite: ISYS 3293 with a grade of C or better. (Typically offered: Fall)

Business intelligence focuses on creating, developing and storing information and knowledge from internal and external sources to better support business decisions. We will consider techniques from machine learning, data mining, and information retrieval to extract useful knowledge from data, which could be used for business intelligence, personalization or user profiling. Prerequisite: ISYS 41903 with a grade of "C" or better. (Typically offered: Spring)

This course covers object-oriented programming concepts and illustrates them via an appropriate object-oriented programming language. Students will be exposed to the design of software objects, creation of software objects, and the use of objects in constructing an information system. Prerequisite: ISYS 3293 with a grade of C or better. (Typically offered: Fall)

This course focuses on an introduction to image processing and analysis for applications in biomedical research. After a review of basic MATLAB usage, students will learn fundamental tools for processing and analyzing data from a variety of subdisciplines within biomedical engineering. Topics include: filtering, thresholding, segmentation, morphological processing, and image registration. Through exercises involving 1D, 2D, and 3D data, students will develop problem-solving skills and a knowledge base in MATLAB required for customized quantitative data analysis. Students may not receive credit for both BMEG 4523 and BMEG 5523. Prerequisite: BMEG 3124 and BMEG 3653. (Typically offered: Irregular)

An introduction to the methods and theory underlying the full range of geographic information science and collateral areas - including GNSS, remote sensing, cadastral, spatial demographics and others. (Typically offered: Fall and Spring) This course is cross-listed with ANTH 3543.

Applications of analysis of spatial data using ArcGIS tools in map design, on-line mapping, creating geodatabases, accessing geospatial data, geo-processing, digitizing, geocoding, spatial analysis including basic spatial statistics, analysis of spatial distributions and patterning and 3D application using ArcGIS 3D Analyst. Prerequisite: GEOS 3543. (Typically offered: Fall and Spring)

Basic tools for analyzing, summarizing and visualizing geospatial data. Exploratory data and spatial data analysis, probability distributions and application, single and multivariate analysis and hypothesis testing, and spatial smoothing and interpolation. Emphasis will be on problem solving in geospatial settings using the R statistical language. Prerequisite: GEOS 3543 and (GEOS 3013 or MATH 2554 or MATH 2043 or DASC 2594) and (GEOS 3103 or DASC 1104 or DASC 2113). (Typically offered: Fall and Spring)

Fundamental concepts and applications of geospatial databases. Schema development and spatial data models for geodata. Spatial and attribute query and optimization, properties and structures of relational and object-oriented geodatabases. Spatial extensions of SQL, spatial indexing, measurement, and geometry. Prerequisite: GEOS 3543 and (GEOS 3013 or MATH 2554 or MATH 2043 or DASC 2594) and (GEOS 3103 or DASC 1104 or DASC 2113). (Typically offered: Fall and Spring)

Covers the wide range of geospatial data sources and characteristics with emphasis on data science applications through hands-on experience recognizing the unique requirements of major sources. Techniques for the integration of disparate, heterogeneous data sets will be covered. Corequisite: GEOS 3563. Prerequisite: GEOS 3543. (Typically offered: Fall)

Unlike conventional GIS courses that focus on studying "where", this course will teach students to address beyond "where" using various GIS analysis and modeling techniques to explore "why" and "how". The course will provide theoretical and methodological reviews of the principles of cartographic modeling and multi-criteria decision-making. (Typically offered: Spring) This course is cross-listed with ANTH 4653.