Electives

In addition to the courses listed below, you may choose to take courses in another major as your electives.

ACC 7148 ERP Systems and Business Integration (3 credits) - Enterprise Planning (ERP) systems are the primary software packages for accounting, operational, and managerial activities of organizations. How ERP systems integrate and coordinate business processes and the management of the organization. Extensive hands-on use of popular software packages for key business activities such as sales, procurement, and production. Prerequisites: BA 7000 with a minimum grade of C and ISM 7500 with a minimum grade of C.

ACC 7280 Accounting Data Analytics (3 credits) - Introduces concepts, techniques, and software applications used to analyze accounting and related data to support financial decision-making and planning. These data are generated both within and outside the organization. Prerequisite: BA 7000 with a minimum grade of C.

ACC 7290 / ISM 7290 Blockchain: An Accounting and Business Perspective (3 credits) - Introduces blockchain: a public, transparent, secure, immutable, and distributed ledger. Blockchains can be used to record and transfer any digital asset, not just currency. The course covers the workings, applications, and potential impact of this revolutionary technology. Prerequisite: BA 7000 with a minimum grade of C.

CSC 5050 Algorithms and Data Structures (3 credits) - Introduction to problem solving methods and algorithm development; data abstraction for structures such as stacks, queues, linked lists, trees, and graphs; searching and sorting algorithms and their analysis.

CSC 5250 Network, Distributed, and Concurrent Programming (3 credits) - Fundamental concepts and skills of developing networked, distributed, and concurrent applications. Topics include: inter-process communication, TCP/IP sockets programming, remote method invocation, multithreading, concurrency and synchronization. Prerequisites: CSC 4420 with a minimum grade of C- CSC 6800 Artificial Intelligence I (3 Credits) - Basic concepts; topics include: recursive problem solving, knowledge representation using semantic networks and frames, state space search methods, planning and problem solving, game playing and adversarial search methods, rules and production systems (RETE networks), constraint satisfaction techniques and applications, optimization algorithms including genetic algorithms, logic programming. Implementation in Lisp and Prolog. Prerequisite: CSC 3110 with a minimum grade of C-.

CSC 6860 Digital Image Processing and Analysis (3 credits) Review of image formation and acquisition; image transformation; image enhancement and restoration; image compression; morphological image processing; edge detection and segmentation; architecture for image processing.

CSC 7220 Parallel Computing II: Algorithms and Applications (3 credits) - Problems in parallel algorithms: design, analysis, complexity. Cluster and grid computing: tools, programming, and applications. Prerequisite: CSC 6220 with a minimum grade of C.

CSC 7260 Distributed Systems (3 credits) - Models of distributed systems, distributed synchronization, algorithms, consistency and replication models and algorithms, fault-tolerance in distributed systems. Prerequisite: CSC 5250 with a minimum grade of C.

CSC 7300 Bioinformatics I: Biological Databases and Data Analysis (3 credits) - Concepts of bioinformatics; tools for storing and analysis of bioinformatics data. Must be taken with CSC 7301.

CSC 7301 Bioinformatics I: Programming Lab (1 credit) - Hands-on experience and exercises for CSC 7300/MBG 7300 lectures.

ECE 7610 Advanced Parallel and Distributed Systems (3 credits) - Advanced topics in parallel and distributed computing, multicore and parallel architecture, communication, synchronization, parallel algorithms and programming, load balancing and scheduling, security. Prerequisite: ECE 5610 or ECE 5650.

ECO 7100 Econometrics I (4 credits) - Probability and statistics: moment generating functions, common families of statistical distributions, multiple random variables and properties of a random sample. Estimation and hypothesis testing: method of moments, generalized method of moments, maximum likelihood estimators, instrumental variable estimators, Bayes estimators, likelihood ratio tests, finite sample properties and asymptotic properties of OLS. Prerequisites: ECO 6100 with a minimum grade of C and ECO 7020 with a minimum grade of C.

ECO 7110 Econometrics II (4 credits) - Modeling and estimation: generalized least squares, panel data models (fixed effects and random effects), system of equations (endogeneity, identification), models with discrete dependent variables (probit, logit), models with limited dependent variables (truncation, censoring), stationary time-series (ARMA), vector-autoregression (VAR, VMA), non-stationary time-series (unit roots, cointegration). Prerequisite: ECO 7100 with a minimum grade of C.

ECO 7120 Econometrics III (4 credits) - Advanced economic techniques in microeconomics and macroeconomics. In the first half of the course, emphasis on specification, estimation, interpretation, and testing of microeconomic models. The second half will cover statistical models for the analysis of economic time series data, with applications in macroeconomics and finance. Prerequisites: ECO 7100 with a minimum grade of C and ECO 7110 with a minimum grade of C.

IE 6010 IoT and Edge AI Programming (3 credits) - Learn sensor programming on an embedded device; use Wi-Fi, Bluetooth and MQTT to implement data streaming, remote control, and multi-device networking; explore the IoT data processing life cycle which includes capturing, cloud storage, and data analysis; develop and deploy machine learning models for use in mobile and edge computing environments. Offered Winter.

Restriction(s): Enrollment is limited to Graduate level students.

IE 6325 Supply Chain Management (3 credits) - Supply chain management and logistics is unique and, to some degree, represents a paradox because it is concerned with one of the oldest and also the most newly discovered activities of business. Supply chain system activities - communication, inventory management, warehousing, transportation, facility location, and production - have been performed since the start of commercial activity. It is difficult to visualize any product that could reach a customer without logistical support. Yet, it is only over the last decade that firms have started focusing on logistics and supply chain management as a source of competitive advantage. Logistics and supply chain management today represents a great challenge as well as a tremendous opportunity for most firms. Another term that has appeared in business jargon recently is demand chain. From our perspective, we will use the phrases logistics management, supply chain management, and demand chain management interchangeably. 

IE 6720 Engineering Risk and Decision Analysis (3 credits) - Structure, modeling and analysis of technical management decisions with emphasis on multiple objectives and trade-offs, and significant uncertainty. Explores barriers to rational decision making.

IE 7860 Intelligent Analytics (3 credits) - Computational intelligence methods used to solve complex analytics problems and develop decision support systems. Project-centric approach with the goal of developing several analytics solutions for real-world problems.

ISM 7505 Information Analytics: Inbound Information Technology (3 credits) - The evolving cyberspace organization. Insights and practical guidelines to create an appealing and engaging digital presence. Discussion focuses on topics relevant to planning, managing, and implementing online and social media interactivity such as search engine organization (SEO), inbound links, blogging, page ranking, tagging content, tweeting, publishing content, analytic reports, and social media.

ISM 7510 Database Management (3 credits) - Overall examination of database management and knowledge management systems. Theories, models, and techniques for designing, developing, understanding, utilizing and creating competitive advantage through database systems. Topics include data modeling, logical and physical database design, strategic value of data, introductory SQL, knowledge management, and emerging database technologies.

ISM 7570 Business Analytics (3 credits) - This course focuses on learning skills necessary for generating insights from data to aid business decision making. Students will learn how to ingest, prep, transform, visualize and analyze data using the popular open source data science tool - R. Specifically, the course will cover descriptive analytics (e.g., data visualization, query, data slicing), and, predictive analytics (e.g., regression, clustering, classification). Basic programming experience is recommended but not required. No credit after ISM 5570. 

ISM 7994 Digital Content Development (3 credits) - Development of responsive, smart, and personalized web sites using leading web development tools and technologies.

ISM 7996 Principles for Customer Relationship Management (3 credits) - Investigation of the antecedents and consequences of implementing a customer-relationship management strategy. The course will provide students with insight on: What CRM and its conceptual foundations are; How CRM forces the interaction between corporate strategy, organizational structure, supply chain, and customer facing front end; The role of measuring and managing customer satisfaction, customer loyalty and customer profitability; Hands-on application with salesforce.com.

STA 5830 Applied Time Series (3 credits) - Time series models, moving average models, autoregressive models, non-stationary models, and more general models; point estimators, confidence intervals, and forecast in the time domain. Statistical analysis in the frequency domain; spectral density and periodogram. Prerequisites:  (MAT 2250 with a minimum grade of C- or MAT 2150 with a minimum grade of C-) and (MAT 2210 with a minimum grade of C-, STA 2210 with a minimum grade of C-, BE 2100 with a minimum grade of C-, ECO 5100 with a minimum grade of C-, or PH 3200 with a minimum grade of C-)

STA 6840 Applied Regression Analysis (3 credits) -Multiple linear regression; generalized linear models; random effect models; repeated measurements; mixed effect models; non-parametric additive models. Computer implementation using statistical software R; student project on real data analysis. Prerequisites: STA 5030 with a minimum grade of C- or STA 5800 with a minimum grade of C-