Management Science and Information Systems

22:135:571 Calculus for Managers (2) Topics include functions, lines, quadratic equations, exponential and logarithmic functions, limits, derivatives, partial derivatives, one and two variable function optimization, Lagrange multipliers, matrix algebra, and solutions to linear equations.

22:135:572 Statistics for Managers (2) Topics include descriptive statistics, elementary probability, discrete distributions, normal distributions, sampling distributions, small and large sample inference for an unknown population mean, and proportions.

22:198:504 Information Technology for Managers - FT (2) The objective of this course is to study management's role in the development and use of information systems that help businesses achieve their goals and objectives. Information technology (IT) has been the driving force behind the new way of doing business. IT has enabled modern organizations to make tremendous strides in productivity, has opened new markets, and has created new product and service opportunities. Managers should understand how IT could help to organize the complexity of modern organizations; manage relationships with customers, suppliers, and employees; and improve work efficiency.

22:198:603 Business Data Management (3) The purpose of this course is to provide students with an understanding of database technology and its application in managing data resources. The conceptual, logical, and physical design of databases will be analyzed. A database management system such as ORACLE or INGRES will be used as a vehicle for illustrating some of the concepts discussed in the course. Cross-listed as 22:544:603. Prerequisite: Background in a procedurally oriented language (C preferred) or permission of the instructor.                      

22:198:604 Computers and Information Systems (3) This general concepts course provides an understanding of the hardware, software, and other components of computer systems; it surveys file and database management systems and telecommunications and networks; analysis, design, and development of computer-based information systems; and evaluation of computer acquisitions.
This course is an alternative to 22:198:605 Introduction to Software Development.

22:198:605 Introduction to Software Development (3) Fundamentals of the C/C++ programming language comprise a major part of this course.  Introduces students to procedural and object-oriented programming. Topics from data storage, systems analysis, and database systems are also covered. Cross-listed as 22:544:605.

22:198:609 Information Technology for Managers - PT (2) The objective of this course is to study management's role in the development and use of information systems that help businesses achieve their goals and objectives. Information technology (IT) has been the driving force behind the new way of doing business. IT has enabled modern organizations to make tremendous strides in productivity, has opened new markets, and has created new product and service opportunities. Managers should understand how IT could help to organize the complexity of modern organizations; manage relationships with customers, suppliers, and employees; and improve work efficiency.

22:198:611 Security for Electronic Commerce (3) The objective of this course is to introduce to students the emerging area of electronic commerce (EC) and the security challenges and threats in EC, and provide them with an understanding of the state-of-the-art EC security technologies. In particular, this course discusses security requirements for electronic commerce such as identification and authentication, authorization and access control, data integrity, confidentiality, nonrepudiation, trust, and regulation. Discusses various security standards including network security architecture standards, data encryption standards, data integrity standards, digital signature standards, authentication standards, certification standards, electronic data interchange standards, and electronic mail standards. It also discusses the emerging internet standards, firewalls, public key cryptography standards, Java security, Lotus Notes security, database security, security payments such as SET (secure electronic transaction), digital cash and digital checks, and smart card technology.

22:198:650 Data Mining (3) Recent advances in information technology along with the phenomenal growth of the internet have resulted in an explosion of data collected, stored, and disseminated by various organizations. 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 the large data repositories. Awareness of the importance of data mining for business is becoming widespread. The industry has created more and more job opportunities for people who have interdisciplinary data analytic skills. Indeed, this course intends to bridge the gap between data mining techniques and business applications. The students have the opportunities to learn both domain and technical knowledge to face the big data challenges in the industry. Cross-listed as 22:544:650.

22:198:670 IT Strategy (3) Over the last few years information technology (IT) teams have evolved and continue evolving to establish IT organizations as business strategic partners, and CIOs and technology leaders are now included in the executive teams and are expected to play a leading role in delivering business value while solving both business and technical problems. Companies are increasing their investments in acquiring and maintaining information on themselves, the markets, and competitors, and they need systems and IT teams to enable a strategic use of the information that makes it a business asset to the organization. Developing and executing an effective IT strategy that enables business strategy is critical for creating business value and gaining competitive advantage. This course presents a framework and methodology for assessing, developing, and implementing an effective IT strategy that is aligned with business needs. The course will be a combination of directed readings, lectures, case studies, one individual assignment, and one group project. Cross-listed as 22:544:670.

22:544:608 Business Forecasting (3) Innovative businesses are using data to make better predictions about their business environment, their business future, and the future of their global competitors. Businesses are storing and collecting more data than ever before to gain a competitive edge. This will result in businesses looking for better data scientists to help them leverage "Big Data" and gain a competitive edge. In this class, students will use the level R programming language to become data scientists and business forecasters. Specifically, students will learn how to: understand data, analyze data, apply various forecasting methods, leverage forecasts to make decisions, communicate forecasts and recommendations to management. No prior knowledge of R programming is required.

22:544:613 Introduction to Data Structures and Algorithms (3) This course covers basic notions such as the Big Oh notation, recursion and recurrent equations, the divide and conquer, greedy and dynamic programming paradigms, data structures such as arrays, lists, stacks and queues, priority queues, balanced binary search trees, hash tables, dictionaries, and disjoint union-find trees, graphs, adjacency lists, and other graph data structures. Algorithms for sorting, polynomials, Fast Fourier Transform, modular arithmetic, primality testing, public-key cryptography, depth and breadth-first search techniques, the minimum spanning tree, shortest paths, and network flows. The concept of NP-completeness and intractability.

22:544:631 Algorithmic Machine Learning (3) This course covers the most fundamental aspects of machine learning. Bayes decision rule, regularization, overfitting, cross validation, AIC and BIC, bias-variance tradeoff, Bayesian point of view, maximum likelihood and maximum a posteriori, cross entropy. Methods such as k Nearest Neighbor, Naļve-Bayes, Classification and Regression Trees, random forest, support vector machines, linear and logistic regression, k-means and hierarchical clustering, Page rank, principal component analysis, and the expectation-maximization algorithms are covered. Software implementation and testing of algorithms and extensive use of machine learning libraries is utilized. A group semester project is required.

22:544:634 Optimization Methods for Machine Learning (3) This course covers convex sets and functions, unconstrained and constrained optimization, applications to matrix completion, matrix factorization, least squares, logistic regression, and principal component analysis, Gradient Descent methods, Franke-Wolfe algorithm, momentum methods, Polyak and Nesterov accelerations, AdaGrad and Adam, Lagranian duality and Karush-Kuhn-Ticker conditions, Stochastic Gradient Descent, acceleration methods for stochastic gradient approach, coordinate descent methods, quasi-Newton methods, applications to neural networks, and deep learning.

22:544:635 Neural Networks and Deep Learning (3) This course covers basic techniques for neural networks and deep learning, sequential dense, convolutional, recurrent, autoencoders, variational autoencoders, Boltzmann machines, generative adversarial networks. The backpropagation algorithm and various optimization techniques for neural networks are covered. Applications in regression, classification, feature extraction, and generative models and natural language and pattern recognition are covered. Software libraries such as Tensorflow, PyTorch, Keras are extensively used. Available hardware on public cloud computing platforms are introduced and used. Students will participate in a substantial group semester project.

22:544:637 Reinforcement Learning (3) Introduction to the basic theory and models of reinforcement learning, the exploration/exploitation model, understanding the e-greedy algorithms, the Gittins indices, posterior sampling, UCB, and optimistic programming methodologies. Software available on public cloud computing platforms are introduced and used.

22:544:640 Fundamentals of Blockchain and Distributed Ledgers (3) Overview of cryptography, overview of blockchain, the notion of distributed ledgers. Cryptographic hash functions, overview of blockchain architecture, overview of bitcoin, Etherium and Libra architectures, vulnerabilities, applications of blockchain beyond cryptocurrencies, smart contracts. The course contains lab assignments.

22:544:641 Analytics for Business Intelligence (3) The course will cover classification (e.g., helps banks to determine who will default on a loan, or email filters to determine which emails are spam); clustering (like classification, but groups are not predefined, as in legitimate vs. spam email, so the algorithm will try to group similar email together for instance); regression (e.g., how ad campaigns in offline media such as print, audio, and TV affect online interest in the advertiser's brand); Association Rule Learning (enables merchants, for example Amazon, to determine which items customers tend to buy together and make suggestions for further purchase, otherwise known as "market basket analysis"); and Neural Nets (has helped financial agents to model complex currency market trading).

22:544:643 Information Security (3) This course prepares students to meet the new challenges in the world of increasing threats to computer security by providing them with an understanding of the various threats and countermeasures. Specifically, students will learn the theoretical advancements in information security, state-of-the-art techniques, standards, and best practices. In particular, the topics covered in this course include: study of security policies, models, and mechanisms for secrecy, integrity, and availability; operating system models and mechanisms for mandatory and discretionary controls; data models, concepts, and mechanisms for database security; basic cryptology and its applications; security in computer networks and distributed systems; identity threat; control and prevention of viruses and other rogue programs.

22:544:646 Data Analysis and Visualization (3) The course will enable students to develop critical business data presentation skills to ensure that the visualizations add to the effective interpretation and explanation of the underlying data without undue strain to the consumer of the information; ensure the visualizations enable the effective detection of trends that can be easily connected to real-world events to help explain relationships and interrelationships; learn appropriate and minimal use of color to maximize its impact. Spatial data analysis tools will be introduced and advanced graphical programming skills will be developed using R graphics packages.

22:544:660 Business Analytics Programming (3) This course covers the principles of programming for business analytics using the Python and R programming languages. Programming is the fundamental background skill based on which all information systems are built. Even if it is not your goal to become a software developer, it is essential for an MBA graduate with concentration in analytics and information management to possess a working knowledge of programming and fundamental insights into what a programmer does. This course provides you with this essential knowledge.

22:544:688 MIT Capstone Project (3) Students pursue independent project/research on a topic of their choice with the guidance of a faculty adviser.

22:711:685 Dynamic Pricing and Revenue Management (3) This course provides students a basic understanding on the modern theory and practice of dynamic pricing and revenue management. It covers topics such as market-response models, economics of revenue management, estimation and forecasting, single-resource capacity control, network capacity control, overbooking, dynamic pricing of reversible and irreversible varieties, auction, competitive pricing, joint inventory-price control, and control with ambiguity.

22:960:575 Data Analysis and Decision-Making (3) Introduces statistics as applied to managerial problems. Emphasis is on conceptual understanding as well as conducting statistical analyses. Students learn the limitations and potential of statistics, gain hands-on experience using Excel, as well as comprehensive packages, such as SPSS®. Topics include descriptive statistics, continuous distributions, confidence intervals for means and proportions, and regression. Application areas include finance, operations, and marketing. Introduces the basic concepts of model building and its role in rational decision-making. Knowledge of specific modeling techniques, such as linear and nonlinear programming, decision analysis, and simulation, along with some insight into their practical application is acquired. Students are encouraged to take an analytic view of decision-making by formalizing trade-offs, specifying constraints, providing for uncertainty, and performing sensitivity analyses. Students form groups to collect and analyze data, and to write and present a final report. Cross-listed as 22:544:575. Prerequisite: 22:135:572 with grade of B or better.           

26:198:621 Electronic Commerce (3) This course will cover the theoretical foundations, implementation problems, and research issues of the emerging area of electronic commerce. It will discuss technological, conceptual, and methodological aspects of electronic commerce. The list of topics to be covered  includes: fundamentals of internet technology, pricing of and accounting for internet transport, security problems of the internet, electronic payment systems, online financial reporting and auditing, intelligent agents, web measurements, electronic markets, and value chain over the internet. The coursework will include presentations of research articles, in-class discussions, and a final course project researching one of the problems of electronic commerce.
Prerequisites: Basic computer literacy; introductory courses in computer information systems and economics.

26:198:622 Machine Learning (3) Basic theory of rule-based systems and Bayes networks. Alternative architectures for managing uncertainty. Use of probabilistic logic to model causality. Related ideas from machine learning, neural networks, and genetic algorithms. Applications to auditing, marketing, and production.

26:198:641 Advanced Database Systems (3) Emphasizes the functions of database administrator. Includes survey of physical and logical organization of data and their methods of accessing, and the characteristics of different models of generalized database management systems. Prerequisite: A master's-level course in databases such as 22:198:603 or NJIT CIS 631.

26:198:642 Multimedia Information Systems (3) This course covers principal topics related to multimedia information systems. These include organizing multimedia content, physical storage and retrieval of multimedia data, content-based search and retrieval, creating and delivering networked and multimedia presentations, and current research directions in this area. Prerequisite: A master's-level course in databases such as 22:198:603.          

26:198:645 Data Privacy (3) Given the ubiquity of data collection and analysis nowadays, the challenge is to enable the legitimate use of collected data without violating privacy. From the organizational perspective, enabling safe and secure use of owned data can lead to great value added and return on investment. This course enables students to analyze the legal and social aspects of privacy and explores potential tools, techniques, and technologies that can enhance privacy. The course introduces students to the core issues surrounding privacy, security, data storage and analysis, and the technologies that have been developed to address those issues.

26:711:561 Mathematical Methods for Economics (3) Explores the quantitative tools and principles used to model operational procedures in economic and business systems: types of variables, mathematical sets, and functional forms in constrained and unconstrained optimization. Other topics include tractability, duality, Kuhn-Tucker theory, algorithms, and computation. Prerequisite: Differential calculus.

26:711:651 Linear Programming (3) A survey of linear programming and its applications. Topics include linear programming models, basic simplex method, duality theory and complementary slackness, sensitivity analysis, degeneracy, matrix notation and revised simplex method, special linear programs such as transportation and network flow theory, applications in statistics, economics and finance models of linear programming, game theory, and introduction to interior point methods. Prerequisite: Undergraduate linear algebra.

26:711:652 Nonlinear Programming (3) Fundamentals of nonlinear optimization, with an emphasis on convex problems. Gradient, Newton, and other methods for unconstrained problems. Projection, linearization, penalty, barrier, and augmented Lagrangian methods for constrained problems. Lagrangian functions and duality theory. Assignments include computer programming and mathematical proofs. Prerequisite: 26:711:651.

26:960:575 Introduction to Probability (3) Foundations of probability. Discrete and continuous simple and multivariate probability distributions; random walks; generating functions; linear functions of random variable; approximate means and variances; exact methods of finding moments; limit theorems; stochastic processes including immigration-emigration, simple queuing, renewal theory, and Markov chains. Prerequisite: Undergraduate or master's-level course in statistics.        

26:960:577 Introduction to Statistical Linear Models (3) Linear models and their application to empirical data. The general linear model; ordinary-least-squares estimation; diagnostics, including departures from underlying assumptions, detection of outliners, effects of influential observations, and leverage; analysis of variance, including one-way layouts, two-way, and higher dimensional layouts, partitioning sums of squares, and incomplete layouts (Latin squares, incomplete blocks, and nested or repeated measures). Emphasizes computational aspects and use of standard computer packages such as SAS. Prerequisite: Undergraduate or master's-level course in statistics.      

26:960:580 Stochastic Processes (3) Review of probability theory with emphasis on conditional expectations; Markov chains; the Poisson process; continuous-time Markov chains; renewal theory; queuing theory; and introduction to stochastic calculus, e.g., Ito's Lemma. Prerequisite: 26:960:575.