Level: Graduate 

This course aims to give students the chance to apply curriculum skills acquired in their graduate program to consulting projects with a live client. Teams of several graduate students will finalize scope, plan and execute a project for an organization to deliver value to them. Teams will have support from the Eller Business Consulting department, the BCOM department and a faculty member who is a relevant subject matter expert. The learning objectives include (a) learning how companies make decisions in real, time-constrained, often politically charged environments, (b) applying skills and knowledge gained from the classroom to a business situation or problem, (c) learning and using project and client management skills and (d) communicating findings and recommendations in a professional manner.

Units: 3

Level: Graduate 

This course introduces students to the concepts and practices of healthcare information systems. (1) introduction to the health IT discipline; (2) major applications and commercial vendors; (3)  decision support methods and technologies; (4) information systems design and engineering; and (5) new opportunities and emerging trends. A semester-long group project will provide students hands-on experience in planning and building healthcare information systems; associated ethical and legal concerns, software engineering and human-computer interaction issues and user acceptance and outcomes evaluation methods will also be discussed.

Units: 3

Level: Graduate 

This course is an overview of the methods, processes and functions necessary for effective communication in today's high tech, global marketplace. The goals for this course are to develop an understanding of the need for and the requisite skills of competent communication in both the physical and electronic environments. 
Note: Not open to non-degree seeking students.

Units: 3

Level: Graduate 

Broad survey of the individual, organizational, cultural, social and ethical issues provoked by current and projected uses of networked computers on the Internet. Graduate-level requirements include an additional term paper.

Units: 3

Level: Graduate 

This course will integrate many business foundations in support of MIS students in the MS program. In today's environment, IT solutions have to support the competitive needs of organizations and recognize the inter-organizational nature of business processes. In addition, the IT solutions have to support the financial well-being of a firm as well as its responsibility to various stakeholders. This course uses five modules: business strategy in a global environment, process analysis and re-design in an ever expanding value chain; IT in support of these business processes, economic justification and social implications. This course is also offered via the MISonline program.

Note: Not open to non-degree seeking students.

Units: 3

Level: Graduate 

This course exposes the student to a broad range of computer systems and information security topics. It is designed to provide a general knowledge of measures to insure confidentiality, availability and integrity of information systems. Topics range from hardware, software and network security to INFOSEC, OPSEC and NSTISS overviews. Components include national policy, threats, countermeasures and risk management among others. Graduate-level requirements include an oral case study report as their final. This course is also offered via the MISonline program.

Units: 3

Level: Graduate 

The objective of our MIS 516 course is to provide our University of Arizona students with a thorough and operational knowledge of information security so that this critical area is recognized as a management issue and not an IT issue. This course is also offered online.

Units: 3

Level: Graduate 

The information security arena contains a broad array of multi-level models for assessing, planning, implementing and monitoring the mitigation of security risks. At the very core of this information security spectrum are the actual system and network devices which store, manage, transmit and secure information. This course is designed to provide a working knowledge of issues and techniques surrounding the proper safeguarding of operating systems and related components. Filled with Information Assurance topics, this course offers a solid base for system administrators and technical managers. This course is also offered online.

Units: 3

Level: Graduate 

Enterprise Resource Planning (ERP) systems represents integrated strategy for management of information among organizations, suppliers and customers. Graduate-level requirements include completion of a group project on an advanced complementary or enabling technology using ERP. Students' projects include implementation or demonstration and presentation to class.

Units : 3

Level: Graduate 

This course introduces the student to fundamentals of database analysis, design and implementation. Emphasis is on practical aspects of business process analysis and the accompanying database design and development. Topics covered include: conceptual design of databases using the entity relationship model, relational design and normalization, SQL and PL/SQL, web-based database design and implementation using Oracle or some other modern Database Management Systems. Students are required to work with a local client organization in understanding their business requirements, developing a detailed set of requirements to support business processes and designing and implementing a web-based database application to support their day-to-day business operations and decision making. Students will acquire hands-on-experience with a state-of-the-art database management system such as Oracle or Microsoft SQLServer and web-based development tools. This course is also offered via the MISonline program.

Units: 3

Level: Graduate 

This course provides an understanding and application of system analysis and design processes centered on the systems development life cycle. Core topics include: project management and cost-benefit analysis; information systems planning and project identification and selection; requirements collection and structuring; process modeling; conceptual and logical data modeling; database design and implementation; design of the human-computer interface (HCI); system implementation; system maintenance and change management. Students will also be introduced to comparative development methodologies and modeling tools. The course involves a substantial project where students will learn the importance of effective communication and integration with users and user systems. The course emphasizes interpersonal skill development with clients, users, team members and others associated with development, operation and maintenance of systems. This course is also offered via the MISonline program.

Units: 3

Level: Graduate 

This course provides an in-depth knowledge of data communications and networking requirements, including networking technologies, hardware and software. This course has two objectives. First, it focuses on basic networking standards and protocols. Second, students will learn to evaluate, select and implement different data network options and prepare a cost-benefit analysis for a proposed solution. This course may be offered as either a ground or distance learning course.

Units: 3

Level: Graduate 

Corporations today are said to be data rich but information poor. For example, retailers can easily process and capture millions of transactions every day. In addition, the widespread proliferation of economic activity on the Internet leaves behind a rich trail of micro-level data on consumers, their purchases, retailers and their offerings, auction bidding, music sharing and more. Data mining techniques can help companies discover knowledge and acquire business intelligence from these massive datasets. This course will cover data mining for business intelligence. Data mining refers to extracting or “mining” knowledge from large amounts of data. It consists of several techniques that aim at discovering rich and interesting patterns that can bring value or “business intelligence” to organizations. Examples of such patterns include fraud detection, consumer behavior and credit approval. The course will cover the most important data mining techniques—classification, clustering, association rule mining, visualization, prediction—through a hands-on approach using XL Miner and other specialized software, such as the open-source WEKA software. This course is also offered via the MISonline program.

Units: 3

This course gives students a deep exposure to Cloud Computing, its enabling technologies, main building blocks, design strategies, and an in-depth understanding through home-works, projects, and exams. Cloud computing has shaped our lives in many ways. Every one of us knowingly or unknowingly is using a number of cloud computing services in our daily life. These include shopping (e.g. Amazon), education (e.g. Coursera), health (e.g. UnitedHealth), social media (e.g. Facebook), entertainment (e.g. Youtube) and many more. The success of cloud computing is attributed to its ability to deliver computing as a service over the network, whereby distributed resources are rented, rather than owned, by an end user as a utility.

Units: 3

Level: Graduate 

This course provides a broad introduction to the concepts, techniques, applications, and tools (mainly Python-based ones) of deep learning (DL). The course will cover a variety of DL methods that have been developed to address various modeling and learning challenges across many applications such as image classification, text data analysis, online user modeling, and recommender systems. Specifically, the course content will include the following modules: deep learning (DL) foundation, multilayer neural networks and linear models, training and optimization, Radial basis function (RBF) networks, convolutional neural networks (CNN), recurrent neural networks (RNN), graph neural networks (GNN) and representation learning, attention mechanism, neural networks for adversarial learning, DL language models, DL for user modeling and recommendations, and reinforcement learning and DL. The goal of this course is to help master-level graduate students understand necessary concepts and knowledge about deep learning and develop critical skills and abilities of applying deep learning for real-world problems.

Prerequisite for this course is MIS545.

Units: 3

This course explores the ethical, social, and policy implications of artificial intelligence in contemporary society. It delves into the impact of AI on privacy, employment, bias, democracy, and human relationships; into evolving governance and regulatory frameworks for AI; and into managerial decisions regarding investment in and adoption of AI technologies in the organization. Through discussions, case studies, and critical analysis, students will examine the benefits and challenges AI presents and consider how to ensure that AI development is aligned with human values.

Network Science is at the heart of some of the most revolutionary technologies of the 21st century, empowering everything from Google to Facebook, CISCO, Netflix, and Amazon. This course will introduce students to quantitative methods in network science used to model, analyze, and understand various complex systems and the unique interactions among their components. Students will learn about emerging graph based network science methods and ultimately apply their knowledge to conduct their own analysis of a large real world complex system  with corresponding dataset(s), ultimately integrating networks with prediction models. The course is expected to use Python, GEPHI, and SNAP to construct and analyze large networks.
 

The course content will cover important artificial intelligence (AI) concepts and methods and their applications in various digital platforms. It will also include hands-on software and tools for applying AI techniques to addressing problems in digital platforms. Specifically, the topics include understanding data-driven challenges in digital platforms, AI-based personalization, AI-based content analysis, AI-based content generation, and AI-driven decision making in digital platforms. The goal of this course is to help master-level graduate students understand necessary concepts and techniques about AI applications for digital platforms and develop critical skills and abilities of applying AI techniques for real-world business problems. 

The course uses state-of-the-art AI tools (e.g., PyTorch, ChatGPT) to provide hands-on experience. You will learn how to apply AI techniques to sift through large amounts of data in digital platforms and provide actionable insights in various business applications. 

This course provides a broad overview of artificial intelligence (AI) as it is developed for and in medicine and healthcare.  There will be seven modules that each target a different aspect of AI in medicine. In each, we focus on the underlying AI technologies integrated into the medical field and their impact on the different stakeholders. The knowledge transfer portion of each module will comprise three lectures:  we will look in detail at technical underpinnings (what’s under the hood!), examples of applications, and associated problems and solutions.

The rapid evolution of artificial intelligence (AI) has brought transformations and advancements for business intelligence (BI). Business Intelligence (BI) refers to the systematic process of gathering, analyzing, and transforming raw data into valuable insights that inform strategic decision-making within an organization. The recent advances in AI have enabled businesses to optimize these processes and gain competitive advantages. Today, with the continuous growth of AI techniques, the value of BI is expected to have a market of 18.5 billion by 2026. 

This course introduces fundamental AI methods, business processes, common business problems related to multiple types of stakeholders (customers, competitors, and suppliers), and how AI can be adopted to facilitate BI to transform raw data into actionable insights, enabling strategic decision-making. Students will learn fundamental machine learning and deep learning techniques, and how to apply them to real-world business scenarios. By the end of this course, students will be equipped with the knowledge and skills to develop AI-driven BI solutions.

This graduate level course is designed to provide students with a hands-on introduction to the fundamental concepts and tools of artificial intelligence and its application in cybersecurity. Students will become familiar with the fundamentals of AI, Deep Learning, Transformers, Large Language Models, and Reinforcement Learning. Students will learn how to apply these AI techniques to create novel, high-impact cybersecurity research. In addition to AI research knowledge, this course will deliver cutting edge research examples of the application of AI in several cybersecurity domains. These domains include Dark Web Analytics, Smart Vulnerability Assessment, Privacy Analytics, and Adversarial Malware Generation and Defense. Students will gain significant experiences in implementing and evaluating AI by creating a novel AI research project and paper utilizing the aforementioned AI techniques.

The course is to teach fundamental concepts and practical skills and tools on generative AI. The covered topics include the design and construction of generative AI, prompt engineering, fine-tuning of large language models (LLM), retrieval-augmented generation with LLM, security and privacy of generative AI, and ethical, legal and social implications of generative AI.

Units: 3

Level: Graduate 

Organizations use their operations to achieve their strategic objectives. While operations can be diverse, they have characteristics in common. This course focuses on those common attributes. The class will focus on managing processes, inventory, supply chain management and the integration of operations with strategic issues.

Units: 2

Level: Graduate 

Visualizing data is an important step in understanding data, exploring relationships and "making a case." The goal of this class is to introduce students to principles and tools of data visualizations, and create visualizations using appropriate tools for two different but related purposes: (1) exploration; and (2) presentation. The first part is about trying to understand the data and test hypotheses that drive the data visualization effort and formulate a story; the second part is to convey that finding to others in a convincing manner.

Units: 3

Level: Graduate 

Project Management is the application of knowledge, analytical skills, scheduling software tools and techniques related to various project activities in order to meet project requirements. This course specifically addresses the nine project management "knowledge areas," the five project management "process groups" and the four-way constraints of project management (i.e., scope, time, cost, quality). Graduate-level requirements include an additional term paper or team-based PM Project with a real organization. Graduate-level requirements include an additional term paper or team-based PM Project with a real organization.

Units: 3

Level: Graduate 

This course is designed to help master-level graduate students develop necessary skills of collecting, storing and managing, exploring, processing and computing big data for business purposes. Topics covered in this course will include big data collection for business, data management with SQL and NoSQL based technologies, data exploration and preprocessing for analytics, data dashboards for business, distributed data storage and computing and big data based machine learning systems. This course will use state-of-the-art data management, data exploration and computing and big data machine learning software tools (such as SQL Server, MongoDB, PySpark and TensorFlow) to provide hands-on experience. Students will learn how to apply big data techniques to sift through large amounts of data and provide actionable business insights. 

Level: Graduate 

Prerequisite(s) 

MIS 531 or an equivalent database course. 
 

The objective of this course is to give students a broad overview of managerial, strategic and technical issues associated with Business Intelligence and Data Warehouse design, implementation and utilization. Topics covered will include the principles of dimensional data modeling, techniques for extraction of data from source systems, data transformation methods, data staging and quality, data warehouse architecture and infrastructure and the various methods for information delivery. Critical issues in planning, physical design process, deployment and ongoing maintenance will also be examined. Students will learn how data warehouses are used to help managers successfully gather, analyze, understand and act on information stored in data warehouses. The components and design issues related to data warehouses and business intelligence techniques for extracting meaningful information from data warehouses will be emphasized. The course will use state-of-the-art data warehouse and OLAP software tools to provide hands-on experience in designing and using Data Warehouses and Data Marts. Students will also learn how to gather strategic decision making requirements from businesses, develop key performance indicators (KPIs) and corporate performance management metrics using the Balanced Scorecard, and design and implement business dashboards. This course is also offered via the MISonline program.

Units: 3

Level: Graduate 

Specialized work, consisting of individual training and practice in actual service in a technical, business or governmental establishment.

Units: 1

Level: Graduate 

The purpose of this course is to deliver an Information Technology (IT) project to an actively engaged client. The course leverages project management, information evaluation, and other project implementation techniques to assist the students in managing, executing, presenting and documenting a quality IT project.

Units: 3

Level: Graduate 

Prerequisite(s) 

MIS531, MIS541 
 

Students will integrate their knowledge from their program of study and apply it to a problem area in MIS. Each student will write a significant report based on the results of his or her work. This course is also offered via the MISonline program.

Units: 3

This course focuses on structuring, analyzing, and solving managerial decision problems using spreadsheets. The course introduces optimization methods (such as linear, integer, nonlinear programming, and network models), computer simulations, and decision-making under uncertainty. These concepts are studied in the context of applications in strategic planning, operations and supply chain management, information systems, and other areas of business. Graduate-level requirements include an additional term paper or program.

Units: 3

Operations is a broad scope word used to describe many things within a company.  This course is designed to provide students with an in-depth look specifically at selected production operations topics. Topics Include operations strategy, product design for manufacturing, process design, forecasting, aggregate sales and operations planning, capacity management, Materials Resource Planning, production scheduling, and lean/quality principles used in production.  Coursework will explore both the quantitative and qualitative techniques that are available to contemporary business managers.

Units: 3

This course is designed to provide students with an in-depth look specifically at selected service operations topics; activities entailed in selecting, designing, operating, controlling, and updating service systems.

Units: 3

Organization, management and control of material flow processes; logistical strategies and relationships of procurement, handling, warehousing, transportation, and inventory control.  Graduate-level requirements include an additional term paper or program.

Units: 3