Focuses on using information technology to support business processes. The purpose and composition of information systems, the utilization of technology and hands-on experience in developing microcomputer business applications with productivity tools (Microsoft Excel and Access).

Focuses on using information technology to support business processes. The purpose and composition of information systems, the utilization of technology, and hands-on experience in developing microcomputer business applications with productivity tools (Microsoft Excel and Access).

This course will present topics related to managing information systems projects and digital business from a global perspective. Project management issues such as analyzing stakeholders, defining expectations, defining project deliverables, analyzing scope, collecting requirements, developing schedules, and mitigating risk, will be covered. Also, variety of digital business issues, such as digital business models, disruptive forces, and digital strategies will be covered from a global perspective.

This course covers basic principles in data modelling, and turning big data into intelligent actionable insights. Through the use of real business case studies and lab sessions students will develop a comprehensive, innovative and practical approach to data analytics that enables them to solve diverse and complex business problems.

This course covers basic principles in data modeling, and turning big data into intelligent actionable insights. Through the use of real business case studies and lab sessions students will develop a comprehensive, innovative and practical approach to data analytics that enables them to solve diverse and complex business problems. Requires C- or better in ISSCM 241H or ISSCM 241 or STAT 103, INFS 247 or INFS 247H, and one of the following: MATH 110, MATH 118, MATH 130, MATH 131, or MATH 161.

Covers current concepts in database theory and use. The course teaches design, implementation, and utilization of relational database management systems by covering the processes, tools, and methodologies such as business requirement collection, ER modeling, relational modeling, normalization, SQL, and MS Access.

This course studies methods for analyzing, developing and implementing business information systems. Stages of the systems development life cycle are explored in depth. Tools and techniques for structured and object-oriented analysis and design are discussed.

This course focuses on extracting insights from complex datasets and progresses from data mining and R principles to data transformation and data mining techniques. Using deep learning and large language models, it addresses challenges in AI analytics, emphasizing ensemble learning techniques, model evaluation, and the handling of big data.

The amount of data that our world generates is growing at a torrid pace. Sifting through & making sense of these humongous mountains of data is crucial to ensuring business growth, success and to making scientific discoveries & advancements. Data visualization plays an important role in this process.

This experiential and research-focused course explores the newest developments in the field of user experience (UX) & biometrics (e.g., collecting and analyzing human behavior data through eye tracking, galvanic skin response, facial expression, voice, and brain activity) and introduces various methods used in cutting-edge research laboratories to study human insight in business contexts. Theoretical UX concepts and practical skills in biometric data collection and analysis will be explored using the latest academic research and hands-on work with biometric hardware, software, and data. Students will complete and present the original human subject research project (team-based and with IRB approval) using biometric trackers and biosensors. The research project will be documented in the format of a full academic article.

This course focuses on how to effectively use the Python computer programming language to support decision making in business. We will particularly focus on using Python for manipulating and analyzing data. In addition to covering the concepts of programming, this course covers working with external data, debugging code and developing user interfaces.

This course focuses on how to effectively use Microsoft Office's built-in programming language, Visual Basic for Applications (VBA), to build models, primarily in Excel. We will cover issues that facilitate the construction of robust and readily understandable models in the VBA language. Starting with basic modeling functions, the course will progress through complex modeling skills. This course assumes that you are familiar with Basic Excel operations.

Introduces the non-math-oriented student to the use of mathematical modeling in business. This course begins with a review of topics from algebra, then covers concepts in calculus and applies them to the solving of business problems.

Introduces the non-math-oriented student to the use of mathematical modeling in business. This course begins with a review of topics from algebra, then covers concepts in calculus and applies them to the solving of business problems.

Quantitative Methods is restricted to students enrolled in the Executive MBA or the Health Care Management MBA programs.

Introduces the non-math-oriented student to the use of mathematical modeling in business. This course begins with a review of topics from algebra, then covers concepts in calculus and applies them to the solving of business problems.

The fundamentals of managerial statistics are presented. Topics include descriptive statistics, regression, and correlation analysis.

Business analytics is the practice of using systematically collected data to support decision-making in business contexts. The goal of this course is to introduce students to the contemporary approaches, tools, and techniques used in business analytics. Because many concepts in this field are best learned through hands-on experience, the course emphasizes obtaining, processing, analyzing, and visualizing data drawn from a variety of business cases. Students will use a modern analytics language widely adopted by data scientists to perform these tasks. Throughout the course, students will develop practical analytic skills and strengthen their ability to generate meaningful business insights through data-driven analysis.

The art and science of project management as applied to a variety of business and technical projects in commercial, public, and private sectors. Covers: project life cycle and methodology; teambuilding; project organization, stakeholders, and leadership; proposals and contracts; techniques for project planning, estimating, scheduling, and control; PMO.

Explores the art and science of project management and systems development, as applied to a variety of large and small project situations in commercial, public, and private sectors.

Project management is the art, craft, and occasional science of being intentional and systematic about transforming ideas into practice and realizing organizational value. We explore how to design and sequence tasks and deliverables, obtain resources, and manage to deadlines and value.

This course provides a comprehensive overview of Artificial Intelligence (AI) designed for Executive MBA (EMBA) students who are mid-level to senior-level professionals. Students will explore foundational AI concepts, strategic and organizational use cases, and ethical implications through the lens of executive leadership. Emphasis is placed on real-world application, critical analysis, and experiential learning, with a focus on how AI supports enterprise-level decision-making, cross-functional leadership, and long-term competitive advantage.

This course examines the integration of statistical methods with artificial intelligence (AI) techniques to analyze and interpret complex data. Students will study key statistical concepts, including descriptive statistics, random variables, probability distributions, estimation, hypothesis testing, regression, correlation analysis, and model building. AI-driven approaches will be introduced for various topics. Emphasis is placed on using statistical software and AI to facilitate data analysis and address real-world problems effectively.

The fundamentals of managerial statistics are presented. Topics may include descriptive statistics, random variables, probability distributions, estimation, hypothesis testing, regression, and correlation analysis. Statistical software is used to assist in the analysis of these problems.

This course uses database systems as the focus for studying concepts of data modeling and data manipulation. Procedures for creating, managing, sorting, and processing data are discussed. Concepts of relational database methods are covered as well as the issues that arise in managing information in a database and using it to support business processes.

The concept of big data, distributed computing frameworks, and massively parallel processing databases are also covered. Students will become proficient using open source databases, performing extensive advanced SQL programming, writing scripts and manipulating strings, numbers, data, etc. within a database.

Data Mining involves the search for patterns in large quantities of data using techniques such as clustering, decision trees, neural networks, and association analysis. Machine learning, the process of using mathematical models of data to help a computer learn without direct instruction, an important application of artificial intelligence (AI), is also introduced.

Restricted to Graduate School of Business students. Techniques of forecasting and model building are introduced. Methods covered are simple and multiple regression, introduction to time series components, exponential smoothing algorithms, and AIRMA models - Box Jenkins techniques. Business cases are demonstrated and solved using the computer.

Provides a core set of skills for planning, managing, and executing systems analysis and design processes in e-business and Web-based environments. Topics typically include project initiation and planning, methods used in the determination of information requirements, prototyping, techniques used in systems design, testing, and implementation strategies.

This course introduces the student to economic and business practices of a foreign country using the analysis of data and on-site experiences. We will focus on business strategies, impediments, and challenges in light of the culture, politics, history, and institutions of a selected country. We will interact with a variety of local people, such as small business owners, firm managers, economists, journalists, and students, in order to inform our understanding and analysis.

The amount of data that our world generates is growing at a torrid pace. Sifting through and making sense of these humongous mountains of data is crucial to ensuring business growth, success and to making scientific discoveries and advancements. Data visualization plays an important role in this process.

Making good decisions in the face of uncertainty and risk is at the heart of successful management. This course provides a coherent set of critical thinking and decision analysis tools that are used to carve out well-structured decision models from ill-structured real-life problems and perform analyses to generate insights.

Perhaps one of the biggest challenges facing organizations is bridging the gap between those who have technical expertise in information systems and those who are managerial decision makers. This course builds on the decision strategy course to help address that challenge.

Perhaps one of the biggest challenges facing organizations is bridging the gap between those who have technical expertise in information systems and those who are managerial decision makers. This course builds on the decision strategy course to help address that challenge.

The course covers concepts related to data organizing and database modeling, and the managerial issues related to the design, implementation, and utilization of systems that support operational data use and provide infrastructure for business data analytics. Enrollment limited to EMBA Cohort.

The course covers the effective uses and applications of data analytics; including On-Line Analytic Processing/Business Intelligence, data mining techniques and their particular applications and data visualizations methods and tools. Enrollment limited to EMBA Cohort.

This course builds upon foundational knowledge from the Business Analytics course by advancing students' proficiency in analytic programming using a modern analytics language widely adopted by data scientists to perform these tasks. Students learn to manipulate and transform data into actionable insights, create advanced visualizations, write functions and control statements for repeatable analysis, build predictive and prescriptive models, and analyze unstructured text.

The rapid advancement in technology necessitates an equally rapid advance in the ethics of data analytics. We will explore ethical questions in this field through the use of business case studies. We will also look at examples of ethical codes of conduct.

Students will explore human perception and cognition, the use of best design practices, and interacting/storytelling with data.

This 10-week intensive course aims at mastering the end-to-end process of discovering, designing, developing, delivering, and managing products in context to data, machine learning and artificial intelligence. The course concentrates on strategic thinking and tactical implementation of data driven products and provides skills needed to become a successful product manager.

Scheduled classes are offered on an ad hoc basis. Specific titles, prerequisites and content will vary.