AU Kogod Graduate IT & Analytics Course Guide¶

Business decision problems can be characterized as situations in which managers must select the best alternative from several competing alternatives. Managers frequently rely on results from statistical analyses to help make the best decision. Students use real-world data sets and PC-based software to describe sets of measurements, construct probability distributions, estimate numerical descriptive measures, and build multiple regression models in a variety of business settings related to accounting, finance, information technology, marketing, and sustainability.

Primer connection: Builds the quantitative foundation for concepts in Analytics Fundamentals and Data Visualization.

Students are introduced to descriptive, predictive, and prescriptive analytics and to models, tools, and methods commonly used in each area to develop multidisciplinary business insights from data. Through hands-on exercises and a term project, students develop skills to present solutions to problems and provide answers to business questions in various business disciplines such as accounting, finance, information technology, marketing, and sustainability.

Primer connection: Deepens the analytics spectrum (descriptive through prescriptive) covered in Analytics Fundamentals.

Students are introduced to predictive modeling methods, approaches, and tools. Students gain skills in predictive analytics to develop and use advanced predictive analytics methods; develop expertise in the use of popular tools and software for predictive analytics; learn how to develop predictive analytic questions; identify and select the most appropriate predictive analytics methods and tools, apply these methods to answer the respective questions, and present data-driven solutions.

Primer connection: Hands-on application of the machine learning concepts introduced in AI & Emerging Tech and Analytics Fundamentals.

Students are exposed to key social network theories, methods, and tools. Students develop and use advanced social network analytics methods; develop expertise in the use of popular network analysis tools and software; learn how to develop analytical questions that can be answered with social network analysis concepts and methods.

Primer connection: Applies network analysis to the organizational dynamics discussed in IT-Business Alignment and the network effects covered in Platform Economics.

Business Intelligence (BI) connects data from multiple sources to produce meaningful information and identify patterns and trends to inform decisions. BI encompasses the methodologies, metrics, processes, and information systems used to monitor and manage an enterprise's business performance and support strategic decision-making. This course exposes students to the management practices, methodologies, and technologies that major corporations are applying, including data warehousing, data mining, business analytics, predictive statistics, online analytical processing, and visual data representations.

Primer connection: Builds on the BI and data warehousing concepts in Data Governance and the dashboard design principles in Data Visualization.

This course introduces important database concepts, including data modeling, database design, data extraction and data analysis skills needed to transform raw data into useful business information and knowledge for decision making and problem solving. Topics include relational design, data warehousing, artificial intelligence (AI) database tools, AI vector databases, NoSQL databases, data search, data querying, basic analytics and reporting.

Primer connection: Deepens the database and data architecture concepts in Enterprise Architecture and the AI infrastructure topics in AI & Emerging Tech.

This course teaches students to design, plan, execute, and report on a relatively large-scale AI-driven text analytics project. It includes multiple hands-on labs in both R and Python, and a semester-long research project. Students explore the fundamentals of text analytics and data visualization beginning with statistical bag-of-words (BoW) approaches, moving to natural language processing (NLP) approaches including language models, word embeddings, and Named Entity Recognition (NER); then on to machine learning approaches, including unsupervised techniques such as topic modeling and clustering, and supervised techniques such as predictive modeling; ending with Deep Learning, transformers, and large language models (LLMs) like GPT, BERT, and LLaMA that underlie Generative AI tools.

Primer connection: Advanced application of the GenAI and LLM concepts covered in AI & Emerging Tech.

This course focuses on three key areas: the risks associated with information management in the digital economy; the most effective personal and business practices to manage these risks; and the associated information forensics to understand where and how information can be traced. Individual, corporate, and national/global aspects of information security risks are covered, as well as issues related to risk understanding, assessment, and management, corporate governance, and incident response.

Primer connection: Deepens the risk frameworks, zero trust architecture, and CISO governance topics covered in Cybersecurity.

Cybersecurity governance refers to the organizational structures, procedures, and policies that address the range of risks and opportunities including legal, technical, financial, and operational, relevant to cybersecurity. Case studies, including an in-depth analysis of the cybersecurity governance of a company, and examples from multiple business sectors are used to understand key aspects of cybersecurity governance in both public and private organizations.

Primer connection: Extends the governance frameworks in IT Governance Frameworks and the CISO organizational models in C-Suite IT Roles to the cybersecurity domain specifically.

This course covers a variety of analytics techniques for cybersecurity applications, from data collection and management to machine learning to data visualization, and discusses their roles in detecting cyber threats and strengthening the defense of critical cyber assets in today's ever-changing cybersecurity landscape.

Primer connection: Combines the analytics methods from Analytics Fundamentals with the SIEM, SOC, and threat detection topics in Cybersecurity.

The course covers the conceptual, practical, and managerial foundations of managing organizations in today's digital and data-driven economy. It includes foundational aspects of information technology: digital transformation, digital competition, analytics management, cybersecurity, and emerging technologies including artificial intelligence.

Primer connection: A comprehensive companion to this entire primer — covers Digital Transformation, AI & Emerging Tech, and IT-Business Alignment in depth with a management lens.

Students learn how to conduct business analysis to document business processes and describe the functional requirements for the corresponding business application and then analyze the information requirements to support the application. The course has a strong hands-on component which prepares students for IT consulting and business analysis practices. Students work in teams on a consulting project with an organization or consulting firm.

Primer connection: Hands-on application of the process modeling, BPMN, and process mining concepts covered in Business Process Management.

Due to the increasing complexity of business and technology environments, it is challenging for managers to complete mission-critical projects on time and within budget while satisfying project stakeholders. This course educates students to become competent project managers. Students learn about the industry-standard Project Management Body of Knowledge (PMBOK) including important project management concepts, frameworks, principles, methodologies, techniques, and tools.

Primer connection: Deepens the project management methodologies (Agile, Waterfall, hybrid) covered in Project Management.

This course introduces production and operations management (POM), the process of managing people and resources in order to produce goods or provide services. Decisions related to forecasting, aggregate planning, facility location, project scheduling, inventory control, supply chain management, and sustainability are discussed. Considerable emphasis is placed on the development of models and analytical tools.

Primer connection: Extends the supply chain and operational efficiency topics in Enterprise Applications and Digital Transformation.

This course provides foundations in software programming for business analytics, focusing on Python. It introduces software development and object-oriented programming, provides an overview of Python syntax and popular Python packages for business analytics modeling, and reviews useful tools to enhance productivity in Python, including iPython and Jupyter Notebooks.

Primer connection: Provides the programming skills referenced in Technical Literacy for Managers — moves from conceptual understanding to hands-on coding.

Topics include relational design, data warehousing, artificial intelligence (AI) database tools, AI vector databases, NoSQL databases, data search, data querying, basic analytics and reporting.

Primer connection: Builds the database skills behind the data architecture concepts in Enterprise Architecture and the AI vector database topics in AI & Emerging Tech.

This hands-on workshop helps students acquire the skills and knowledge needed to successfully complete the Microsoft Office Specialist (MOS) exam. Topics vary by section. All students take the certification exam on the second day of the workshop. (1 credit, Pass/Fail, repeatable with different topic.)

Primer connection: Practical certification aligned with the enterprise software ecosystem discussed in Enterprise Applications.

This course provides students with a clear understanding, through experiential learning, of blockchain, combined with other emerging technologies (e.g., artificial intelligence, machine learning, etc.), applications, and implementations in the global digital ecosystem. It explores the implications of this technology for different types of organizations such as governments, multinationals, international development organizations, and other institutions. Topics include blockchain fundamentals, the value proposition to different stakeholders, barriers to implementation, and the impact of blockchain on existing business processes.

Primer connection: Extends the emerging technology landscape in AI & Emerging Tech and Innovation Management.