This course provides comprehensive coverage of essential data mining techniques including classification, clustering, association rule mining, visualization, and prediction. Through a practical, hands-on approach, students will gain experience using industry tools such as XL Miner alongside powerful open-source software like WEKA. By the end of this course, students will develop the technical skills necessary to extract meaningful insights from complex datasets and apply these techniques to real-world business challenges.

This course immerses students in cutting-edge big data ecosystems using state-of-the-art tools and technologies. Working with professional platforms like SQL Server, MongoDB, PySpark, and TensorFlow, students gain practical experience in data management, exploration, and advanced machine learning techniques. The hands-on curriculum teaches students how to effectively process and analyze massive datasets, uncovering valuable patterns and transforming complex information into clear, actionable business insights that drive strategic decision-making.

This course delivers a comprehensive introduction to deep learning (DL) fundamentals, exploring essential concepts, techniques, applications, and Python-based tools that power today's AI revolution. Students will master a diverse range of deep learning methods designed to solve complex modeling and learning challenges across multiple domains. The curriculum spans practical applications including image classification, natural language processing, online user behavior analysis, and recommender systems—equipping students with the technical foundation to implement these powerful technologies in business contexts.

This course provides hands-on experience with cutting-edge generative AI and deep learning tools that are transforming business operations. Students will develop practical skills working with advanced technologies like large language models, diffusion models, and neural networks. Through immersive projects, students learn to harness generative AI techniques to process vast datasets, identify patterns, and produce actionable business insights that drive innovation and competitive advantage in today's AI-powered business landscape.

This course examines the critical ethical, social, and policy dimensions of artificial intelligence as it reshapes business and society. Students will explore AI's profound impacts on privacy, employment patterns, algorithmic bias, democratic processes, and human relationships. The curriculum navigates the rapidly evolving governance and regulatory landscape surrounding AI technologies, preparing future leaders to make responsible managerial decisions about AI investment, implementation, and oversight. Through case studies and discussions, students develop frameworks for balancing innovation with ethical considerations when deploying AI technologies in organizational contexts.

This course introduces students to powerful quantitative methods from network science essential for modeling and analyzing complex interconnected systems. Students will learn advanced techniques for visualizing, measuring, and interpreting relationships within networks across various business domains. The curriculum builds competency in graph theory applications, centrality analysis, community detection, and dynamic network modeling. Through practical exercises, students develop the analytical skills to uncover hidden patterns within complex systems and gain valuable insights into the unique interactions among components that influence organizational structures, market behaviors, and information flows.

This graduate-level course explores essential artificial intelligence concepts, methods, and applications across various digital platforms. Students will gain hands-on experience with software tools for implementing AI solutions to real-world challenges. Key topics include understanding data-driven challenges in digital environments, AI-based personalization systems, content analysis and generation using artificial intelligence, and AI-driven decision making frameworks. The course combines theoretical foundations with practical implementation, preparing students to leverage AI technologies effectively in digital platform contexts.

This graduate-level course provides a comprehensive overview of artificial intelligence as it is developed for and implemented in medicine and healthcare settings. Through seven focused modules, students explore different aspects of AI applications in medicine, examining both the underlying technologies and their integration into clinical practice. This course emphasizes how these AI systems impact various healthcare stakeholders, from practitioners to patients. Students will gain insight into the technical foundations, practical applications, and ethical considerations of AI technologies that are transforming modern healthcare delivery and medical research.

This graduate-level course provides students with a hands-on introduction to artificial intelligence fundamentals and their practical applications in cybersecurity. Students will develop proficiency in AI core concepts, deep learning architectures, transformer models, large language models, and reinforcement learning techniques. Through practical exercises and projects, participants will learn to implement these advanced AI methodologies to develop innovative cybersecurity research solutions that address emerging threats and vulnerabilities. The course bridges theoretical understanding with practical implementation, equipping students with the technical skills needed to leverage AI for solving complex cybersecurity challenges.

This graduate-level course introduces students to fundamental artificial intelligence methods and their integration with business intelligence processes. Students will explore how AI can transform raw data into actionable insights to address common business challenges related to various stakeholders, including customers, competitors, and suppliers. The course covers essential machine learning and deep learning techniques, emphasizing their practical application in real-world business scenarios. Through hands-on projects and case studies, students will develop the skills to implement AI-driven solutions that enable strategic decision-making and business transformation. Participants will gain a comprehensive understanding of how AI adoption can enhance organizational performance across different business domains.

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.

This graduate-level course provides a comprehensive overview of artificial intelligence as it is developed for and implemented in medicine and healthcare settings. Through seven focused modules, students explore different aspects of AI applications in medicine, examining both the underlying technologies and their integration into clinical practice. This course emphasizes how these AI systems impact various healthcare stakeholders, from practitioners to patients. Students will gain insight into the technical foundations, practical applications, and ethical considerations of AI technologies that are transforming modern healthcare delivery and medical research.

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.

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.

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.