Core Subjects

• Introduction to Statistics and Probability for decision modeling.
• Understanding Machine Learning (ML) and its different types.
• Exploring Data Visualization techniques and storytelling through data.
• Learning about Supervised Learning and Unsupervised Learning.
• Understanding Learning Theory and including bias-variance trade-offs.
• Exploring practical applications of Artificial Intelligence (AI) and Machine Learning.
• Learning how to build Machine Learning applications using statistics and data visualization.
• Using statistical analysis and visualization to identify meaningful insights and support better decision-making.

• Introduction to advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques.
• Understanding AI and ML applications for image and text processing.
• Learning about Autoencoders for data representation and feature extraction.
• Exploring Convolutional Neural Networks (CNNs) for image-based applications.
• Understanding Recurrent Neural Networks (RNNs) for sequential and text data.
• Learning about Generative Adversarial Networks (GANs) for generating and processing data.
• Exploring Seq2Seq models for sequence-based AI applications.
• Understanding Transformers and their role in modern AI and language processing.
• Exploring state-of-the-art AI and ML techniques for real-world applications.

Core Subjects

• Understand the shift from in-house model training to using pre-trained models based on public data.
• Learn how to differentiate between custom-trained and pre-trained AI models.
• Develop an understanding of how to select the right AI approach based on specific business requirements.
• Gain hands-on exposure to AI tools and model-building applications.
• Explore the concepts and applications of Large Language Models (LLMs) such as GPT and other advanced models.
• Understand AI image generation and its practical applications.
• Explore frameworks and tools such as LangChain, AgentGPT, and enterprise-focused GPT solutions.
• Get introduced to latest AI models and emerging techniques in the field.
• Apply course concepts through practical experimentation and existing AI tools.
• Develop a research proposal as part of the final course outcome.

Core Subjects

• Understand the shift from in-house AI model training to using pre-trained models built on public data.
• Learn how to differentiate between custom-trained and pre-trained models.
• Understand how to choose the right AI approach based on business requirements and objectives.
• Gain hands-on exposure to AI model development and existing AI tools.
• Explore the fundamentals and applications of Large Language Models (LLMs) such as GPT and other models.
• Understand the concepts and applications of AI-powered image generators.
• Develop practical insights into applying AI and ML techniques to real-world business problems.

• Understand the ethical and social implications of Artificial Intelligence (AI).
• Explore how AI can create both business opportunities and potential risks.
• Learn the importance of AI explainability, fairness, transparency, and accountability.
• Understand how to design and deploy responsible and trustworthy AI systems.
• Examine the impact of AI on society, businesses, and decision-making.
• Explore the future of work, including changes in the workforce and workplace driven by AI.
• Understand how organizations can adopt and use AI responsibly.
• Analyze key challenges related to responsible AI governance and implementation.
• Conduct research on ethical, social, or business aspects of AI as part of the course.

• Develop advanced research skills and ethical techniques for doctoral-level writing and action research.
• Learn quantitative research methods, including survey design and experimental design.
• Apply statistical analysis to survey and experimental data.
• Explore regression analysis and time series analysis for research and decision-making.
• Learn qualitative research techniques, including open-ended interviews, focus groups, and case studies.
• Understand how to integrate quantitative and qualitative research methods into academic research.
• Strengthen academic writing and research skills aligned with doctoral-level standards.
• Develop the ability to conduct and present rigorous, ethical, and well-structured research.

• Develop advanced practitioner-scholar skills required for doctoral-level research and academic work.
• Learn how to research, analyze, and present complex business and management topics.
• Understand the structure and requirements of a traditional doctoral dissertation.
• Explore the development of a Practice-Based Book as an alternative doctoral research format.
• Learn how to develop a DBA business plan as a doctoral-level applied research project.
• Understand the key differences between a DBA business plan and an MBA business plan.
• Develop skills to write, structure, and defend different types of doctoral-level research projects.
• Apply advanced research and analytical methods to produce a rigorous, practice-oriented doctoral outcome.

• Explore emerging technologies shaping modern business transformation.
• Understand the applications of Internet of Things (IoT) and High-Performance Computing (HPC).
• Learn about Quantum Computing and Neuromorphic Computing and their business potential.
• Explore Cybersecurity and Blockchain technologies for secure and trusted business operations.
• Understand the applications of AR/VR, Robotic Process Automation (RPA), and Digital Twins.
• Analyze the strategic importance and business applications of emerging technologies.
• Develop actionable strategies for integrating emerging technologies into business operations.
• Apply technology concepts to real-world business transformation challenges.
• Complete a final project demonstrating the practical application and business impact of these technologies for relevant stakeholders.