Golden Gate Online DBA Program in Gen-AI – 2026 Complete Guide

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 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; Elective: Develop research skills to conduct and present doctoral-level research effectively

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

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; Elective: Explore various data collection methods and techniques and ethical considerations necessary for effective and rigorous data collection

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; Elective: Explore statistical techniques and data analysis methods essential for conducting robust quantitative research

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