Earn a business AI graduate certificate online

Explore an end-to-end framework for managing data as assets to enhance managerial decision-making. Learn how to create databases, manage data throughout its lifecycle, and generate reports and dashboards using data visualization tools. Additionally, you’ll gain hands-on experience with SQL, Tableau, Talend, Alteryx, and more. Finally, the course covers important aspects for the use of AI in organizations, such as big data and AI governance, emphasizing the need for accurate and complete data in the development of AI solutions.

Developing business capabilities for the digital future involves rethinking the current way of doing business within the context of understanding where the new frontiers of digital value may lie. To uncover the business capabilities, the course focuses on artificial intelligence (AI) and automation (especially robotics process automation and intelligent process automation), and their applications on both digital processes and digital products. In particular, the course covers both managerial aspects of AI initiatives and a variety of automation tools, including UiPath, Power Automate, and AI agents.

This course helps students to effectively analyze data using machine learning approaches, enhance prediction performance and robustness, and extract valuable insights from large datasets to support managerial decision-making processes. The course structures machine learning knowledge into four layers—applications, tasks, models, and algorithms—applied to a variety of business contexts. Specifically, students will learn how to apply machine learning techniques to solve business problems while taking into consideration obstacles for the use of each approach regarding data quality and other organizational issues.

This course covers four modules, providing an overview of the following:

1. Prevailing application environments, modern data generating processes, data exploration approaches, and fundamental data encoding structures
2. Fundamental machine learning concepts and their relationship with deep learning (DL)
3. DL and generative AI (GenAI) design learning strategy, implementation, and sample applications
4. DL and GenAI model evaluation, deployment, and presentation

The course focuses on specific business cases distributed across multiple industries, such as manufacturing, healthcare, and telecommunications.