Data analytics in higher education

Show Notes

• 01:55
  The role of the Center for AI and Data Analytics

  The Center for AI and Data Analytics aims to educate students and professionals about AI, preparing them for a rapidly changing world and its ethical implications. They also focus on staying at the forefront of research to keep their educational programs relevant.
• 04:17
  Addressing the educational needs of midcareer professionals in AI

  Continuous learning is a must, and mid-career professionals tend to hold significant influence in organizations. Consequently, they must gain the knowledge and tools to navigate AI effectively and ethically as more organizations adopt AI into their toolset
• 07:22
  Integrating critical thinking and problem solving into AI education

  The Centre focuses on real-world projects and simulations to teach students data prioritization and how to guide AI. To elevate student thinking, the center focuses on the leadership elements of AI and analytics and how to execute them effectively in the organization.
• 11:06
  Leveraging AI for personalized learning pathways

  AI in education allows for personalized learning experiences. AI can be used to identify student strengths and weaknesses, curate exercises, and provide learning paths aligned with each student's interests and career aspirations.
• 14:02
  Success factors in AI implementation

  Success in AI implementation relies on factors beyond technical skills, such as problem-solving, team management, data management, and execution. Mastering these skills alongside technical proficiency is critical to ensure long-term success and ethical AI usage.
• 17:21
  AI and the evolution of analytics

  AI has led to data science tools becoming more accessible and user-friendly. While coding skills remain crucial for some, the emphasis has shifted towards teaching students how to add value through analytics and how to leverage analytics tools effectively and ethically.