Fundamentals of Artificial Intelligence and Data Science

 Educational goals

• Understand the fundamentals of AI, its history and current applications.
• Exploring ethical aspects (bias, privacy, accountability).
• Recognize the impact of AI in industries and everyday life.
• Understand how data is collected, stored, and used in AI applications.
• Evaluate data quality and understand bias and its impact on model performance.
• Explore data visualization tools and interpretation techniques.

Learning Outcomes

• Describe the basic concepts and history of artificial intelligence.
• Identify the ethical challenges associated with AI technologies.
• Familiarize yourself with real-life applications of AI.
• Explain the role of data in AI applications.
• Evaluate data quality and understand data biases.
• Interpret graphical visualizations to support decision-making.

 General axes

1) Foundations and concepts

• The evolution of AI from concept to reality.
• Identify the uses of AI in everyday life.
• Case studies: How AI has changed customer service.

2) Sectoral applications

• Impact on health, finance, retail and more.
• Successes and limitations of real-world applications.
• Group activity: A mind map of AI applications across sectors.

3) Ethics and governance

• Bias, privacy, and accountability.
• Building an ethical framework and applying it to cases like facial recognition.

4) Tools and platforms

• Explore IBM Watson and Google AI and use ready-made templates.
• How non-technical people can benefit from AI tools.

5) Data and visualization

• What is data? Storytelling with data.
• Visualization tools (Tableau, IBM Cognos) and identifying data sources for projects.
• Analyze visualizations, blueprints, and present visions visually.

6) Evaluation and projects

• Conceptual testing and review.
• Group demonstrations of AI applications and real-world data.
• Challenging data interpretation and peer feedback.
• Applied project: A concept map for 5 uses + a dashboard for a decision-making scenario.