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Pre-program requirements

The participants will be provided with essential readings and other preparation material one week prior to the program. They are required to go through those materials to be prepared for the program activities.

Data Science (Day 1, first half day)

1. Data science concepts, procedures, and tools (1.5 hours)

• Introduction to data science project development life cycle: data specification, data collection, data preprocessing, exploratory data analysis, data analysis and learning, data visualization and interpretation, verification and validation, and decision making.
• Overview of key tools for data science project development: Python and other programming languages, SQL and no-SQL databases, Development environments including Jupyter, Tensorflow, and PyTorch, and cloud-based platforms such as Google CoLab, and data repositories including Kaggle and Github. Students will use the provided tools and data to go through the life cycle of building a data science project.

2. Data-centric solutions: Case Study (2.0 hours)

• Real-world data-centric decision-making case study such as: predictive analytics models forecast breakdowns, automated quality control systems analyze production line data in real-time to identify and correct defects, retail and logistics companies use data science to optimize delivery routes, streaming services build recommend personalized content, and FinTech startups use machine learning to assess creditworthiness.
• Interactive discussion on success factors and challenges in implementing data-centric solutions.

3. Industry trends and outlook. (0.5 hour)

• The evolution of data science from traditional analytics to AI-driven automation.
• Emerging trends: AutoML, federated learning, data-centric AI, and synthetic data.
• The growing role of data ethics, security, and governance in the data-centric economy.

AI (Day 1, second half day)

1. AI concepts, procedures, and tools (symbolic AI, machine learning, and neural networks) (1 hour)

• Foundations of AI: symbolic AI, machine learning (supervised, unsupervised, reinforcement learning), and deep learning.
• Key AI frameworks and tools: TensorFlow, PyTorch, Scikit-learn, and cloud AI platforms.
• Applications of AI in natural language processing (NLP), computer vision, engineering, and finance.

2. Data-centric AI applications (1.5 hours)

• How data-centric AI is transforming industries (e.g., finance, healthcare, marketing, engineering, and cybersecurity).
• Application: e.g. fraud detection in finance, anomaly detection in cybersecurity, diagnostics in healthcare, recommendation in e-commerce. Students will exam the AI models, make changes, and validate the provided AI applications.

3. AI-centric solutions: Case Study (1 hour)

• In-depth case study on an AI-powered transformation initiative: e.g. automated software engineering, digital twins, and personalized medicine.
• Examining AI implementation strategies, challenges, and return on investment (ROI).
• Best practices for integrating AI into business operations.

4. Industry trends and outlook. (0.5 hour)

• Advances in Explainable AI (XAI), multimodal AI, and hybrid AI systems.
• Regulatory and compliance considerations for responsible AI adoption.
• The future role of AI ethics, human-AI collaboration, and autonomous systems.

Generative AI (Day 2, first half day)

1. Generative AI concepts, procedures, and tools (Deep learning, Transformer, and Large Language Models) (1.5 hours)

• Understanding large language models: from BERT to GPT4o
• Hands-on demonstration of LLMs: GPT4, Llama3, and Gemini2.

2. Generative AI transforming business strategies: Case Study. (2 hours)

• How generative AI is disrupting content creation, software development, automation, entertainment, and customer engagement.
• Applications in: Automated report generation & chatbots, auto piloting, robotics, AI-generated design and media.
• Hands-on Experience: use generative AI tools to build an application.

3. Future outlook (0.5 hour)

• Trends in multi-agent generative AI and self-improving AI models.
• The impact of generative AI on knowledge work, automation, and creativity.
• Ethical concerns: AI hallucinations, misinformation, privacy, and deepfake regulation.

Data Science and AI for Strategic Leadership (Day 2, second half day)

1. Identifying opportunities for data-centric intelligence innovation. (1.0 hour)

• How executives can identify and leverage AI and data science for strategic advantage.
• Aligning business objectives with AI-driven innovation.
• Framework for evaluating AI adoption readiness and risk assessment.

2. Leading successful data and AI initiatives in organizations. (1.5 hours)

• Build and managing high-performing data science teams.
• Implement AI transformation strategies at scale.
• Case study: Enterprise AI deployment and governance frameworks.
• Hands-one experience: propose an idea for your organizations.

3. Fairness, bias, privacy and transparency in data science and AI systems. (0.5 hour)

• Understanding bias in AI models and mitigation techniques.
• Ensuring AI fairness, accountability, and compliance with privacy laws (GDPR, CCPA).
• The role of explainability and transparency in ethical AI adoption.

4. Discussion and Evaluation. (1.0 hour)