KOUT 2110 - Data, Algorithms and AI: a Technical Foundation for Decision-Makers

This course introduces students to the foundations of databases, machine learning, and language models from a non-technical perspective. Students will learn how organizations collect, store, and use data; how databases can be vulnerable to cyberattacks; and how institutions respond to data breaches and security risks. The course also explores supervised and unsupervised machine learning, with emphasis on how algorithms influence business strategy, public policy, governance, and decision-making. Finally, students will examine language models and large language models (LLMs), including their practical applications, ethical challenges, and societal impact. By the end of the course, students will be able to critically evaluate data-driven systems and communicate informed perspectives about Artificial Intelligence (AI) and digital governance in professional and public settings.

Learning Outcomes

• Understand core concepts in databases and artificial intelligence

Students will be able to explain the basic functions of databases, identify common cybersecurity risks and responses, and distinguish between supervised learning, unsupervised learning, and large language models in non-technical terms.

• Critically evaluate the societal and organizational impact of data-driven technologies

Students will develop the ability to analyze how databases, machine learning systems, and AI tools influence business strategy, political institutions, public policy, and ethical decision-making.

• Communicate informed recommendations about technology and governance

Students will strengthen their skills in presenting and writing clear, evidence-based analyses of technology-related issues for non-specialist audiences, including managers, policymakers, and the public.

Professional Skills

• Critical thinking and analytical reasoning: evaluating the opportunities, risks, and limitations of data-driven technologies and AI systems.
• Data literacy: understanding how organizations collect, store, manage, and interpret data for decision-making.
• Cybersecurity awareness: recognizing common database vulnerabilities, security threats, and institutional responses to cyberattacks.
• Policy and ethical analysis: assessing the social, political, legal, and ethical implications of artificial intelligence and machine learning.
• Strategic decision-making: applying technological knowledge to organizational and public-sector problems.
• Research skills: locating, interpreting, and synthesizing information from academic, policy, and media sources.
• Professional communication: explaining complex technological concepts clearly to non-technical audiences through written reports and oral presentations.
• Presentation and public speaking skills: delivering structured, persuasive presentations supported by evidence and critical analysis.
• Digital and AI literacy: understanding how machine learning systems and large language models are used in business, government, and public communication.
• Interdisciplinary problem-solving: connecting technological issues with questions of governance, economics, management, and society.

Pre-semester Bootcamp.

Feedback is to be provided at the end of the course in written format after students pass the final exam and receive their grades.

• Oral Presentation (30% of final grade): Students will deliver a short presentation analyzing a real-world case related to the course themes. Topics will be communicated during class.
• Executive Memo (40% of final grade): Students will write a 1,500-2,000-word policy brief addressed to a hypothetical decision-maker (e.g. CEO, minister, city government, NGO director).
• Technology Case Analysisw (30% of final grade): Students will write a 1,200-1,500-word analysis of a real-world case involving a database breach, machine learning system, or large language model deployment. Topics will be communicated during class

• Attendance: 2 hours a week / 24 hours a semester
• Online learning activities: 24 hours a semester
• Reading and Preparation for Class: 24 hours a semester
• Research and Writing for Individual Assessments: 24 hours a semester