This course introduces the foundations of data management by integrating traditional database principles with contemporary artificial intelligence (AI), and analytics techniques. Students explore database management system (DBMS) architecture, the database environment, and data warehousing concepts within modern organizational contexts. The course emphasizes AI-enhanced data modeling using entity-relationship (ER) and relational models, principles of logical database design, normalization, and the application of intelligent analytics. Students examine business rules and their role in ensuring data integrity, consistency, and governance. Practical components include structured query language (SQL), AI-assisted query generation and optimization to support data-driven decision-making. Additional topics may include distributed databases, web-based database services, cloud-based data environments, graph databases, and introductory data mining concepts.

This course provides a comprehensive introduction to the world of data programming with Python, augmented by the power of Artificial Intelligence (AI). Designed for beginners, this course will equip students with the essential skills to analyze data, automate tasks, and build intelligent applications. Students will learn fundamental Python programming concepts, explore AI algorithms and techniques, and gain practical experience working with real-world data. Through hands-on labs and projects, students will discover how to leverage AI to extract meaningful insights from data, build AI-powered tools, and develop a deeper understanding of the growing role of AI in data science. No prior programming experience is required.

Data analytics is extensively employed in practical settings to gain comprehensive insights into data, extract actionable information, and facilitate strategic decision-making. This course is designed to introduce essential statistical techniques tailored for data analytics. Topics covered include data summarization and presentation, confidence intervals, hypothesis testing, and regression analysis. Additionally, the course places a strong emphasis on the practical application of these techniques and encourages students to actively engage with data analysis exercises to enhance their managerial decision-making capabilities.