AAI 301 - Data-Centric AI

Credits: 3
Lecture Hours: 2
Lab Hours: 2
Practicum Hours: 0
Work Experience: 0
Course Type: Voc/Tech

This course explores the principles and practices of data-centric artificial intelligence (AI). Students will learn how to design, implement, and evaluate AI systems with a strong emphasis on data quality, data management, and data-driven decision-making. The course covers various techniques and tools for data preprocessing, feature engineering, model training, and validation.
Prerequisite: A minimum grade of C- in AAI 201
Competencies

Understand the fundamentals of data-centric artificial intelligence (AI)
Perform data preprocessing and feature engineering
1. Apply techniques to clean, transform, and prepare data
2. Develop models using best practices
Evaluate AI models using appropriate algorithms and tools
Interpret AI model performance
1. List metrics used to access accuracy
2. Compare techniques to improve reliability
Identify ethical issues related to data privacy, bias, and fairness in AI systems
Communicate project results clearly and concisely to various stakeholders

Competencies Revised Date: AY2026