This course introduces learners to the essential processes that transform raw data into AI-ready information. It covers AI data pipelines, data collection methods, data annotation techniques, data cleaning, preprocessing, and feature scaling. Learners will gain practical knowledge of how high-quality data supports the development of reliable and efficient AI systems. Learning Outcomes After completing this course, learners will be able to: 1. Explain the role and stages of an AI data pipeline. 2. Identify various data collection methods used in AI systems. 3. Describe data annotation techniques and their applications. 4. Apply basic data cleaning and preprocessing concepts. 5. Recognize the importance of feature scaling and data quality in AI. Evaluate how effective data pipelines improve AI performance Course Modules 1. Introduction to AI Data Pipeline 2. Data Collection Methods 3. Data Annotation 4. Data Cleaning & Preprocessing Target Audience · Undergraduate and postgraduate students · Commerce, Management, Computer Science, and Technology learners · Faculty members and educators interested in AI fundamentals · Beginners seeking a non-technical introduction to AI data processing · Researchers exploring AI and data-driven systems · Entrepreneurs and business professionals interested in AI adoption Attribution Statement: Portions of this educational resource have been adapted from Applications of Artificial Intelligence Across Domains: An Interdisciplinary Approach with No-Code Tools and Real-World Use Cases by Suneel Kumar Duvvuri (2026), licensed under CC BY-NC-SA 4.0. Adapted content has been reorganised and supplemented for instructional purposes.