In this module, you will explore how generative AI can help identify and address complex data quality issues. You will learn about issues such as inconsistent formats, missing values, outliers, hidden errors, and complex data structures. You will also consider how techniques such as anomaly detection, data imputation, and format conversion support cleaner and more reliable datasets.

In this module, you will examine how dataset preparation affects the quality and reliability of generative AI workflows. You will explore preprocessing concepts such as cleaning, transformation, normalization, feature engineering, and data wrangling. You will also compare synthetic, real-world, and hybrid datasets and consider when each type may be appropriate.

In this module, you will explore the key components of a well-structured dataset. You will consider how generative AI can help improve dataset relevance, accessibility, structure, and quality. You will also examine how optimized datasets can be compared with original datasets to evaluate changes.

In this module, you will examine ethical considerations related to data processing and synthetic data generation. You will explore issues such as bias, fairness, privacy, transparency, and potential misuse of data. You will also consider how responsible practices and ethics guidelines can support better decisions when working with generative AI and data.