Anomaly Detection in Big Data' is a course designed to equip doctoral candidates with the principles and techniques necessary to identify anomalies within large datasets. Throughout this course, candidates will explore various methodologies for anomaly detection, including statistical approaches, machine learning algorithms, and advanced data mining techniques. Candidates will also gain experience in handling data errors, discussing strategies such as human inspection, outlier removal, and leveraging both traditional imputation techniques and artificial intelligence to fill data gaps. Furthermore, the course teaches how to map data quality metrics to assess the reliability and integrity of financial datasets, ensuring robust anomaly detection processes. By the end of this course, candidates will possess the skills and insights needed to effectively detect and mitigate anomalies in big data environments, enabling to safeguard against financial risks and improve decision-making processes.