Global Certificate in Detecting Data Anomalies: Future-Ready
-- ViewingNowThe Global Certificate in Detecting Data Anomalies is a comprehensive course, designed to equip learners with the essential skills needed to excel in data analysis and anomaly detection. In today's data-driven world, the ability to identify and analyze data anomalies is crucial for making informed business decisions and preventing potential risks.
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โข Unit 1: Introduction to Data Anomalies - Defining data anomalies, their impact, and the importance of detecting them. โข Unit 2: Types of Data Anomalies - Point anomalies, contextual anomalies, collective anomalies, and their characteristics. โข Unit 3: Data Preprocessing - Data cleaning, normalization, transformation, and aggregation techniques for anomaly detection. โข Unit 4: Statistical Methods for Anomaly Detection - Univariate and multivariate statistical techniques for anomaly detection. โข Unit 5: Machine Learning Techniques - Supervised, unsupervised, and semi-supervised learning approaches for anomaly detection. โข Unit 6: Deep Learning Techniques - Autoencoders, recurrent neural networks, and generative adversarial networks for anomaly detection. โข Unit 7: Time Series Anomaly Detection - Seasonal decomposition, exponential smoothing, and ARIMA models for anomaly detection in time series data. โข Unit 8: Anomaly Detection in Big Data - Distributed processing, sampling, and parallelization techniques for anomaly detection in big data. โข Unit 9: Real-world Applications - Fraud detection, intrusion detection, system health monitoring, and predictive maintenance. โข Unit 10: Ethics and Regulations - Data privacy, security, and ethical considerations in anomaly detection.
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