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Welcome to Sigma Corp, a large conglomerate that produces nuclear, coal, and renewable energy sources. As a lead data scientist at Sigma, you’ve been tasked with creating mission-critical anomaly detection algorithms that will prevent operation interruptions at Sigma’s many facilities. You’ll develop the means to evaluate the performance of the algorithms using the receiver operating characteristic (ROC) curve and the area under curve (AUC) metrics. You’ll then build and implement a simple z-score anomaly detection algorithm for one-dimensional data. According to requirements and feedback, you’ll progress to implementing more complex methods designed for multidimensional data including the Mahalanobis distance (MD) method, the principal component analysis (PCA) method, the Empirical Cumulative distribution-based Outlier Detection (ECOD) method, and Isolation Forest algorithms. When you’re finished with this series of liveProjects, you’ll have a solid understanding of how anomaly detection methods work as well as the knowledge and skills to build them according to your specific needs.
This series is truly exceptional, encompassing the most vital approaches to anomaly detection.
Failure is not an option for Sigma Corp. As a lead data scientist for the large conglomerate of energy production companies, it’s up to you to help ensure interruption-free operations by developing a means for detecting anomalies that signal potential problems. Using metrics, including the receiver operating characteristic (ROC) curve and the area under curve (AUC) score, you’ll evaluate anomaly detection algorithms. You’ll build a z-score anomaly detection algorithm, which focuses on a single feature and provides a simple benchmark, and you’ll apply it to a dataset to establish a reference for comparison. When you’re finished, you’ll have a firm grasp of z-score anomaly detection, classification error categories, and evaluating anomaly detection algorithms.
Preventing operation failures and interruptions is mission-critical at Sigma Corp. The large conglomerate of energy production companies has recently implemented a z-score anomaly detection algorithm that focuses on a single feature. Now that the algorithm has proved its value, members of Sigma have requested additional algorithms that are just as simple to use, but that can handle multidimensional data. As a lead data scientist at Sigma, you’ll implement the Mahalanobis distance (MD) method and the principal component analysis (PCA) method as you build anomaly detection algorithms for multidimensional data. To gauge the performance of your algorithms, you’ll test them against a benchmark dataset as well as synthetic anomalies generated by your own algorithms. When you’re done, you’ll have firsthand experience building anomaly detection algorithms for multidimensional datasets as well as testing anomaly detection algorithms against both benchmark datasets and synthetic anomalies.
Sigma Corp, a large conglomerate of energy production companies, has recently implemented anomaly detection algorithms and is generally pleased with their performance. However, analysts report that not all anomalies are being identified and the algorithms are too slow at times. As a lead data scientist at Sigma, it’s up to you to address these concerns. To increase the robustness of the algorithms, you’ll implement and optimize the probability-based Empirical Cumulative distribution-based Outlier Detection (ECOD) method, an alternative to statistical methods. You’ll benchmark the ECOD method in order to compare its performance with the statistical MD and PCA methods Sigma is currently using. When you’re finished, you’ll have firsthand experience implementing the highly efficient ECOD method to detect anomalies in multidimensional data.
Red alert! One of the energy production companies managed by Sigma Corp has suffered an outage. An investigation has led to the conclusion that the facility’s anomaly detection mechanism failed to detect early signals due to a sudden change in the distribution of the analyzed data. As a lead data scientist at Sigma, you’ll build an Isolation Forest algorithm, which is less likely than the Empirical Cumulative distribution-based Outlier Detection (ECOD) method to fail in such scenarios. To gauge how robust your method is, you’ll benchmark your algorithms against adversarial scenarios, synthetic anomalies, and standard datasets. When you’re done, you’ll have practical experience creating, using, and testing the Isolation Forest algorithm as an effective alternative to ECOD in circumstances where the data distribution changes.
The knowledge is universal, and I can apply it to other similar tasks.
The subject covered is very important; implementing algorithms from scratch is always a good idea to understand.
This liveProject is for beginner data scientists interested in learning the sought-after skills of building, implementing, and evaluating anomaly detection algorithms. To begin these liveProjects you’ll need to be familiar with the following:TOOLS
In this liveProject series, you’ll learn to implement and evaluate progressively more complex anomaly detection algorithms.
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