Examine statistical and machine learning implementation methods and how to manage anomalies and improvise data for better data insights and accuracy.
Data Insights, Anomalies, & Verification: Handling Anomalies
- list sources of data anomaly and compare the differences between data verification and validation
- describe approaches of facilitating decomposition and forecasting, and list the steps and formulas used to achieve the desired outcome
- recall data examination approaches, and use randomization tests, null hypothesis, and Monte Carlo
- identify anomaly detection scenarios and categories of anomaly detection techniques
- recognize prominent anomaly detection techniques
- demonstrate how to facilitate contextual data and collective anomaly detection using scikit-learn
- list prominent anomaly detection tools and their key components
- recognize essential rules of anomaly detection
- implement anomaly detection using scikit-learn, R, and boxplot
If you would like to provide feedback for this course, please e-mail the NICCS SO at NICCS@hq.dhs.gov.