Data Insights, Anomalies, & Verification: Handling Anomalies from Skillsoft

Examine statistical and machine learning implementation methods and how to manage anomalies and improvise data for better data insights and accuracy.

Learning Objectives

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