Course details

Data Insights, Anomalies, & Verification: Handling Anomalies

Data Insights, Anomalies, & Verification: Handling Anomalies


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

In this 9-video course, learners examine statistical and machine learning implementation methods and how to manage anomalies and improvise data for better data insights and accuracy. The course opens with a thorough look at the sources of data anomaly and comparing differences between data verification and validation. You will then learn about approaches to facilitating data decomposition and forecasting, and steps and formulas used to achieve the desired outcome. Next, recall approaches to data examination and use randomization tests, null hypothesis, and Monte Carlo. Learners will examine anomaly detection scenarios and categories of anomaly detection techniques and how to recognize prominent anomaly detection techniques. Then learn how to facilitate contextual data and collective anomaly detection by using scikit-learn. After moving on to tools, you will explore the most prominent anomaly detection tools and their key components, and recognize the essential rules of anomaly detection. The concluding exercise shows how to implement anomaly detection with scikit-learn, R, and boxplot.



Expected Duration (hours)
0.8

Lesson Objectives

Data Insights, Anomalies, & Verification: Handling Anomalies

  • Course Overview
  • 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
  • Course Number:
    it_dsdiavdj_01_enus

    Expertise Level
    Intermediate