Learn how to conduct extreme value analysis for outliers. This video covers point outliers, contextual outliers, and collective outliers.
- [Instructor] Now let's look at … extreme value analysis for outliers. … Most machine learning methods assume that … your data has been treated for outliers. … Detecting outliers can be a data preprocessing task … or an analytical method of its own merit. … Basically, you use outlier detection to … uncover anomalies in data. … In this section, we're going to talk about … univariate methods. … Some use cases for outlier analysis include … detecting fraud, detecting equipment failure, … and also cybersecurity event detection. … Tukey methods are useful for identifying … a variable as outliers. … You can detect unusually high or low data points … in a variable by applying the Tukey methods … for outlier detection. … Data points identified using Tukey methods … should be treated as potential outliers … to be investigated further. … This is a Tukey box plot. … I wanted to point it out to you so that … I could show you how to use a box plot … to detect outliers. … Box plot whiskers are set at 1.5 times …
AuthorLillian Pierson, P.E.
- Why use Python for working with data
- Filtering and selecting data
- Concatenating and transforming data
- Data visualization best practices
- Visualizing data
- Creating a plot
- Creating statistical data graphics
- Performing basic math and linear algebra
- Correlation analysis
- Multivariate analysis
- Data sourcing via web scraping
- Introduction to natural language processing
- Collaborative analytics with Plotly
Skill Level Intermediate
Python Statistics Essential Trainingwith Michele Vallisneri2h 58m Intermediate
1. Introduction to the Data Professions
High-level course road map1m 28s
2. Data Preparation Basics
3. Data Visualization 101
4. Practical Data Visualization
5. Basic Math and Statistics
6. Data Sourcing via Web Scraping
7. Collaborative Analytics with Plotly
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