Join Barton Poulson for an in-depth discussion in this video Anomaly detection goals, part of Data Science Foundations: Data Mining.
- Let's begin our discussion of anomaly detection…by looking at the goals of the entire procedure.…In essence what you're trying to do…is set up a system that allows you to find the unexpected…so you can then react or respond to it appropriately.…Now I should begin very quickly with…the difference between anomalies and outliers.…Anomalies are things that are not supposed to happen,…they're unusual events that usually signal a problem.…So for instance if you have…sensors on a physical system of say, for instance, pipes,…or the electrical grid and you get an anomaly…that means something's gone wrong and you have to fix it.…
A really common place for anomalies is fraud detection.…When you get a set of conditions…that simply should not be happening…and that triggers an event that we need to respond to this,…we need to reject this transaction,…we need to find out what's going on.…And then another way to look at it is with diseases,…where an anomaly represents an usual combination…of symptoms that is not only unusual…
Barton Poulson covers data sources and types, the languages and software used in data mining (including R and Python), and specific task-based lessons that help you practice the most common data-mining techniques: text mining, data clustering, association analysis, and more. This course is an absolute necessity for those interested in joining the data science workforce, and for those who need to obtain more experience in data mining.
- Prerequisites for data mining
- Data mining using R, Python, Orange, and RapidMiner
- Data reduction
- Data clustering
- Anomaly detection
- Association analysis
- Regression analysis
- Sequence mining
- Text mining
Skill Level Beginner
Transitioning from Data Warehousing to Big Datawith Alan Simon1h 50m Intermediate
Big Data Foundations: Program Managementwith Alan Simon1h 11m Intermediate
2. Data Reduction
5. Anomaly Detection
6. Association Analysis
7. Regression Analysis
8. Sequential Patterns
9. Text Mining
Next steps1m 18s
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