Join Barton Poulson for an in-depth discussion in this video Data mining prerequisites, part of Data Science Foundations: Data Mining.
- [Narrator] You're simply trying to find patterns…or regularities within the data…especially ones that you did not see otherwise.…Now if you want to,…we can break this even to a few sort of sub-goals.…Number one, you do try to simplify the data a little bit…because when you have real data…and you got a lot of it…there is a lot of noise and so,…one of the primary beginning points…is to try to reduce that noise,…usually through something called dimensionality reduction.…And that's where you trying to find important variables…or combination of variables…that will either most informative…and you can ignore some of the one's that are noisiest.…
Now I know it sounds counter intuitive,…you spend all the time to get big data…why would you get rid of it?…Because it's really hard to see things…when you've got all these extra noises graininess going on,…and dimensionality reduction allows you to deal with that.…The second general task is to find cases…that you might say attract or avoid one another.…And this is trying to find groups.…
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
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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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