- [Narrator] The thing about RapidMiner is that…it's a really busy interface.…That's okay.…There's a lot of stuff there…and we can simplify it a little bit.…This one is actually one of the default examples…in RapidMiner, but it works really well…for what we're trying to do.…The most important thing here…is first to get your data into RapidMiner.…So you have to come over to the repository…and click add data,…and then you go to my computer,…and then you select the data.…And you say for instance,…that you wanna put it in your local repository.…I've got it right here.…I've saved it as bakery.…
And then, that makes it available…to the rest of the program.…Then you get a new process,…and the first thing we need to do…is load the transactions.…This says the ID,…the transaction ID,…the product ID, the quantifier.…And so we click on that,…and then you come over here to say…which of the data sets in your repository…you're gonna be using.…And I selected my data from the bakery.…And then we have a whole bunch of separate processes.…
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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