Join Barton Poulson for an in-depth discussion in this video Sequence mining in BigML: Part 2, part of Data Science Foundations: Data Mining.
- [Narrator] I've imported the data set speed.csv…as a source, but to be able to use it…you actually have to create a data set with it properly.…So I click on that source file,…and then I'm going to create a data set.…I do configure data set.…And I give it a name.…I'm just gonna give it the same name, speed,…because I know what that means.…I'm going to keep all of it,…and I'm going to not include the row variable.…I don't need that.…But I'm going to keep the other ones in there.…
Hit create data set.…Great, it opens it up in data set,…and I get to see the distribution.…I see we have a bi-modal distribution here…of reaction times.…That's really important because the theory…is that there are two different modes…of people making judgments in this task.…And then we also have the payoff for accuracy,…and you see the distribution there.…And corr is whether they got the current round correct…and prev is whether they got the previous round correct.…And what we're going to do here is not…a sequential mining analysis,…but we're actually just gonna do a logistic regression.…
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
Manage Your Organization's Big Data Programwith 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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