Join Barton Poulson for an in-depth discussion in this video Sequence mining in BigML: Part 1, part of Data Science Foundations: Data Mining.
- [Narrator] Now the first thing you need to do…is bring in your data source, and…you're going to read from a local file,…in this case, it's gonna be a csv file.…But I've already done that so,…I've got it right here as my source.…Then what you do, is you click on that…and you need to then configure a dataset.…We don't need the row id in this.…We can deselect it and hit Create dataset.…I've already done that though,…so I'm gonna head right over to Datasets.…And here's my dataset, speed.…
It's based on this csv file.…I'm gonna click on that and again,…one of the nice things about bigml is…it shows you the distributions.…You get some information about the variables immediately.…We've got these four things and…we're gonna be using corr as our outcome.…But let's do this, what we're gonna do is…we're gonna come up and we're going to configure a model.…That's just a decision tree.…We have to tell where the objective field is,…in this case, I need to tell that it's corr.…And I see that the little target has moved up to that one.…
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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