Join Barton Poulson for an in-depth discussion in this video Clustering in BigML, part of Data Science Foundations: Data Mining.
- (Lecturer) Now I'm in my account here in BigML.com…and the first thing we need to do…is we need to bring in the data.…You do this in a few steps.…The first thing you do is you identify it as a source…so you're gonna have a data source…and there's a lot of different ways to do it.…You can bring it in from Google Drive or Dropbox.…You can actually type it into a window, copy and paste.…You can do it from a URL or in this case…I'm going to do it from a local file…so I'm gonna click on that…and then I've got the data saved here on my desktop…so I'll just open up that.…
Then, it takes a moment and there it is…at the top of the list now, so that is a source.…It's not, however, a data set I can work with yet.…It has to get converted into the BigML format…so that's something I'm going to do next.…I'm going to click on this and when I do that…I'm gonna make a few small changes to it.…I'm gonna just make sure that things…are the way I want them to.…Now, the fact that the states are text, that's fine.…Everything else is coding as quantitative.…
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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