Join Barton Poulson for an in-depth discussion in this video Clustering in Python, part of Data Science Foundations: Data Mining.
- [Voiceover] When we get this IPython notebook open…in Jupyter the first thing we can do is install packages.…Again, this may not be necessary,…you may already have these installed,…but it doesn't hurt to run this anyhow.…And when we run this we see…that I've already got all of these installed,…that's fine, but we do need to import them.…That's what I'm gonna do right here by running this cell.…Now, this warning that came up it just tells us…that it's taking a moment to build the font cache.…Not a big deal we can ignore that one.…Then we can read the data.…Again, this is something that is different…if you're using a Mac or a Windows.…
If you're using a Mac you can run this first command…if you've saved it to the desktop.…That's what I'm gonna do.…If you have a Windows computer, then you're gonna need…to use this path instead,…except where it has bart you're gonna need…to change the name of your home directory then use that.…Either one will feed the CSV file…into an object called states…and then we can actually look at the columns, or variables,…
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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