Join Barton Poulson for an in-depth discussion in this video Data reduction in R, part of Data Science Foundations: Data Mining.
- [Teacher] The first thing I'm going to do…is load a few packages.…This one, pacman, simply helps with loading packages,…and I'm going to use the package psych…for its principal components function.…And I'm also going to check the dependencies…'cause I know I'm gonna need…this one right here, GPArotation.…So I'm gonna come back and load that one separately.…And now I'm going to load some data.…What I've done is I've downloaded a data set,…a public data set from online,…that contains information from…the Big Five Personality Inventory.…
I'll show you some information about this data set.…If you go to the codebook.txt file,…it describes how the data was collected…and the 50 outcome variables in it.…They describe five personality factors writ large.…They're extraversion, neuroticism, agreeableness,…conscientiousness, openness to experience.…And we have 10 items for each of these,…so there's 50 items total.…You can see all the actual items here,…and they're rated on a one-to-five scale,…and we also have some demographic 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
2. Data Reduction
5. Anomaly Detection
6. Association Analysis
7. Regression Analysis
8. Sequential Patterns
9. Text Mining
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