Join Barton Poulson for an in-depth discussion in this video Sequence mining in R, part of Data Science Foundations: Data Mining.
- [Voiceover] Now we're going to be doing an analysis…to check for changes in response methods.…We're going to use something called a hidden Markov model,…and what that does is it looks for whether people are…changing between different states or different…methods of responding.…The data set that we're going to be using is speed.csv.…It's about reactions to a judgment task…in a psychology experiment.…And the idea is that people are either responding quickly…or they're responding accurately,…that there are these two qualitatively different methods.…
And we're going to use the hidden Markov models…to see if two different states matches the data.…We're going to first load two packages:…pacman, to simply manage the packages, but also depmixS4.…That's the one that's going to allow us to do…the hidden Markov model.…And I'm going to be using a data set that's…from that package called speed,…and I'll be using that, by the way,…in my other demonstrations, but I'll be…importing it as a CSV in those ones.…So let's take a quick look at speed.…
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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