Join Barton Poulson for an in-depth discussion in this video Anomaly detection in R, part of Data Science Foundations: Data Mining.
- [Narrator] When you get to RStudio,…we need to load a few packages to do…some of the procedures we are going to do.…I'm using pacman to load the packages, and we need…ggplot two, grid, great extra,…and robust base for these various procedures.…Next, we need to import the data.…I've saved the CSV file, anomaly data, to my desktop,…so I'm going to feed that into an object, called data,…and we can check the structure on that.…I see it's loaded on the top right, in environment.…I will zoom in on the results.…
Here we have our structure.…We have state, the name of the state, the state code,…the two letter code, and then a…whole bunch of various search terms;…How much your state has searched for data science,…cluster analysis, through to NBA, NFL, MLB,…modern dance, down to barbecue and royal family,…and then we have some information about the states…on their personality characteristics,…at a state level, extroversion, agreeableness, and so on.…
Then, we have regions created by psychology profiles,…and then region and division from standard demographics.…
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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