- [Narrator] The first thing we need to do…is we need to actually read the data in.…So I come over here where I can get my various…widgets or bits and functional operating bits,…and I'm going to drag in file.…Double click on that and let it know I want to read…this file.…You simply click on the folder and you tell it which one.…I want that one, I mean it's already what was there.…We will hit reload just to make sure its got it.…It has a 1,000 cases which is what it's supposed to have,…50 features or 50 different items that people…could purchase and it has zero meta attributes.…Which is fine for this data set.…
I'm going to close that and just so we know…what we are dealing with,…I'm also going to drag in this item data table.…That we we can sort of see what we have,…and I'll sort of drag from this corner to this corner.…Then when we open up data table, you can see that…we've got the question marks for the missing data.…You see the one's in there, there are a few of them…but that makes it possible to represent the data…
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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