Join Barton Poulson for an in-depth discussion in this video Data reduction in Orange, part of Data Science Foundations: Data Mining.
- [Voiceover] You should know that you can launch Orange…from the Anaconda Distribution, if you have that.…However, on my version, that's going to launch…an older version of Orange that's not going to have…the functionality we need.…Instead, you need to download the current stand alone…version of Orange,…and when you launch that, you'll get this window.…And what you have here is a blank worksheet…in which you get to drag icons in…that have functions for working with data.…We're going to start by using File,…which is a way of reading the data.…
I'm going to double click on that one,…just let it know the file that I'm using.…Now, because I've used it before, it already has b5.csv…and you just click on here…and go to the desktop and you can choose it there.…I've already got it so I'm going to leave it that way,…so it knows what data I'm going to read.…Then I need to read it into a data table.…I'm going to drag this down a little bit…and I just come over here to this edge and I click on that…and I drag up to where I want the next item,…
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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