Join Barton Poulson for an in-depth discussion in this video Software prerequisites, part of Data Science Foundations: Data Mining.
- [Instructor] The software for data mining…falls into two very general categories.…The first is text interfaces,…these are generally programming languages…that use written commands,…and they're easy to share and they're easy to repeat,…so that's nice.…The other category is graphical interfaces,…and these include specialized applications…that use menus, widgets, virtual connections,…and it's really easy to see the process,…although, it may be harder to share it…and it may be harder to repeat it.…Let's begin by looking at some of the text interfaces,…then we'll look at the graphics ones.…
One of the most fundamental tools in data science…and data mining is the statistical programming language R,…and you can download this for free at r-project.org.…Simply click on the download R link, and now look at you,…what you need is simply choose a server…and you choose your operating system.…In addition, you'll also want to download something…called RStudio, which is an interface that lays on top of R,…and makes it much easier to deal with,…
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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