Join Barton Poulson for an in-depth discussion in this video Data reduction in Python, part of Data Science Foundations: Data Mining.
- [Voiceover] When you open up this notebook…in Jupyter in your web browser,…this is what you're going to see.…Now, some of these commands are slightly different…for a Macintosh and for a PC.…I'll point out when that's the case.…And some of them are slightly different…if you're using Python version 2 versus version 3.…Also, if you've installed Anaconda Continuum,…a lot of packages come installed with it.…But in case you don't necessarily have all of those,…we're going to manually install some of the key packages.…So I'm going to run this command,…pip install pandas, numpy, scipy and sklearn,…and actually it's telling me…that they're already installed…so it doesn't hurt to do it this way.…
Once they're installed, we can import them,…and then we can load some data.…I've saved the data file to the desktop as B5.csv.…Now, this is one where it makes a difference…if you're using a Mac or a Windows PC.…For Mac, you can specify it this way…with the Tilda for the user directory…and then desktop and then the name of the file.…
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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