Join Barton Poulson for an in-depth discussion in this video Goals of data reduction, part of Data Science Foundations: Data Mining.
- [Presenter] Now you may say to yourself,…isn't more data better?…Aren't we all about big data?…Well, data reduction actually helps further the purposes…of data science analyses and data mining…by helping you simplify the data set…so you can focus on the variables…or the constructs, the things made from the variables,…that are most likely to carry…the meaning that you care about,…and least likely to carry the noise…that distracts you from that meaning.…Now I'm not referring to a reduction…of cases or observations.…There may be situations in which you want to do that.…
I'm talking about a simplification or reduction…in variables, or fields.…There's a few reasons that you might want to do that.…One is practicality.…You may simply be limited by your machine.…You may have storage constraints,…only so much hard drive space.…You may have memory constraints,…only so much RAM.…And maybe you're able to fire up…an Amazon web server and do that,…but sometimes you just have to do the work…on the machine that you have.…And you might only have…
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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