Join Barton Poulson for an in-depth discussion in this video Algorithm prerequisites, part of Data Science Foundations: Data Mining.
- [Instructor] There are two very general…approaches to this, number one,…there are approaches based on classical statistics.…These are methods based on familiar approaches…in statistics, they are typically…transparent and easy to understand,…at least, maybe you have to be trained,…but you can still understand each step that goes into 'em,…and in many cases it's actually possible…to calculate them by hand, because many…of these procedures are 50 or 100 years old.…Next, there are modern machine learning approaches.…These are generally more complex methods,…often amazingly complex.…
They can be opaque, they call it a black box,…you can't really see what's going on.…You can describe it in generalities…but you can't follow the numbers all the way through.…And these methods often require very substantial…computing power, and they allow you to do things…that really just aren't possible…with a lot of the classical approaches.…And so in data mining, you'll find both of these.…So let's talk about a few algorithms in data mining…
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
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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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