Join Barton Poulson for an in-depth discussion in this video Regression analysis goals, part of Data Science Foundations: Data Mining.
- One of the most important procedures…you can do in any data analytic project…is regression.…Really, the general idea here is…that you want to use many variables…to predict scores or outcomes on one.…To show the most basic version of this,…we have a bivariate scatter plot here.…We have two quantitative variables…where we have the speed of a car…measured across the bottom,…and the distance to stop,…and these are from cars from about a hundreds years ago,…so the points are a little bit different.…But, while we have a scatter plot,…each dot represents a paired observation,…the speed and the time.…
What regression does is,…it allows us to give a very…parsimonious explanation, or summary of this data.…All we need to do is, we draw a straight line…through the data.…There's the same data,…but now with what's called…a regression line going through it.…That's the diagonal line here, and what that is…is it's a straight line,…because we're doing something called…linear regression, or straight lines,…that minimizes a value,…in this case called the sum of square deviations,…
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
Manage Your Organization's Big Data Programwith 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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