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LinkedIn Learning

Data Science Foundations: Data Mining

via LinkedIn Learning

Overview

Get started in data mining. Discover data mining techniques such as data reduction, clustering association analysis, and more, with data mining tools like R and Python.

Syllabus

Introduction
  • Welcome
  • Who should watch this course
  • Exercise files
1. Preliminaries
  • Data mining prerequisites
  • Algorithm prerequisites
  • Software prerequisites
2. Data Reduction
  • Goals of data reduction
  • Data for data reduction
  • Data reduction in R
  • Data reduction in Python
  • Data reduction in Orange
  • Data reduction in RapidMiner
3. Clustering
  • Clustering goals
  • Clustering data
  • Clustering in R
  • Clustering in Python
  • Clustering in BigML
  • Clustering in Orange
4. Classification
  • Classification goals
  • Classification data
  • Classification in R
  • Classification in Python
  • Classification in RapidMiner
  • Classification in KNIME
5. Anomaly Detection
  • Anomaly detection goals
  • Anomaly detection data
  • Anomaly detection in R
  • Anomaly detection in Python
  • Anomaly detection in BigML
  • Anomaly detection in RapidMiner
6. Association Analysis
  • Association analysis goals
  • Association analysis data
  • Association analysis in R
  • Association analysis in Python
  • Association analysis in Orange
  • Association analysis in RapidMiner
7. Regression Analysis
  • Regression analysis goals
  • Regression analysis data
  • Regression analysis in R
  • Regression analysis in Python
  • Regression analysis in KNIME
  • Regression analysis in RapidMiner
8. Sequential Patterns
  • Sequence mining goals
  • Sequence mining algorithms
  • Sequence mining in R
  • Sequence mining in Python
  • Sequence mining in BigML: Part 1
  • Sequence mining in BigML: Part 2
9. Text Mining
  • Text mining goals
  • Text mining algorithms
  • Text mining in R
  • Text mining in Python
  • Text mining in RapidMiner
Conclusion
  • Next steps

Taught by

Barton Poulson

Reviews

4.4 rating at LinkedIn Learning based on 268 ratings

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