Share Description
In this course, you will :
- assists you in parsing large data sets and extracting the most meaningful, useful information
- This course, Data Science Foundations: Data Mining, is intended to provide a solid foundation for all of the tools, techniques, and tactical thinking that go into data mining.
- covers data sources and types, data mining languages and software (including R and Python), and task-based lessons that help you practise the most common data-mining techniques, such as text mining, data clustering, association analysis, and more.
- This course is a must-take for anyone interested in working in data science or who needs to brush up on their data mining skills.
Syllabus :
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
9. Text Mining
- Text mining goals
- Text mining algorithms
- Text mining in R
- Text mining in Python
- Text mining in RapidMiner
