Data mining is gaining popularity as the most advanced data analysis technique. With modern data mining engines, products, and packages, like SQL Server Analysis Services (SSAS), Excel, and R, data mining has become a black box. It is possible to use data mining without knowing how it works. However, not knowing how the algorithms work might lead to many problems, including using the wrong algorithm for a task, misinterpretation of the results, and more. This course explains how the most popular data mining algorithms work, when to use which algorithm, and advantages and drawbacks of each algorithm as well. Demonstrations show the algorithms usage in SSAS, Excel, and R.
Overview
Data mining is gaining popularity as the most advanced data analysis technique. With modern data mining engines, products, and packages, like SQL Server Analysis Services (SSAS), Excel, and R, data mining has become a black box. It is possible to use data mining without knowing how it works. However, not knowing how the algorithms work might lead to many problems, including using the wrong algorithm for a task, misinterpretation of the results, and more. This course explains how the most popular data mining algorithms work, when to use which algorithm, and advantages and drawbacks of each algorithm as well. Demonstrations show the algorithms usage in SSAS, Excel, and R.
Syllabus
- Introduction to Data Mining 31mins
- Naive Bayes and Decision Trees 34mins
- Linear Regression, Regression Trees, and Support Vector Machines 20mins
- Linear Regression, Neural Network, and Models Evaluation 26mins
- Time Series 24mins
- Clustering 17mins
- Association Rules and Sequence Clustering 24mins
Taught by
Dejan Sarka
