Microsoft SQL Server Data Mining

Data mining introduction

• What is data mining
• Why and when to use

The CRISP-DM methodology

The Cross-industry standard process for data mining (CRISP-DM) is a methodology for running and documenting the enterprise data mining process. It is technology independant. In this module we introduce this methodology in general, each of the next modules corresponds with a phase in this CRISP-DM methodology.

• The need for a methodology
• CRISP-DM reference model
• Introducing the 6 phases
• Alternative methodologies

Business and Data understanding

We must first be able to identify usefull data mining goals in our business. In this module you learn about the most common data mining goals such as regression and classification.

• Business objectives
• Data mining goals
• Data collection
• Exploring and validating data

Data preparation

Since data is the key ingredient in any data mining process, we must take care it is of good quality. This module explains how the concept of data quality is different for data mining than it is for other business intelligence processes.

• Data selection
• Data cleaning
• Feature extraction
• Data integration and formatting

Modeling

Modeling is the actual process of building mathematical models based on the data. You will get an overview of the different modeling techniques such as decission trees, neural networks, logistic regression, association rules and more!

• Modeling techniques
• Test design
• Model building
• Model assessment
• Data sources and data source views
• Decision trees
• Naïve Bayes
• Association rules
• Neural networks
• Clustering
• Time series
• Sequence clustering

Evaluation

Before a model can be used in production we must first be sure it's good enough. This can be done with statistical measurements, but also human inspection can be important.

• Model testing
• Cross-validation
• Model filtering

Data mining clients

For most of this training we use Visual Studio and Management Studio to build our models. But the applications which will query these models will be different. In this module we first show the Excel add-in for creating and consuming data mining models. Then the integration with Reporting Services is illustrated. We end with showing how .Net programmers can build applications on top of these models as well.

• Using the Excel data mining add-in
• Introduction into Data Mining eXpressions (DMX)
• Building DMX queries
• DMX in Reporting Services
• DMX in .Net applications