Module 1:

Identifying, Staging, and Understanding Data

Data mining life cycle

Staging data

Extract, transform, and load

Data warehouse

Measures and dimensions

Schema

Data mart

Refreshing data

Understanding and cleansing data

Summary

 

Module 2:

Data Model Preparation and Deployment

Preparing data models

Cross-Industry Standard Process for Data Mining

Validating data models

Preparing the data mining models

Deploying data models

Updating the models

Summary

 

Module 3:

Tools of the Trade

SQL Server BI Suite

SQL Server Engine

SQL Server Data Tools

SQL Server Data Quality Services

SQL Server Integration Services

SQL Server Analysis Services

SQL Server Reporting Services

Summary

 

Module 4:

Preparing the Data

Listing of popular databases

Migrating data from popular databases to a staging database

Migrating data from IBM DB2

Building a data warehouse

Automating data ingestion

Summary

 

Module 5:

Classification Models

Input, output, and predicted columns

The feature selection

The Microsoft Decision Tree algorithm
Data Mining Extensions for the Decision Tree algorithm

The Microsoft Neural Network algorithm

Data Mining Extensions for the Neural Network algorithm

The Microsoft Naïve Bayes algorithm

Data Mining Extensions for the Naïve Bayes algorithm

Summary

 

Module 6:

Segmentation and Association Models

The Microsoft Clustering algorithm

Data Mining Extensions for the Microsoft Clustering models

The Microsoft Association algorithm

Data Mining Extensions for the Microsoft Association models

Summary

 

Module 7:

Sequence and Regression Models

The Microsoft Sequence Clustering algorithm

Data Mining Extensions for the Microsoft Sequence Clustering models

The Microsoft Time Series algorithm

Summary

 

Module 8:

Data Mining Using Excel and Big Data

Data mining using Microsoft Excel

Data mining using HDInsight and Microsoft Azure Machine Learning

Microsoft Azure

Microsoft HDInsight

HDInsight PowerShell

Microsoft Azure Machine Learning

Summary 237

 

Module 9:

Tuning the Models

Getting the real-world data

Building the decision tree model

Tuning the model

Adding a clustering model to the data mining structure

Adding the Neural Network model to the data mining structure

Comparing the predictions of different models

Summary

 

Module 10:

Troubleshooting

A fraction of rows get transferred into a SQL table

Error during changing of the data type of the table

Troubleshooting the data mining structure performance

The Decision Tree algorithm
The Naïve Bayes algorithm

The Microsoft Clustering algorithm

The Microsoft Association algorithm

The Microsoft Time Series algorithm

Error during the deployment of a model

Summary

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