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