Data Mining, Predictive Analytics with Microsoft Analysis Services and Excel PowerPivot

This module will get students grounded in the terminology and concepts commonly utilized in data mining.

Concepts and Terminology

Data Mining and Results

CRISP-DM

Business Problems for Data Mining

Models, Induction, and Prediction

Data Mining Tasks

Key Concepts

Group discussion of data mining examples

After completing this module, students will be able to:

Have a firm understanding of the concept of data mining.

This module familiarizes the student with the data mining tools in SQL Server Analysis Services.

Introduction to SQL Server Data Tools

Project Walk-Through

Stepping Through the Data Mining Wizard

Testing and Validation of Mining Models

Cross Validation

The Mining Model Prediction Tab

Reports

The User Interface

Offline Mode and Immediate Mode

Data Source

Data View

Exploring Data

Named Calculation

Named Queries

Project Walk-Through to Completion of the Structure Parts 1 and 2

Explore the Models

Compare Mining Structures

Cross Validation

Creating Reports Using Reporting Services

Saving Queries

Saving Results to the Database

Multiple Nested Tables

After completing this module, students will be able to:

Explore the user interface.

Use offline mode and immediate mode.

Create and configure a data source.

Create and configure data view.

Explore data.

Create and configure named calculations.

Create and configure named queries.

Walk-through a project to completion.

Explore the models.

Compare mining structures.

Use cross validation.

Create reports using Reporting Services.

Save queries.

Save results to the database.

Create multiple nested tables off of a case table.

This module explains the Microsoft implementations of the generic types of algorithms uses in data mining. The students will work with each algorithm and implement an example of each.

Types of Data Mining Algorithms

Microsoft Decision Trees Algorithm

Microsoft Linear Regression Algorithm

Microsoft Clustering Algorithm

Microsoft Nave Bayes Algorithm

Microsoft Association Algorithm

Microsoft Sequence Clustering Algorithm

Microsoft Time Series Algorithm

Microsoft Neural Network Algorithm

Microsoft Logistic Regression Algorithm

Microsoft Association Rules Algorithm

Microsoft Sequence Clustering Algorithm

Microsoft Time Series Algorithm

Microsoft Neural Network Algorithm

After completing this module, students will be able to:

Use Microsoft Association Rules Algorithm.

Use Microsoft Sequence Clustering Algorithm.

Use Microsoft Time Series Algorithm.

Use Microsoft Neural Network Algorithm.

This module switches to the use of Excel with PowerPivot and the Data Mining Add-ins. Here the students will see the different capabilities between Excel and SQL Server Analysis Services and learn to use the data mining features of Excel and generate consumable reports from analytics and data mining.

Data Mining Tab

Connection

Data Preparation

Management

Model Usage

Accuracy and Validation

Data Modeling

Visio Data Mining Add-In

Data Preparation

Model Usage—Browse and Document Model

Model Usage—Query

Accuracy and Validation

Decision Trees

Logistic Regression

Nave Bayes

Neural Network

Estimate Tool

Cluster

Associate Tool

Forecast Tool

Table Analysis Tools

Visio Add-In

After completing this module, students will be able to:

Properly prepare data for mining.

Use Model Usage—Browse and Document Model.

Use Model Usage—Query.

Use Accuracy and Validation.

Use Decision Trees.

Use Logistic Regression.

Use Nave Bayes.

Use Neural Network.

Use Estimate Tool.

Use Cluster.

Use Associate Tool.

Use Forecast Tool.

Use Table Analysis Tools.

Use Visio Add-In.

This module consists of five scenarios to help reinforce the concepts covered in this course.

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 5

n/a