Data Mining: Predictive Analytics with Microsoft SQL Server Analysis Services and Excel Using PowerPivot and the Data Mining Add-ins

Before attending this course, students should have:

Experience with Excel
Basic understanding of business analytics

Target Student

This course is intended for Power Users, IT Professionals, Report Developers, BI Professionals, Project Managers and Team Leads interested in exploring the Microsoft toolsets for data mining, machine learning, and predictive analytics.

Course duration

3 Days

Course outline

Module 1: Introduction

This module explains how the class will be structured and introduces course materials and additional administrative information.

Lessons

Introduction
Course Materials
Facilities
Prerequisites
What We'll Be Discussing

Lab 1: None

None

After completing this module, students will be able to:

Successfully log into their virtual machine.
Have a full understanding of what the course intends to cover.

Module 2: Data Mining Concepts

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

Lessons

Concepts and Terminology
Data Mining and Results
CRISP-DM
Business Problems for Data Mining
Models, Induction, and Prediction
Data Mining Tasks
Key Concepts

Lab 1: Data Mining 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.

Module 3: SQL Server Analysis Services Data Mining Tools

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

Lessons

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

Lab 1: SQL Server Analysis Services Data Mining Tools

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.

Module 4: The Microsoft Data Mining Algorithms

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.

Lessons

Types of Data Mining Algorithms
Microsoft Decision Trees Algorithm
Microsoft Linear Regression Algorithm
Microsoft Clustering Algorithm
Microsoft Naïve Bayes Algorithm
Microsoft Association Algorithm
Microsoft Sequence Clustering Algorithm
Microsoft Time Series Algorithm
Microsoft Neural Network Algorithm
Microsoft Logistic Regression Algorithm

Lab 1: The Microsoft Data Mining Algorithms

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.

Module 5: Excel PowerPivot Data Mining Add-ins

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.

Lessons

Data Mining Tab
Connection
Data Preparation
Management
Model Usage
Accuracy and Validation
Data Modeling
Visio Data Mining Add-In

Lab 1: Excel PowerPivot Data Mining Add-ins

Data Preparation
Model Usage—Browse and Document Model
Model Usage—Query
Accuracy and Validation
Decision Trees
Logistic Regression
Naïve 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:

Use Model Usage—Browse and Document Model.
Use Model Usage—Query.
Use Accuracy and Validation.
Use Decision Trees.
Use Logistic Regression.
Use Naïve Bayes.
Use Neural Network.
Use Estimate Tool.
Use Cluster.
Use Associate Tool.
Use Forecast Tool.
Use Table Analysis Tools.
Use Visio Add-In.

Module 6: Concept Reinforcement Scenarios

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

Lessons

Scenario 1
Scenario 2
Scenario 3
Scenario 4
Scenario 5

Lab 1: Concept Reinforcement Scenarios

Concept Reinforcement Scenarios

After completing this module, students will be able to:

Complete five different business scenarios that further reinforce the concepts learned.

Please contact your training representative for more details on having this course delivered onsite or online

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