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  Data Mining with SQL Server 2008
 

3-Day (BI-DMNNG08-401-EN)

Description
Target Audience
Prerequisites
Course Objectives
Course Summary Outline

Description

In this course attendees learn how to use Data Mining to find advanced patterns in their data and perform predictions based on the patterns found using SQL Server 2008 Analysis Services.

Target Audience

This course is intended for business intelligence application developers and advanced administrators.

Prerequisites

Before attending this course, it is recommended that students have the following skills:

  • Moderate experience with data warehousing, reporting and On-Line Analytical Processing
  • Familiarity with the Transact-SQL language
  • Knowledge of a .NET language like C# or VB.NET would be helpful

Course Objectives

Upon completion of this course, the student will be able to:

  • Describe what Data Mining is and what business questions it can answer
  • Explain the process of a Data Mining project
  • Explore and understand data using descriptive statistics, OLAP cubes, reports and other tools
  • Prepare the data to make better models
  • Understand the Data Mining algorithms, and when to use them
  • Create Data Mining Models and browse them
  • Evaluate models to find the one that gives best results
  • Use SQL Server 2008 Integration Services Data Mining tasks
  • Do Text Mining with Integration Services
  • Understand and use the Data Mining Extensions (DMX) language
  • Deploy Data Mining models in production using custom application, OLAP cubes or reports developed with SQL Server 2008 Reporting Services

Course Summary Outline

Module 01: Introduction to Data Mining

  • Introduction to Data Mining
    • Data Mining in SQL Server 2008
  • Analysis Services 2008
  • LAB 01A:  Tools and Sample Databases


Module 02: Data Preparation and Overview

  • Preparing the Data
  • Overview of the Data
  • LAB 02A:  Data Preparation and Overview


Module 03: Data Mining Algorithms Part 1

  • Naïve Bayes
  • Decision Trees
    • Linear Regression
  • Neural Networks
    • Logistic Regression
  • Checking the Accuracy
  • Advanced Topics
  • LAB 03A:  Data Mining Algorithms Part 1


Module 04: Data Mining Algorithms Part 2

  • Clustering
  • Sequence Clustering
  • Association Rules
  • Time Series
  • Advanced Topics
  • LAB 04A:  Data Mining Algorithms Part 2


Module 05: Using Integration Services with Data Mining

  • General Tasks and Transforms
  • Data Profiling
  • Data Mining tasks and Transforms
  • Text Mining
  • LAB 05A:  Data Mining and SSIS


Module 06: DMX Language

  • XMLA
  • Data Mining Structure and Data Mining Model
  • DMX Elements
  • DMX DDL Statements
  • DMX DML Statement
  • DMX Select
  • LAB 06A:  DMX Language


Module 07: Integrating Data Mining in BI Applications

  • Integrating with SSAS UDM
  • Integrating with SSRS
  • Office 2007 Data Mining Add-Ins
  • Advanced Topics
  • LAB 07A:  Integrating Data Mining in BI Applications


Module 08: Developing Data Mining Applications

  • Management Applications
  • Programming Clients
  • Server Stored Procedures

Module 09: Managing and Maintaining Data Mining models

  • Security in SSAS 2008
  • Managing Databases
  • Processing
  • Monitoring SSAS 2008
  • LAB 09A:  Managing and Maintaining Data Mining Models