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  Home > Courses > Course Descriptions > Course Details Print Page
Course Description

OLAP Solutions Using Microsoft SQL Server 2005 Analysis Services (SSAS)

Course Outline

Introducing Decision Support Systems
  • What is Business Intelligence?
  • Starting from the Requirements and Understanding their Nature
  • What Business Intelligence Can Do For You
  • Data Warehouse Life cycle - Where OLAP fits in
  • Dimensional modeling Refresher
  • The Data Mart – Features & Structure
  • Snowflakes, Stars, and Analysis Services
  • OLAP v/s OLTP
  • OLAP v/s DWH
  • Data Mining Basics
The Unified Dimensional Model
  • Online Analytical Processing
  • Building OLAP—Out of Cubes
  • Features of an OLAP System
  • Architecture
  • The Unified Dimensional Model - Structure & Advantages
  • Tools of the Trade
Analyzing Cube Content
  • Measures and Dimensions
  • Creating a Cube
  • Measures
  • Measure Groups
  • Made-up Facts—Calculated Measures
  • It Doesn't Add Up—Measure Aggregates Other than SUM
  • Dimensions
  • Managing Dimensions
  • Relating Dimensions to Measure Groups
  • Types of Dimensions
  • Slowly Changing Dimensions
Deploying OLAP Cubes
  • Where No Cube Has Gone Before
  • Deploying and Processing
  • Deploying from the Business Intelligence Development Studio
  • Deploying from the Analysis Services Deployment Wizard
Advanced Cube Features
  • Linked Objects
  • The Business Intelligence Wizard
  • Key Performance Indicators
  • Actions
  • Partitions
  • Perspectives
  • Translations
MDX Scripting
  • Terms and Concepts
  • Where Are We?
  • Getting There from Here
  • Putting MDX Scripting to Work
  • Cube Security
  • This Year to Last Year Comparisons and Year-To-Date Rollups Extracting Data from Cubes
MDX Queries
  • The MDX SELECT Statement
  • The Basic MDX SELECT Statement
  • Additional Tools for Querying
  • Additional Dimensions
  • Additional MDX Syntax
  • Operators
  • Functions
Introduction to Data Mining
  • What Is Data Mining?
  • Order from Chaos
  • Tasks Accomplished by Data Mining
  • Steps for Data Mining
  • Data Mining Algorithms

Case Study 1 & 2

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