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SSAS 2012, Part 10 of 10: Data Mining

with expert Thomas LeBlanc

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Course at a glance

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Release date 1/25/2013
Level Intermediate
Runtime 1h 31m
Closed captioning Included
Transcript Included
eBooks / courseware Included
Hands-on labs Included
Sample code Included
Exams Included

Enterprise Solutions

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Course description

Data Mining is an analytical process to explore data looking for patterns. In this course, you will learn how to create a model from a data source and by using data sets and SQL Server Data Tools analyze data. By using business questions like "what customers may be inclined to buy a product" or "how products are grouped" to looking for patterns of fraud, business analytics are a powerful tool to both examine data and look for predictions. Finally, when Data Mining should be used vs a Lift chart will explained giving you power choices in getting control of your data.


This course assumes that students have working experience with SQL Server; basic relational database concepts (e.g., tables, queries, and indexing); data transformation services.

Learning Paths

This course is part of the following LearnNowOnline SuccessPaths™:
SQL Server Analysis Services

Meet the expert

Using Microsoft SQL Server started for Thomas with a Laboratory Information System in version 6.5. The Analysis Service (also called SSAS) option in version 7 got him excited about Data Warehousing, but before he used a production version of SSAS, he became a Database Administrator for versions 6.5 through 2005 while working at a paper mill and home health agency. After writing reports as an application developer for 10 years, he rediscovered Online Analytical Processing (OLAP) implemented into Data Warehouses. Since 2009, he has become a speaker in the SQL Server community and a voice for Microsoft Business Intelligence (MSBI) for enterprises. His transition from Sr. DBA to a Business Intelligence Architect has been a great career path. Thomas has certifications MCP, MCDBA and MCITP in Database Administration and Business Intelligence.

Course outline

Data Mining

Data Mining Concepts (22:13)
  • Introduction (00:32)
  • Mining Concepts (02:02)
  • Terminology (01:13)
  • Business Questions (02:45)
  • DMX Language (01:14)
  • Other Algorithm (03:55)
  • Demo: Data Mining (04:50)
  • Demo: Mining Views (05:11)
  • Summary (00:26)
Creating a Model (32:51)
  • Introduction (00:39)
  • Create a Mining Structure (01:41)
  • Predict to Buy Product (00:55)
  • Look at Data (01:24)
  • Model to Use (01:09)
  • Demo: Create a Model (02:17)
  • Demo: Data Mining Wizard (04:58)
  • Demo: Data Mining Wizard Cont. (06:12)
  • Modify/Review a Model (01:32)
  • Add Mining Model (01:19)
  • Review Cluster (01:25)
  • Other Cluster Views (01:14)
  • Demo: Adding More Algorithms (03:30)
  • Demo: Clusters (03:52)
  • Summary (00:42)
Accuracy and Predicting (35:55)
  • Introduction (00:40)
  • Add Filters (01:09)
  • Accuracy Chart (17:24)
  • Demo: Filter (05:54)
  • Use Data to Predict (01:21)
  • New Data (00:54)
  • Plug Into Model (00:47)
  • Run and Analyze (01:27)
  • Demo: Predict Based on a Model (05:36)
  • Summary (00:39)