Microsoft Data Mining technology provides advanced analytics using SQL Server Analysis Services and Microsoft Office.
This webpage provides links for videos and other online resources.
September 12, 2008 (5 minutes)
Tatyana Yakushev is a Developer in the SQL Server Analysis Services Team. She discusses working as a Data Mining Developer on the Analysis Services Team as well as Data Mining for Microsoft Office. (Level 200)
May 21, 2008 (90 minutes)
[Fictional company] Contoso wants to implement data mining to help the sales department better understand its customer base and forecast future sales based on historical trends. In this webcast, we review the data mining capabilities of the Microsoft business intelligence (BI) platform, including:
- Using Microsoft SQL Server Data Mining Add-ins for Excel for table analysis. - Developing a mining structure with multiple mining models. - Viewing mining model results including drill-through and reviewing accuracy. - Viewing results of mining models using Data Mining Add-ins for Excel. Using the Time Series algorithm for forecasting.
April 2, 2008 (120 minutes)
Introduction to Data Mining
This session commences with the discussion of the concepts, the terminology used in the discipline of Data Mining and reviews of the common scenarios and applications for the use of Data Mining. It also takes a look at the "bigger picture" of the discipline of Business Intelligence and as well as how Data Mining is part of it. The fundamental process for data mining, looking at the concepts of data assets and their preparedness is introduced. Finally, the technology product roadmap is presented showing you relationships between Data Mining and technologies of Microsoft SQL Server 2008 and 2005, Microsoft Office 2007, and other systems. At the end of this session, you should have a good understanding of applications of Data Mining.
You are ready to mine data - what are the steps and what is the recommended order? This session covers the already-introduced Data Mining process in detail and studies its main steps: model preparation, model training, testing and evaluation of the built model, deployment, and ongoing model maintenance. It looks at possible exceptions and problems faces with in this process, such as missing or inconsistent data, or even data that seems fine but produces strange results. Finally, we want to make sure that the intelligence you are gathering is of quality that you expected. At the end of this session, you will know more about how to use data mining.
This session provides an overview of scenarios and cases most commonly encountered by IT professionals. These cases are suited to data mining. The presentation covers a number of canonical applications of data mining from the perspective of a practical scenario in order to show how to correctly select the best features of Data Mining technologies. Finally, tips and tricks are presented for correct configuration of data mining tools and their parameters.
This part of the session explores the exceptions and commonly encountered issues such as seasonality or incomplete or even inconsistent inputs. The presentation looks at the application of data mining for day-to-day needs of an IT professional, System Admininstrator or a Security Officer such as: analyzing infrastructure performance characteristics, creation of higher-level data sources, or discovery of insecure chains of infrastructure events that could lead to fraud.
March 7, 2008 (60 minutes)
Microsoft SQL Server 2008 includes several important enhancements for data mining. One area will be of particular interest to the user who has progressed beyond simple models—there are new features for building many and varied models over common mining structures, and also for validating the accuracy of these models. In this webcast, you learn how to use these new techniques, not only through the user interface (UI), but also programmatically from within your own applications.