SQL Server Data Mining

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.



Data Mining Development with Tatyana Yakushev  --  VISIT
 Hits: 168Updated:
Fri 09/12/2008 @ 12:00

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)

TechNet Webcast: 24 Hours of SQL Server 2008: Data Mining (Level 200)  --  VISIT
 Hits: 263Updated:
Wed 05/21/2008 @ 12:00

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.

Rafal Lukawiecki -- Data Mining and Business Intelligence for Enterprises (Part 1 of 4)  --  VISIT
 Hits: 174Updated:
Wed 04/02/2008 @ 12:00

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.

Rafal Lukawiecki -- Working with Data Mining (Part 2 of 4)  --  VISIT
 Hits: 67Updated:
Wed 04/02/2008 @ 12:00

April 2, 2008 (120 minutes)

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.

Rafal Lukawiecki -- Using Data Mining in your IT Systems (Part 3 of 4)  --  VISIT
 Hits: 23Updated:
Wed 04/02/2008 @ 12:00

April 2, 2008 (120 minutes)

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.

Rafal Lukawiecki -- Using Data Mining in your IT Systems (Part 4 of 4)  --  VISIT
 Hits: 13Updated:
Wed 04/02/2008 @ 12:00

April 2, 2008 (120 minutes)

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.


TechNet Webcast: Building and Validating Advanced Mining Models with SQL Server 2008 Data Mining (Level 200)  --  VISIT
 Hits: 61Updated:
Fri 03/07/2008 @ 12:00

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.

TechNet Webcast: Mastering Time Series Prediction with SQL Server 2008 Data Mining (Level 300)  --  VISIT
 Hits: 84Updated:
Thu 01/24/2008 @ 12:00

January 24, 2008 (60 minutes)

Time series data is one of the most useful sources for data mining. Whether it be estimating future gasoline prices or understanding the relationship between the weather and sales, there is a role for time series analysis. In the new release of Microsoft SQL Server 2008 Data Mining, Microsoft has introduced some important enhancements to our support in this area, making powerful analysis both more effective and easier to use. In this webcast, we not only introduce these new features, but we also cover many of the business scenarios for which time series prediction is invaluable.

TechNet Webcast: Mining for Quality: Apply Adaptive Data Quality with SQL Server Data Mining (Level 200)  --  VISIT
 Hits: 15Updated:
Thu 01/17/2008 @ 12:00

January 17, 2008 (60 minutes)

Good quality data is essential to a successful business intelligence application. You are probably aware that Microsoft SQL Server includes some useful data quality tools such as Fuzzy Grouping or Fuzzy Lookup. However, there is one tool you may have overlooked—SQL Server Data Mining. When used operationally, SQL Server Data Mining is extremely useful for finding data that lies outside the boundaries of known good data, and it finds these outliers inductively rather than relying on exhaustively hard-coded rules. In this webcast, we introduce this new, adaptive approach to data quality and we show how adaptive quality can be applied at many phases of the business intelligence project—whether data entry, during warehouse loading, or during analysis.

TechNet Webcast: Preparing Data for Use with SQL Server Data Mining (Level 200)  --  VISIT
 Hits: 14Updated:
Thu 01/10/2008 @ 12:00

January 10, 2008 (75 minutes)

Many users who struggle with data mining do not realize that their problems start with badly prepared data. This webcast is a very practical introduction to some of the important topics that you should understand when preparing your data for a data mining project with Microsoft SQL Server . Issues include finding the right data, handling missing values, identifying and fixing overloaded fields, and deriving useful variables.

MSDN Webcast: Creating Visualizations for SQL Server Data Mining (Level 300)  --  VISIT
 Hits: 34Updated:
Fri 11/30/2007 @ 12:00

November 30, 2007 (60 minutes)

Data mining is well-known for empowering users with insightful analyses from vast quantities of information. In many cases, the best way to present these analyses is visually. Microsoft SQL Server Data Mining includes useful visualization tools that can be embedded in your own applications. However, it is also possible to write your own visualizations, which can be useful for your specific needs. In this webcast, we show how to embed the existing visualization tools, but we also describe how to you can create your own . Examples include both rich and thin client visualizations.

TechNet Webcast: Data Mining (Part 3 of 3): Use Predictive Intelligence to Create Smarter KPIs (Level 200)  --  VISIT
 Hits: 74Updated:
Thu 11/29/2007 @ 12:00

November 29, 2007 (60 minutes)

The key performance indicator (KPI) is an essential tool in building business intelligence applications for executive and strategic use. However, traditional KPIs are built over historical data, showing what has happened in the past and the current state of the business. There is increasing demand for "predictive KPIs," which show not only current status but project future status. For example, rather than knowing how many customers churned last quarter, would it not be useful to know how many customers are in danger of churning next quarter? This webcast demonstrates how to design, build, and deploy predictive KPIs using Microsoft SQL Server Data Mining. Although there are useful worked examples, even non-technical users can benefit from this webcast by finding new possibilities for KPIs.

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