Data mining with excel 2010 and power pivot

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This document discusses data mining with Excel 2010 and PowerPivot. It introduces data mining concepts and tasks like supervised and unsupervised learning. It describes the data mining add-in for Excel, which provides algorithms for tasks like classification, clustering, and association rules. PowerPivot is introduced as a tool for loading large datasets from various sources and performing advanced analytics using DAX expressions. The document concludes by providing resources for further learning about data mining.

Data Mining
with Excel 2010
and PowerPivot

Mark Tabladillo Ph.D.
MTabladillo <(at)> solidq.com
September 18, 2010

SQL Saturday 46 -- Raleigh NC
#sqlsat46

© 2010 Mark Tabladillo Ph.D.
2

MarkTab & Data Mining

© 2010 Mark Tabladillo Ph.D.
3

Outline

© 2010 Mark Tabladillo Ph.D.
What is What is
Demos
Data Mining PowerPivot

6

Data Mining as a Service

© 2010 Mark Tabladillo Ph.D.
7

Outline

© 2010 Mark Tabladillo Ph.D.
What is What is
Demos
Data Mining PowerPivot

8

Data Mining Definitions
• Data mining
• Machine Learning
• Data mining algorithms -- typically use estimation or
optimization to achieve results (as opposed to only
calculations).

© 2010 Mark Tabladillo Ph.D.
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Data Mining Tasks
• Supervised
• Answer known, what is correlated?
• Unsupervised
• Answer unknown (unspecified), what are the groups?
• Forecasting

© 2010 Mark Tabladillo Ph.D.
• Given a trend, what is next?

Value
Slide

10

Data Mining Add-In for Excel
• Requires Analysis Services instance
• Version 10.00.2531.00 (April 2009)
• 32-Bit Add-In
• Microsoft .NET Framework 2.0 (32-bit)
• Office 2007 (Professional, Professional Plus, Ultimate,

© 2010 Mark Tabladillo Ph.D.
Enterprise)
• SQL Server Enterprise or Standard (or Developer) 2008 or
higher

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The Analyze Tab

© 2010 Mark Tabladillo Ph.D.
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The Analyze Tab

Menu Option Data Mining Algorithm
Analyze Key Influencers Naïve Bayes

© 2010 Mark Tabladillo Ph.D.
Detect Categories Clustering
Fill from Example Logistic Regression
Forecast Time Series
Highlight Exceptions Clustering
Scenario Analysis (Goal Seek) Logistic Regression
Scenario Analysis (What If) Logistic Regression
Prediction Calculator Logistic Regression
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Shopping Basket Analysis Association Rules

Data Mining Tab

© 2010 Mark Tabladillo Ph.D.
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Data Mining Tab

Many

© 2010 Mark Tabladillo Ph.D.
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Data Mining Capacities

SQL Server 2008 R2 Analysis Services
Maximum sizes/numbers
Object
Maximum data mining models per
2^31-1 = 2,147,483,647
structure
Maximum data mining structures per

© 2010 Mark Tabladillo Ph.D.
2^31-1 = 2,147,483,647
solution
Maximum data mining structures per
2^31-1 = 2,147,483,647
Analysis Services database
Maximum data mining attributes
2^31-1 = 2,147,483,647
(variables) per structure

Reference:
http://www.marktab.net/datamining/index.php/2010/08/01/sql-server-
data-mining-capacities-2008-r2/ 16

Data Mining Tab

© 2010 Mark Tabladillo Ph.D.
17

Outline

© 2010 Mark Tabladillo Ph.D.
What is What is
Demos
Data Mining PowerPivot

18

PowerPivot for Excel
• Take advantage of familiar Excel tools and
features
• Process massive amounts of data in seconds
• Load even the largest data sets from virtually any

© 2010 Mark Tabladillo Ph.D.
source
• Use powerful new analytical capabilities, such as
Data Analysis Expressions (DAX)
• Make the most of multi-core processors and
gigabytes of memory
19

PowerPivot for Excel Sources
• SQL Server
• SQL Azure
• Oracle, Teradata, Sybase, Informix, IBM DB2
• OLEDB/ODBC

© 2010 Mark Tabladillo Ph.D.
• Analysis Services (SSAS)
• Reporting Services (SSRS)
• Excel, Text File

20

PowerPivot Reference
• http://www.powerpivot.com (Product Site)
• http://www.powerpivotpro.com (Blog Site)

© 2010 Mark Tabladillo Ph.D.
21

Outline

© 2010 Mark Tabladillo Ph.D.
What is What is
Demos
Data Mining PowerPivot

22

Resources
• MarkTab.NET
Blog, links, video resources and information for
data mining
• Blog: http://marktab.net/datamining

© 2010 Mark Tabladillo Ph.D.
• Twitter: @MarkTabNet

23

Regroup and Conclusion
• Main Points from this Presentation

© 2010 Mark Tabladillo Ph.D.
25

Contact Information
• Mark Tabladillo
mtabladillo <{at}> solidq.com

• Also on:
Twitter

© 2010 Mark Tabladillo Ph.D.
Linked In

26

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This document outlines a presentation on data mining with Excel 2010 and PowerPivot. It introduces key concepts in data mining like supervised and unsupervised learning. It describes the data mining add-in for Excel, which provides algorithms for tasks like classification, clustering, and association rule mining. It also explains PowerPivot, a tool for loading large datasets and performing advanced analytics using DAX expressions. The presentation covers these topics, provides demos, and concludes with resources for further learning.

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Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency. During the hour, we’ll take you through: Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board. Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes. Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI. We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI. This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!

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Neo4j

Leonard Jayamohan, Partner & Generative AI Lead, Deloitte This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.

Removing Uninteresting Bytes in Software Fuzzing

Aftab Hussain

Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process. In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds. - These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.

UiPath Test Automation using UiPath Test Suite series, part 5

DianaGray10

National Security Agency - NSA mobile device best practices

Quotidiano Piemontese

HCL Notes and Domino License Cost Reduction in the World of DLAU

panagenda

Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/ The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this! We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model. Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward. These topics will be covered - Reducing license cost by finding and fixing misconfigurations and superfluous accounts - How do CCB and CCX licenses really work? - Understanding the DLAU tool and how to best utilize it - Tips for common problem areas, like team mailboxes, functional/test users, etc - Practical examples and best practices to implement right away

GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...

Neo4j

Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.

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Data mining with excel 2010 and power pivot

1. Data Mining with Excel 2010 and PowerPivot Mark Tabladillo Ph.D. MTabladillo <(at)> solidq.com September 18, 2010

9. Data Mining Definitions • Data mining • Machine Learning • Data mining algorithms -- typically use estimation or optimization to achieve results (as opposed to only calculations). © 2010 Mark Tabladillo Ph.D. 9

10. Data Mining Tasks • Supervised • Answer known, what is correlated? • Unsupervised • Answer unknown (unspecified), what are the groups? • Forecasting © 2010 Mark Tabladillo Ph.D. • Given a trend, what is next? Value Slide 10

11. Data Mining Add-In for Excel • Requires Analysis Services instance • Version 10.00.2531.00 (April 2009) • 32-Bit Add-In • Microsoft .NET Framework 2.0 (32-bit) • Office 2007 (Professional, Professional Plus, Ultimate, © 2010 Mark Tabladillo Ph.D. Enterprise) • SQL Server Enterprise or Standard (or Developer) 2008 or higher 11

13. The Analyze Tab Menu Option Data Mining Algorithm Analyze Key Influencers Naïve Bayes © 2010 Mark Tabladillo Ph.D. Detect Categories Clustering Fill from Example Logistic Regression Forecast Time Series Highlight Exceptions Clustering Scenario Analysis (Goal Seek) Logistic Regression Scenario Analysis (What If) Logistic Regression Prediction Calculator Logistic Regression 13 Shopping Basket Analysis Association Rules

16. Data Mining Capacities SQL Server 2008 R2 Analysis Services Maximum sizes/numbers Object Maximum data mining models per 2^31-1 = 2,147,483,647 structure Maximum data mining structures per © 2010 Mark Tabladillo Ph.D. 2^31-1 = 2,147,483,647 solution Maximum data mining structures per 2^31-1 = 2,147,483,647 Analysis Services database Maximum data mining attributes 2^31-1 = 2,147,483,647 (variables) per structure Reference: http://www.marktab.net/datamining/index.php/2010/08/01/sql-server- data-mining-capacities-2008-r2/ 16

19. PowerPivot for Excel • Take advantage of familiar Excel tools and features • Process massive amounts of data in seconds • Load even the largest data sets from virtually any © 2010 Mark Tabladillo Ph.D. source • Use powerful new analytical capabilities, such as Data Analysis Expressions (DAX) • Make the most of multi-core processors and gigabytes of memory 19

20. PowerPivot for Excel Sources • SQL Server • SQL Azure • Oracle, Teradata, Sybase, Informix, IBM DB2 • OLEDB/ODBC © 2010 Mark Tabladillo Ph.D. • Analysis Services (SSAS) • Reporting Services (SSRS) • Excel, Text File 20

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