SlideShare une entreprise Scribd logo
1  sur  11
Rodrigo Ramos Dornel
www.rdornel.com
(Site/Blog/Videos)

       @rdornel

Microsoft MCP, MCTS, MCITP e MCT
SolidQ – Data Platform Engineer
http://www.solidq.com/br-pt
Data Mining
Introducing Data Mining Concepts and Tools with SQL Server 2012




• Reference

         http://msdn.microsoft.com/en-us/library/bb510516
Data Mining
Introducing Data Mining Concepts and Tools with SQL Server 2012




• Data Mining Concepts
• Data Mining Algorithms
• Mining Structures
• Mining Models
• Testing and Validation
• Data Mining Queries
• Data Mining Solutions
• Data Mining Architecture
• Data Mining Tools
Data Mining
Introducing Data Mining Concepts and Tools with SQL Server 2012




• Data Mining Concepts

  •   Is Data Mining part of BI – Business Intelligence?

  •   Is Data Mining part of BA – Business Analytics?




                                                                        Reference and Recommendation:
                     http://timoelliott.com/blog/2011/03/business-analytics-vs-business-intelligence.html
                                                         http://en.wikipedia.org/wiki/Business_analytics
Data Mining
Introducing Data Mining Concepts and Tools with SQL Server 2012




• Data Mining Concepts

  •   In other words, querying, reporting, OLAP, and alert tools can answer questions such as
      what happened, how many, how often, where the problem is, and what actions are
      needed.

  (Summarize)

  •   Business analytics can answer questions like why is this happening, what if these trends
      continue, what will happen next (that is, predict), what is the best that can happen (that
      is, optimize)

  (Tendency)
Data Mining
Introducing Data Mining Concepts and Tools with SQL Server 2012




• Data Mining Concepts

  •   Data mining is the process of discovering information, trend and knowledge from large
      sets of data (any data).

  •   Uses statistical and mathematic techniques to derive patterns and trends that exist in
      data.

  •   This task cannot be resolved with the traditional database query's, OLTP or OLAP.

  •   In Data Mining world you want recommendations, sequences, groups and risk.

  •   You have not structured decision´s.
Data Mining
Introducing Data Mining Concepts and Tools with SQL Server 2012




• Data Mining Concepts

  •   First Step: What do you want? Oh God, it´s hard to define it!!!

  •   What are you looking for? What types of relationships are you trying to find?

  •   Do you want to make predictions from the data mining model, or just look for interesting
      patterns and associations?



  This is very important: “To answer these questions, you might have to conduct a data
  availability study, to investigate the needs of the business users with regard to the available
  data. So, if the data does not support the needs of the users, you might have to redefine the
  project.”
Data Mining
Introducing Data Mining Concepts and Tools with SQL Server 2012




• Data Mining Concepts

  •   Second Step: Ok, I have the data and now ?! …

  •   We need to standardize, normalize, discretize, clean, and correct this data. Put this data in
      one place.

  •   How can we do this?

  •   SQL Server 2012 and older versions can help you:
      –   Integration Services in Business Intelligence Development Studio
      –   Master Data Services
      –   Data Quality Services
Data Mining
Introducing Data Mining Concepts and Tools with SQL Server 2012




• Data Mining Concepts


• Integration Services in Business Intelligence Development Studio


     –   Master Data Services
     –   http://msdn.microsoft.com/en-us/sqlserver/ff943581.aspx
     –   Data Quality Services
     –   http://technet.microsoft.com/en-us/sqlserver/hh780961.aspx
Data Mining
Introducing Data Mining Concepts and Tools with SQL Server 2012




• Data Mining Concepts
• Fisrt Demonstration

  •   Discretizing data

  •   Normalizing data

  •   SSIS Look Up
Rodrigo Ramos Dornel
                                             www.rdornel.com
                                             (Site/Blog/Videos)

                                                    @rdornel

                                             Microsoft MCP, MCTS, MCITP e MCT
                                             SolidQ – Data Platform Engineer
                                             http://www.solidq.com/br-pt




Little Tip:

(Basic Data Mining Tutorial) http://msdn.microsoft.com/en-us/library/ms167167

Contenu connexe

Tendances

Mastering Customer Data on Apache Spark
Mastering Customer Data on Apache SparkMastering Customer Data on Apache Spark
Mastering Customer Data on Apache SparkCaserta
 
Graph Databases - Where Do We Do the Modeling Part?
Graph Databases - Where Do We Do the Modeling Part?Graph Databases - Where Do We Do the Modeling Part?
Graph Databases - Where Do We Do the Modeling Part?DATAVERSITY
 
Watson Studio : ML Made Simple
Watson Studio : ML Made SimpleWatson Studio : ML Made Simple
Watson Studio : ML Made SimpleMofizur Rahman
 
Data Modeling for Big Data & NoSQL Technologies with Karen Lopez
Data Modeling for Big Data & NoSQL Technologies with Karen LopezData Modeling for Big Data & NoSQL Technologies with Karen Lopez
Data Modeling for Big Data & NoSQL Technologies with Karen LopezEmbarcadero Technologies
 
What's with the 1s and 0s? Making sense of binary data at scale - Berlin Buzz...
What's with the 1s and 0s? Making sense of binary data at scale - Berlin Buzz...What's with the 1s and 0s? Making sense of binary data at scale - Berlin Buzz...
What's with the 1s and 0s? Making sense of binary data at scale - Berlin Buzz...gagravarr
 
Data Security and Protection in DevOps
Data Security and Protection in DevOps Data Security and Protection in DevOps
Data Security and Protection in DevOps Karen Lopez
 
Harmonizing Data for the Warehouse
Harmonizing Data for the WarehouseHarmonizing Data for the Warehouse
Harmonizing Data for the WarehouseKalido
 
Applied Data Science Course Part 1: Concepts & your first ML model
Applied Data Science Course Part 1: Concepts & your first ML modelApplied Data Science Course Part 1: Concepts & your first ML model
Applied Data Science Course Part 1: Concepts & your first ML modelDataiku
 
H2O World - Data Science in Action @ 6sense - Viral Bajaria
H2O World - Data Science in Action @ 6sense - Viral BajariaH2O World - Data Science in Action @ 6sense - Viral Bajaria
H2O World - Data Science in Action @ 6sense - Viral BajariaSri Ambati
 
The Key to Keys - Database Design
The Key to Keys - Database DesignThe Key to Keys - Database Design
The Key to Keys - Database DesignKaren Lopez
 
Webinar: Proofpoint, a pioneer in security-as-a-service protects people, info...
Webinar: Proofpoint, a pioneer in security-as-a-service protects people, info...Webinar: Proofpoint, a pioneer in security-as-a-service protects people, info...
Webinar: Proofpoint, a pioneer in security-as-a-service protects people, info...DataStax
 
Agile Data Science by Russell Jurney_ The Hive_Janruary 29 2014
Agile Data Science by Russell Jurney_ The Hive_Janruary 29 2014Agile Data Science by Russell Jurney_ The Hive_Janruary 29 2014
Agile Data Science by Russell Jurney_ The Hive_Janruary 29 2014The Hive
 

Tendances (16)

Mastering Customer Data on Apache Spark
Mastering Customer Data on Apache SparkMastering Customer Data on Apache Spark
Mastering Customer Data on Apache Spark
 
Before Kaggle
Before KaggleBefore Kaggle
Before Kaggle
 
DataHub
DataHubDataHub
DataHub
 
Graph Databases - Where Do We Do the Modeling Part?
Graph Databases - Where Do We Do the Modeling Part?Graph Databases - Where Do We Do the Modeling Part?
Graph Databases - Where Do We Do the Modeling Part?
 
Watson Studio : ML Made Simple
Watson Studio : ML Made SimpleWatson Studio : ML Made Simple
Watson Studio : ML Made Simple
 
Data Modeling for Big Data & NoSQL Technologies with Karen Lopez
Data Modeling for Big Data & NoSQL Technologies with Karen LopezData Modeling for Big Data & NoSQL Technologies with Karen Lopez
Data Modeling for Big Data & NoSQL Technologies with Karen Lopez
 
What's with the 1s and 0s? Making sense of binary data at scale - Berlin Buzz...
What's with the 1s and 0s? Making sense of binary data at scale - Berlin Buzz...What's with the 1s and 0s? Making sense of binary data at scale - Berlin Buzz...
What's with the 1s and 0s? Making sense of binary data at scale - Berlin Buzz...
 
Big Data Analytics
Big Data AnalyticsBig Data Analytics
Big Data Analytics
 
Data Security and Protection in DevOps
Data Security and Protection in DevOps Data Security and Protection in DevOps
Data Security and Protection in DevOps
 
Harmonizing Data for the Warehouse
Harmonizing Data for the WarehouseHarmonizing Data for the Warehouse
Harmonizing Data for the Warehouse
 
Applied Data Science Course Part 1: Concepts & your first ML model
Applied Data Science Course Part 1: Concepts & your first ML modelApplied Data Science Course Part 1: Concepts & your first ML model
Applied Data Science Course Part 1: Concepts & your first ML model
 
H2O World - Data Science in Action @ 6sense - Viral Bajaria
H2O World - Data Science in Action @ 6sense - Viral BajariaH2O World - Data Science in Action @ 6sense - Viral Bajaria
H2O World - Data Science in Action @ 6sense - Viral Bajaria
 
The Key to Keys - Database Design
The Key to Keys - Database DesignThe Key to Keys - Database Design
The Key to Keys - Database Design
 
Webinar: Proofpoint, a pioneer in security-as-a-service protects people, info...
Webinar: Proofpoint, a pioneer in security-as-a-service protects people, info...Webinar: Proofpoint, a pioneer in security-as-a-service protects people, info...
Webinar: Proofpoint, a pioneer in security-as-a-service protects people, info...
 
Big Data Modeling
Big Data ModelingBig Data Modeling
Big Data Modeling
 
Agile Data Science by Russell Jurney_ The Hive_Janruary 29 2014
Agile Data Science by Russell Jurney_ The Hive_Janruary 29 2014Agile Data Science by Russell Jurney_ The Hive_Janruary 29 2014
Agile Data Science by Russell Jurney_ The Hive_Janruary 29 2014
 

En vedette

SQL Saturday 119 Chicago -- Enterprise Data Mining with SQL Server
SQL Saturday 119 Chicago -- Enterprise Data Mining with SQL ServerSQL Saturday 119 Chicago -- Enterprise Data Mining with SQL Server
SQL Saturday 119 Chicago -- Enterprise Data Mining with SQL ServerMark Tabladillo
 
Palestra sql saturday 361
Palestra sql saturday 361Palestra sql saturday 361
Palestra sql saturday 361Rodrigo Dornel
 
Reunião02 pass chapter - desenvolvimento
Reunião02 pass chapter - desenvolvimentoReunião02 pass chapter - desenvolvimento
Reunião02 pass chapter - desenvolvimentoRodrigo Dornel
 
SQL Server Heterogêneo: SQL Server + BigData
SQL Server Heterogêneo: SQL Server + BigDataSQL Server Heterogêneo: SQL Server + BigData
SQL Server Heterogêneo: SQL Server + BigDataRodrigo Dornel
 
Mentoring para prova MTA - Fundamento de Banco de Dados
Mentoring para prova MTA - Fundamento de Banco de DadosMentoring para prova MTA - Fundamento de Banco de Dados
Mentoring para prova MTA - Fundamento de Banco de DadosRodrigo Dornel
 
Power bi na prática 2016
Power bi na prática 2016Power bi na prática 2016
Power bi na prática 2016Rodrigo Dornel
 
SQL Saturday 570 - São Paulo - 2016
SQL Saturday 570 - São Paulo - 2016SQL Saturday 570 - São Paulo - 2016
SQL Saturday 570 - São Paulo - 2016Rodrigo Dornel
 

En vedette (7)

SQL Saturday 119 Chicago -- Enterprise Data Mining with SQL Server
SQL Saturday 119 Chicago -- Enterprise Data Mining with SQL ServerSQL Saturday 119 Chicago -- Enterprise Data Mining with SQL Server
SQL Saturday 119 Chicago -- Enterprise Data Mining with SQL Server
 
Palestra sql saturday 361
Palestra sql saturday 361Palestra sql saturday 361
Palestra sql saturday 361
 
Reunião02 pass chapter - desenvolvimento
Reunião02 pass chapter - desenvolvimentoReunião02 pass chapter - desenvolvimento
Reunião02 pass chapter - desenvolvimento
 
SQL Server Heterogêneo: SQL Server + BigData
SQL Server Heterogêneo: SQL Server + BigDataSQL Server Heterogêneo: SQL Server + BigData
SQL Server Heterogêneo: SQL Server + BigData
 
Mentoring para prova MTA - Fundamento de Banco de Dados
Mentoring para prova MTA - Fundamento de Banco de DadosMentoring para prova MTA - Fundamento de Banco de Dados
Mentoring para prova MTA - Fundamento de Banco de Dados
 
Power bi na prática 2016
Power bi na prática 2016Power bi na prática 2016
Power bi na prática 2016
 
SQL Saturday 570 - São Paulo - 2016
SQL Saturday 570 - São Paulo - 2016SQL Saturday 570 - São Paulo - 2016
SQL Saturday 570 - São Paulo - 2016
 

Similaire à Data mining (Part I)

24 Hours of PASS -- Enterprise Data Mining with SQL Server
24 Hours of PASS -- Enterprise Data Mining with SQL Server24 Hours of PASS -- Enterprise Data Mining with SQL Server
24 Hours of PASS -- Enterprise Data Mining with SQL ServerMark Tabladillo
 
Doing Analytics Right - Building the Analytics Environment
Doing Analytics Right - Building the Analytics EnvironmentDoing Analytics Right - Building the Analytics Environment
Doing Analytics Right - Building the Analytics EnvironmentTasktop
 
SPSChicagoBurbs 2019 - What is CDM and CDS?
SPSChicagoBurbs 2019 - What is CDM and CDS?SPSChicagoBurbs 2019 - What is CDM and CDS?
SPSChicagoBurbs 2019 - What is CDM and CDS?Nicolas Georgeault
 
Architecting Your Own DBaaS in a Private Cloud with EM12c
Architecting Your Own DBaaS in a Private Cloud with EM12cArchitecting Your Own DBaaS in a Private Cloud with EM12c
Architecting Your Own DBaaS in a Private Cloud with EM12cGustavo Rene Antunez
 
Bbbt presentation 210415_final_2
Bbbt presentation 210415_final_2Bbbt presentation 210415_final_2
Bbbt presentation 210415_final_2Roland Bullivant
 
SQL Saturday 86 -- Enterprise Data Mining with SQL Server
SQL Saturday 86 -- Enterprise Data Mining with SQL ServerSQL Saturday 86 -- Enterprise Data Mining with SQL Server
SQL Saturday 86 -- Enterprise Data Mining with SQL ServerMark Tabladillo
 
Introduction To SQL Server 2014
Introduction To SQL Server 2014Introduction To SQL Server 2014
Introduction To SQL Server 2014Vishal Pawar
 
Democratizing Data Science in the Enterprise
Democratizing Data Science in the EnterpriseDemocratizing Data Science in the Enterprise
Democratizing Data Science in the EnterpriseJesus Rodriguez
 
Big Data Analytics with Microsoft
Big Data Analytics with MicrosoftBig Data Analytics with Microsoft
Big Data Analytics with MicrosoftCaserta
 
The Death of the Star Schema
The Death of the Star SchemaThe Death of the Star Schema
The Death of the Star SchemaDATAVERSITY
 
Best practice for_agile_ds_projects
Best practice for_agile_ds_projectsBest practice for_agile_ds_projects
Best practice for_agile_ds_projectsKhalid Kahloot
 
AnalytixLabs - Data Science 360 (Nasscom)-1648178720283 (1).pdf
AnalytixLabs - Data Science 360 (Nasscom)-1648178720283 (1).pdfAnalytixLabs - Data Science 360 (Nasscom)-1648178720283 (1).pdf
AnalytixLabs - Data Science 360 (Nasscom)-1648178720283 (1).pdfNamanGulati17
 
Data Modeling - Series 1 Storing summarised data
Data Modeling - Series 1 Storing summarised dataData Modeling - Series 1 Storing summarised data
Data Modeling - Series 1 Storing summarised dataDAGEOP LTD
 
Self-Service Analytics with Guard Rails
Self-Service Analytics with Guard RailsSelf-Service Analytics with Guard Rails
Self-Service Analytics with Guard RailsDenodo
 
Big Data Expo 2015 - Barnsten Why Data Modelling is Essential
Big Data Expo 2015 - Barnsten Why Data Modelling is EssentialBig Data Expo 2015 - Barnsten Why Data Modelling is Essential
Big Data Expo 2015 - Barnsten Why Data Modelling is EssentialBigDataExpo
 
Introduction to Master Data Services in SQL Server 2012
Introduction to Master Data Services in SQL Server 2012Introduction to Master Data Services in SQL Server 2012
Introduction to Master Data Services in SQL Server 2012Stéphane Fréchette
 

Similaire à Data mining (Part I) (20)

Data mining (Part II)
Data mining (Part II)Data mining (Part II)
Data mining (Part II)
 
24 Hours of PASS -- Enterprise Data Mining with SQL Server
24 Hours of PASS -- Enterprise Data Mining with SQL Server24 Hours of PASS -- Enterprise Data Mining with SQL Server
24 Hours of PASS -- Enterprise Data Mining with SQL Server
 
Doing Analytics Right - Building the Analytics Environment
Doing Analytics Right - Building the Analytics EnvironmentDoing Analytics Right - Building the Analytics Environment
Doing Analytics Right - Building the Analytics Environment
 
Mine craft:
Mine craft: Mine craft:
Mine craft:
 
SPSChicagoBurbs 2019 - What is CDM and CDS?
SPSChicagoBurbs 2019 - What is CDM and CDS?SPSChicagoBurbs 2019 - What is CDM and CDS?
SPSChicagoBurbs 2019 - What is CDM and CDS?
 
Architecting Your Own DBaaS in a Private Cloud with EM12c
Architecting Your Own DBaaS in a Private Cloud with EM12cArchitecting Your Own DBaaS in a Private Cloud with EM12c
Architecting Your Own DBaaS in a Private Cloud with EM12c
 
Bbbt presentation 210415_final_2
Bbbt presentation 210415_final_2Bbbt presentation 210415_final_2
Bbbt presentation 210415_final_2
 
SQL Saturday 86 -- Enterprise Data Mining with SQL Server
SQL Saturday 86 -- Enterprise Data Mining with SQL ServerSQL Saturday 86 -- Enterprise Data Mining with SQL Server
SQL Saturday 86 -- Enterprise Data Mining with SQL Server
 
Introduction To SQL Server 2014
Introduction To SQL Server 2014Introduction To SQL Server 2014
Introduction To SQL Server 2014
 
Democratizing Data Science in the Enterprise
Democratizing Data Science in the EnterpriseDemocratizing Data Science in the Enterprise
Democratizing Data Science in the Enterprise
 
Big Data Analytics with Microsoft
Big Data Analytics with MicrosoftBig Data Analytics with Microsoft
Big Data Analytics with Microsoft
 
The Death of the Star Schema
The Death of the Star SchemaThe Death of the Star Schema
The Death of the Star Schema
 
Lean Analytics: How to get more out of your data science team
Lean Analytics: How to get more out of your data science teamLean Analytics: How to get more out of your data science team
Lean Analytics: How to get more out of your data science team
 
Best practice for_agile_ds_projects
Best practice for_agile_ds_projectsBest practice for_agile_ds_projects
Best practice for_agile_ds_projects
 
AnalytixLabs - Data Science 360 (Nasscom)-1648178720283 (1).pdf
AnalytixLabs - Data Science 360 (Nasscom)-1648178720283 (1).pdfAnalytixLabs - Data Science 360 (Nasscom)-1648178720283 (1).pdf
AnalytixLabs - Data Science 360 (Nasscom)-1648178720283 (1).pdf
 
Data Modeling - Series 1 Storing summarised data
Data Modeling - Series 1 Storing summarised dataData Modeling - Series 1 Storing summarised data
Data Modeling - Series 1 Storing summarised data
 
Self-Service Analytics with Guard Rails
Self-Service Analytics with Guard RailsSelf-Service Analytics with Guard Rails
Self-Service Analytics with Guard Rails
 
Big Data Expo 2015 - Barnsten Why Data Modelling is Essential
Big Data Expo 2015 - Barnsten Why Data Modelling is EssentialBig Data Expo 2015 - Barnsten Why Data Modelling is Essential
Big Data Expo 2015 - Barnsten Why Data Modelling is Essential
 
Introduction to Master Data Services in SQL Server 2012
Introduction to Master Data Services in SQL Server 2012Introduction to Master Data Services in SQL Server 2012
Introduction to Master Data Services in SQL Server 2012
 
Alphonso_Triplett.Sr_Prometheus_Phoenix
Alphonso_Triplett.Sr_Prometheus_PhoenixAlphonso_Triplett.Sr_Prometheus_Phoenix
Alphonso_Triplett.Sr_Prometheus_Phoenix
 

Plus de Rodrigo Dornel

Biweek Mineração de Dados com SQL Server
Biweek   Mineração de Dados com SQL ServerBiweek   Mineração de Dados com SQL Server
Biweek Mineração de Dados com SQL ServerRodrigo Dornel
 
Reunião #1 – 2015 – Overview
Reunião #1 – 2015 – OverviewReunião #1 – 2015 – Overview
Reunião #1 – 2015 – OverviewRodrigo Dornel
 
Mineração de dados com SQL Server - Datamining
Mineração de dados com SQL Server - DataminingMineração de dados com SQL Server - Datamining
Mineração de dados com SQL Server - DataminingRodrigo Dornel
 
Reunião 02 PASS Chapter MCITPSC
Reunião 02 PASS Chapter MCITPSCReunião 02 PASS Chapter MCITPSC
Reunião 02 PASS Chapter MCITPSCRodrigo Dornel
 
Reunião01 Pass Chapter - MCITPSC
Reunião01 Pass Chapter - MCITPSCReunião01 Pass Chapter - MCITPSC
Reunião01 Pass Chapter - MCITPSCRodrigo Dornel
 
Mineração com sql server 2008 r2
Mineração com sql server 2008 r2Mineração com sql server 2008 r2
Mineração com sql server 2008 r2Rodrigo Dornel
 

Plus de Rodrigo Dornel (6)

Biweek Mineração de Dados com SQL Server
Biweek   Mineração de Dados com SQL ServerBiweek   Mineração de Dados com SQL Server
Biweek Mineração de Dados com SQL Server
 
Reunião #1 – 2015 – Overview
Reunião #1 – 2015 – OverviewReunião #1 – 2015 – Overview
Reunião #1 – 2015 – Overview
 
Mineração de dados com SQL Server - Datamining
Mineração de dados com SQL Server - DataminingMineração de dados com SQL Server - Datamining
Mineração de dados com SQL Server - Datamining
 
Reunião 02 PASS Chapter MCITPSC
Reunião 02 PASS Chapter MCITPSCReunião 02 PASS Chapter MCITPSC
Reunião 02 PASS Chapter MCITPSC
 
Reunião01 Pass Chapter - MCITPSC
Reunião01 Pass Chapter - MCITPSCReunião01 Pass Chapter - MCITPSC
Reunião01 Pass Chapter - MCITPSC
 
Mineração com sql server 2008 r2
Mineração com sql server 2008 r2Mineração com sql server 2008 r2
Mineração com sql server 2008 r2
 

Dernier

Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Orbitshub
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businesspanagenda
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherRemote DBA Services
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodJuan lago vázquez
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century educationjfdjdjcjdnsjd
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024The Digital Insurer
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Angeliki Cooney
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Zilliz
 
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​Bhuvaneswari Subramani
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...apidays
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusZilliz
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontologyjohnbeverley2021
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsNanddeep Nachan
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfsudhanshuwaghmare1
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MIND CTI
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdfSandro Moreira
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingEdi Saputra
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...DianaGray10
 

Dernier (20)

Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)
 
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontology
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 

Data mining (Part I)

  • 1. Rodrigo Ramos Dornel www.rdornel.com (Site/Blog/Videos) @rdornel Microsoft MCP, MCTS, MCITP e MCT SolidQ – Data Platform Engineer http://www.solidq.com/br-pt
  • 2. Data Mining Introducing Data Mining Concepts and Tools with SQL Server 2012 • Reference http://msdn.microsoft.com/en-us/library/bb510516
  • 3. Data Mining Introducing Data Mining Concepts and Tools with SQL Server 2012 • Data Mining Concepts • Data Mining Algorithms • Mining Structures • Mining Models • Testing and Validation • Data Mining Queries • Data Mining Solutions • Data Mining Architecture • Data Mining Tools
  • 4. Data Mining Introducing Data Mining Concepts and Tools with SQL Server 2012 • Data Mining Concepts • Is Data Mining part of BI – Business Intelligence? • Is Data Mining part of BA – Business Analytics? Reference and Recommendation: http://timoelliott.com/blog/2011/03/business-analytics-vs-business-intelligence.html http://en.wikipedia.org/wiki/Business_analytics
  • 5. Data Mining Introducing Data Mining Concepts and Tools with SQL Server 2012 • Data Mining Concepts • In other words, querying, reporting, OLAP, and alert tools can answer questions such as what happened, how many, how often, where the problem is, and what actions are needed. (Summarize) • Business analytics can answer questions like why is this happening, what if these trends continue, what will happen next (that is, predict), what is the best that can happen (that is, optimize) (Tendency)
  • 6. Data Mining Introducing Data Mining Concepts and Tools with SQL Server 2012 • Data Mining Concepts • Data mining is the process of discovering information, trend and knowledge from large sets of data (any data). • Uses statistical and mathematic techniques to derive patterns and trends that exist in data. • This task cannot be resolved with the traditional database query's, OLTP or OLAP. • In Data Mining world you want recommendations, sequences, groups and risk. • You have not structured decision´s.
  • 7. Data Mining Introducing Data Mining Concepts and Tools with SQL Server 2012 • Data Mining Concepts • First Step: What do you want? Oh God, it´s hard to define it!!! • What are you looking for? What types of relationships are you trying to find? • Do you want to make predictions from the data mining model, or just look for interesting patterns and associations? This is very important: “To answer these questions, you might have to conduct a data availability study, to investigate the needs of the business users with regard to the available data. So, if the data does not support the needs of the users, you might have to redefine the project.”
  • 8. Data Mining Introducing Data Mining Concepts and Tools with SQL Server 2012 • Data Mining Concepts • Second Step: Ok, I have the data and now ?! … • We need to standardize, normalize, discretize, clean, and correct this data. Put this data in one place. • How can we do this? • SQL Server 2012 and older versions can help you: – Integration Services in Business Intelligence Development Studio – Master Data Services – Data Quality Services
  • 9. Data Mining Introducing Data Mining Concepts and Tools with SQL Server 2012 • Data Mining Concepts • Integration Services in Business Intelligence Development Studio – Master Data Services – http://msdn.microsoft.com/en-us/sqlserver/ff943581.aspx – Data Quality Services – http://technet.microsoft.com/en-us/sqlserver/hh780961.aspx
  • 10. Data Mining Introducing Data Mining Concepts and Tools with SQL Server 2012 • Data Mining Concepts • Fisrt Demonstration • Discretizing data • Normalizing data • SSIS Look Up
  • 11. Rodrigo Ramos Dornel www.rdornel.com (Site/Blog/Videos) @rdornel Microsoft MCP, MCTS, MCITP e MCT SolidQ – Data Platform Engineer http://www.solidq.com/br-pt Little Tip: (Basic Data Mining Tutorial) http://msdn.microsoft.com/en-us/library/ms167167

Notes de l'éditeur

  1. Apresentação da empresa, tema e expositor ressaltando as certificações
  2. Apresentação da empresa, tema e expositor ressaltando as certificações