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Data Profiling
 
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Automated data profiling within Alteryx Designer evaluates the completeness and quality of a dataset prior to building a model.
Views: 10489 Alteryx
Data Quality Concepts
 
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Learn about data problems with multiple examples and the data QA process. The volume is low. Please click the Cc button to see subtitles in English. Next, view VBScript tutorials at https://www.youtube.com/watch?v=03BfHDJsFpk&index=1&list=PLc3SzDYhhiGXH8hEHtayRPdwAsddelkh6 Follow me on: Website: http://inderpsingh.blogspot.com/ Google+: https://plus.google.com/+InderPSingh Twitter: https://twitter.com/inder_p_singh
Profiling Household Energy Consumption TEAM O Data Mining
 
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The goal of this project is to analyze household energy consumption data and predict the consumer’s energy consumption behavior. This analysis can be used to provide recommendations individuals to help them reduce their energy consumption. By combining the three datasets that represented a subset of the results of the Residential Energy Consumption Survey taken in 1995, various predictive models were explored. These models helped classify the consumers into 2 categories: high energy consumers and low energy consumers by estimating the most sensitive control parameters that would help reduce household energy consumption.
Views: 653 Deepa Parmeswaran
Data Quality Concepts | Data Quality Tutorial | Data Warehousing Tutorial | Edureka
 
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***** Data Warehousing & BI Training: https://www.edureka.co/data-warehousing-and-bi ***** Data quality assurance is the process of profiling the data to discover inconsistencies and other anomalies in the data, as well as performing data cleansing activities (e.g. removing outliers, missing data interpolation) to improve the data quality . These activities can be undertaken as part of data warehousing or as part of the database administration of an existing piece of applications software. Video covers the following topics : 1.Data Quality Concept 2Error Handling Concepts 3.ETL Summary 4.Data Extraction 5.Data Transform 6.Data Loading 7.What is Data warehouse? 8.Data warehouse Architecture 9.Why Data warehouse is used? Related Blogs: http://www.edureka.co/blog/a-brief-on-etl/?utm_source=youtube&utm_medium=referral&utm_campaign=data-quality-concept http://www.edureka.co/blog/architecture-of-a-data-warehouse/?utm_source=youtube&utm_medium=referral&utm_campaign=data-quality-concept Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world. The topics related to ‘Introduction to Dataware Housing’ have been covered in our course ‘Datawarehousing‘. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004
Views: 25548 edureka!
SuperDemographics Customer Profile Analysis
 
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Customer profile analysis based on a list of 6-digit postal code, including heat map, demographic, spending and lifestyle cluster profile reports.
Tips & Tricks for Segmentation (Targeting, Profiling, Classification)
 
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Segmentation (Targeting, Profiling, Classification) is the process of dividing a database into distinct groups of individuals who share common characteristics. This is readily accomplished using modern data mining and machine learning techniques. The methods are easily implemented and work well with large datasets containing nonlinearities, interactions in the data and a mix of categorical and numerical variables. In this webinar, you will learn, via step-by-step instruction, how to use modern techniques to: 1) Segment a large database AND 2) Look at an already segmented/clustered database and discover the reasons for the class memberships. Access the data set, slides, and step-by-step guide here: http://info.salford-systems.com/customer-segmentation-webinar http://www.salford-systems.com
Views: 1052 Salford Systems
Intelligent Pattern Profiling on Semistructured Data with Machine Learning
 
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Trifacta Co-founder & CTO Sean Kandel and Senior Software Engineer Karthik Sethuraman share specific examples of this approach using common data formats and real-world customer use cases and discuss future plans for how this approach will evolve over time to handle a wider range of formats and provide more automated structuring suggestions for common tasks.
Views: 813 Trifacta
How to run cluster analysis in Excel
 
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A step by step guide of how to run k-means clustering in Excel. Please note that more information on cluster analysis and a free Excel template is available at http://www.clusteranalysis4marketing.com
Views: 99558 MktgStudyGuide
Data Profiling Task - SQL Server 2008
 
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Full Detailed Article for "Data Profiling Task" is available at : http://sqlserver-training.com/data-profiling-sql-server-2008-video-tutorials Follow us at more Updates at ----------------------------------------- Website : http://sqlserver-training.com/ Twitter : http://twitter.com/dbatag/ Facebook : http://facebook.com/dbatag/ Linked In : http://in.linkedin.com/in/dbatag/ ================================ Data Profiling is being supported by SQL Server 2008 for SQL Server data. You use the Data Profiling task to configure the profiles that you want to compute and then run the SSIS package that contains the task. The task creates results in XML format, which you can then review in the Data Profile Viewer. Data profiling can help you to: • Analyze and understand your source data better and more efficiently. • Identify data quality problems before they are moved to the data warehouse Information that is gathered during profiling may include: • The percentage of null values. • The distribution of values in the column. • Column statistics for numeric columns. • Regular expressions that match string columns. • Validity of a relationship between columns. • Candidate key columns. • Functional dependencies between columns. For example, the strength of the dependency between the state and zip code fields. • The inclusion of the set of values in one column in the set of values in another column. How to Open Data Profile Viewer To run the Data Profile Viewer, on the Start menu, click in the run box, type dataprofileviewer and then press ENTER. Visit the following link for more details and SQL Server Tutorials http://sqlserver-training.com/data-profiling-sql-server-2008-video-tutorials
Views: 21300 DBATAG
Customer Segmentation
 
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By using advanced analytics to create your segmentation strategies, you can: - Identify your most proitable customers - Focus your marketing on segments most likely to purchase - Discover potential niche markets - Develop or improve products to meet customer needs For more information visit http://www.angoss.com/predictive-analytics-software/applications/customer-analytics/
Views: 26588 AngossSoftware
Advanced Customer Segmentation for Consumer Marketing - MindEcology.com
 
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http://www.mindecology.com This video shows you how to use advanced market (customer) segmentation techniques that go beyond using traditional demographic data alone. Video uses data-oriented examples so that you can see how it really works. The result is better-targeted advertising and marketing for better return on investment (ROI). http://www.mindecology.com
Views: 23805 Jed Jones
Profiling a Target Variable Before a Predictive Model
 
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View this video tip from Catherine Truxillo to learn how to profile target variables using the segment profile node in SAS® Enterprise Miner™. This material is related to the type of content covered in the SAS training course, "Advanced Analytics for the Modern Business Analyst." To learn more, visit: https://support.sas.com/edu/schedules.html?ctry=us&id=1076 To view another video tip on using the segment profile node, visit http://www.youtube.com/watch?v=Cm0fDWHpbwg
Views: 16539 SAS Software
Data Analysis with Python and Pandas Tutorial Introduction
 
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Pandas is a Python module, and Python is the programming language that we're going to use. The Pandas module is a high performance, highly efficient, and high level data analysis library. At its core, it is very much like operating a headless version of a spreadsheet, like Excel. Most of the datasets you work with will be what are called dataframes. You may be familiar with this term already, it is used across other languages, but, if not, a dataframe is most often just like a spreadsheet. Columns and rows, that's all there is to it! From here, we can utilize Pandas to perform operations on our data sets at lightning speeds. Sample code: http://pythonprogramming.net/data-analysis-python-pandas-tutorial-introduction/ Pip install tutorial: http://pythonprogramming.net/using-pip-install-for-python-modules/ Matplotlib series starts here: http://pythonprogramming.net/matplotlib-intro-tutorial/
Views: 515565 sentdex
Introduction to Data Quality Profiling and Scorecards
 
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An Introduction to Data Quality Profiling and Scorecards by Robert Whelan.He is an expert in Data Quality. DQ version 9.6
Views: 9041 dataUtrust
Profiling Excel
 
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Views: 1568 LBS ANALYSIS
Data Mining Tutorial: Building The Optimal CART Tree
 
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http://www.salford-systems.com Learn how to forward step, back prune, and perfect your CART model in the Salford Predictive Modeler software suite.
Views: 1328 Salford Systems
Create an SSIS Data Profiling Task In SQL Server
 
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I Use SQL Server Management Studio and SQL Server Data Tools to Create an SSIS Data Profiling Task. Data profiling should be established as a best practice for every data warehouse, BI, and data migration project! AnthonySmoak.com @AnthonySmoak
Views: 2205 Anthony B. Smoak
Cluster Analysis on SAS Enterprise Miner
 
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Please visit http://web.ics.purdue.edu/~jinsuh/analyticspractice-cluster.php for data and sas codes.
Views: 11764 Jinsuh Lee
Data Profiling using SSIS
 
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Learn how to use the "Data Profiling Task" component in SSIS to perform data profiling, and using "Profile Viewer" to view the report
Views: 12355 DataAcademy.in
Twitter Text Mining with Orange 3
 
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A simple example in using Orange 3 to mining texts from Twitter. Notice that collecting data and processing tweet profiles may take 1 minute or more for 500 corpus(es). This video also recorded common mistake in using Twitter widget which is not disabling "Collect result" option if you want a fresh dataset.
Data Exploration
 
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This tutorial aims to give you an overview of data exploration features in Analytics Canvas. Analytics Canvas offers the following data profiling features: • Data Viewer: Data preview of the raw data • Data Profiler: Data structure and selection of statistics • Data Chart: Graphical representation of the time series data.
Views: 273 Analytics Canvas
Data Mining Presentation (Customer Segmentation)
 
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None-- Created using PowToon -- Free sign up at http://www.powtoon.com/ . Make your own animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 2681 Afiq Zaimi
Applying Data Mining Models with  SQL Server Integration Services (SSIS)
 
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SQL Server Integration Services (SSIS) can be used to apply Data Mining predictions. This tutorial demonstrates how to use the SSIS "Data Mining Query" to predictive the risk of having a vehicle using profile information stored in a SQL Server table. I also have a comprehensive 60 minute T-SQL course available at Udemy : https://www.udemy.com/t-sql-for-data-analysts/?couponCode=ANALYTICS50%25OFF
Views: 7908 Steve Fox
Active Learning with SAS®  Text Miner
 
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The video illustrates key enhancements with the 12.1 release of SAS Text Miner.
Views: 16029 SAS Software
SAS® Enterprise Miner™ Software Demo
 
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http://www.sas.com/enterpriseminer SAS Enterprise Miner streamlines data mining to create accurate predictive and descriptive models based on large volumes of enterprisewide data. SAS ENTERPRISE MINER Reveal valuable insights with powerful data mining software. Descriptive and predictive modeling provide insights that drive better decision making. Now you can streamline the data mining process to develop models quickly. Understand key relationships. And find the patterns that matter most. Looking for benefits? How about: * Build better models with the best tools. * Empower business users. * Improve prediction accuracy. Share reliable results. * Automate model deployment and scoring. LEARN MORE ABOUT SAS ENTERPRISE MINER http://www.sas.com/enterpriseminer SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 75,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world The Power to Know.® VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 103188 SAS Software
Chalk Talk Data Profiling
 
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Jim McGann shows how Data Profiling can be used to see what legacy data you have, such as on backup tapes, locate loose email, find ex-employee data and perform a PII audits to make sure all data is secure. For more information http://www.indexengines.com/storage-management/catalyst-data-center-platform/data-profiling-and-file-analysis
Views: 5397 IndexEngines
BADM 9.1 Logistic Regression for Profiling
 
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Interpreting coefficients; Contribution of individual predictors This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining. It is part of a series of 37 videos, all of which are available on YouTube. For more information: http://www.dataminingbook.com https://www.twitter.com/gshmueli https://www.facebook.com/dataminingbook Here is the complete list of the videos: • Welcome to Business Analytics Using Data Mining (BADM) • BADM 1.1: Data Mining Applications • BADM 1.2: Data Mining in a Nutshell • BADM 1.3: The Holdout Set • BADM 2.1: Data Visualization • BADM 2.2: Data Preparation • BADM 3.1: PCA Part 1 • BADM 3.2: PCA Part 2 • BADM 3.3: Dimension Reduction Approaches • BADM 4.1: Linear Regression for Descriptive Modeling Part 1 • BADM 4.2 Linear Regression for Descriptive Modeling Part 2 • BADM 4.3 Linear Regression for Prediction Part 1 • BADM 4.4 Linear Regression for Prediction Part 2 • BADM 5.1 Clustering Examples • BADM 5.2 Hierarchical Clustering Part 1 • BADM 5.3 Hierarchical Clustering Part 2 • BADM 5.4 K-Means Clustering • BADM 6.1 Classification Goals • BADM 6.2 Classification Performance Part 1: The Naive Rule • BADM 6.3 Classification Performance Part 2 • BADM 6.4 Classification Performance Part 3 • BADM 7.1 K-Nearest Neighbors • BADM 7.2 Naive Bayes • BADM 8.1 Classification and Regression Trees Part 1 • BADM 8.2 Classification and Regression Trees Part 2 • BADM 8.3 Classification and Regression Trees Part 3 • BADM 9.1 Logistic Regression for Profiling • BADM 9.2 Logistic Regression for Classification • BADM 10 Multi-Class Classification • BADM 11 Ensembles • BADM 12.1 Association Rules Part 1 • BADM 12.2 Association Rules Part 2 • Neural Networks: Part I • Neural Networks: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 661 Galit Shmueli
SAXually Explicit Images: Data Mining Large Shape Databases
 
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Google TechTalks May 12, 2006 Eamonn Keogh ABSTRACT The problem of indexing large collections of time series and images has received much attention in the last decade, however we argue that there is potentially great untapped utility in data mining such collections. Consider the following two concrete examples of problems in data mining. Motif Discovery (duplication detection): Given a large repository of time series or images, find approximately repeated patterns/images. Discord Discovery: Given a large repository of time series or images, find the most unusual time series/image. As we will show, both these problems have applications in fields as diverse as anthropology, crime prevention, zoology and entertainment. Both problems are trivial to solve given time quadratic in the number of objects, but only a linear time solution is tractable for realistic problems. In this talk we will show how a symbolic representation of the data call SAX (Symbolic Aggregate ApproXimation) allows fast, scalable solutions to these problems. Google engEDU
Views: 4664 GoogleTalksArchive
Safe and Sorry – Terrorism & Mass Surveillance
 
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Sources: Terrorist surveillance program: Original press release: http://1.usa.gov/1p0lZXT Assessment of potential effect of surveillance measures if implemented before 9/11: Interview with FBI director Robert Mueller: http://bit.ly/1MvHNpB FBI investigations of immigrants: "NSEERS effect" report: http://bit.ly/1qU8Wcu Quote on aggressive racial profiling: Article "Are we safer?" by David Cole, Professor of Law at Georgetown University Law Center: http://bit.ly/1Sc8tLo Extent of NSA surveillance: NSA power point slides on collecting buddy lists, obtained by Washington Post: http://wapo.st/1cWi0SM NSA slides on prism data collection, obtained by The Guardian: http://bit.ly/1qmj46r NSA results from mass surveillance vs. target surveillance: Report from the Presidents NSA Review group 2013 (recommending to stop mass data mining because of lack of results): http://1.usa.gov/1bK0q7x Article from ProPublica: http://bit.ly/1PAusfR Analysis from the New America Foundation: http://bit.ly/1SSq8ea Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World by Bruce Schneier Surveillance program didn`t stop any major attacks: Full video of court hearing with NSA director Keith B. Alexander on surveillance: http://cs.pn/1Yv1G0N Official report on results of phone surveillance policy: http://1.usa.gov/1bK0q7x Article on debunked claims: http://bit.ly/1p0n2ae Official judge ruling on matter points to no evidence: https://www.propublica.org/documents/item/902454-judge-leon-ruling#document/p62 Report by the legal affairs and human rights committee of the parliamentary assembly of the Council of Europe: http://bit.ly/1qr9aXC Boston marathon bomber was known to FBI: Official press release: http://1.usa.gov/1Vrw4vI FBI asked Apple for help: Official court order: http://bit.ly/24auFf6 Apple`s refusal to crack iPhone: Official public statement: http://apple.co/1Lt7ReW Objections against FBI demands from cryptographers: Brad Smith keynote at the RSA information security conference: http://bit.ly/1Vrwd1Y (especially relevant from minute 7 on) Statement by Information Technology Industry Council: http://bit.ly/1Q9cg7N Amicus briefs supporting Apple: http://apple.co/1OSBypU FBI changing their story about needing Apple`s help: Initial article on Washington Post: http://wapo.st/1KqHIT7 Initial story on Reutersblog: http://reut.rs/1SCl73o Update on Reuters: http://reut.rs/1NdTJae Article on ACLU about possible work-around: http://bit.ly/1OZ2nZL Blogpost on another possible workaround: http://bit.ly/1Vrwv98 NSA can turn on iPhone remotely: BBC interview with Edward Snowden: http://bit.ly/1Nab09Q Article on Wired: http://bit.ly/1hvZMNn Abuse of anti-terrorism laws: Proof of Patriot Act laws used for investigating other crimes, especially drugs: http://bit.ly/1LXBu9X „Sneak and Peak“ report: http://bit.ly/1RVGhgM Enforcement of French anti-terrorism laws: Detailed explanation of new powers given by extended laws: http://bit.ly/1OYBpSl Original law text (in french): http://bit.ly/1qraiKQ Abuse of french anti-terrorism laws: Human rights watch reports cases: http://bit.ly/1SZmwpH Climate change protesters placed under house arrest: http://reut.rs/20DYZfa Censorship in Hungary, Poland and Spain: http://bit.ly/20DZ3eS http://bit.ly/1Qgc7lX http://bit.ly/1WtmIyv http://bit.ly/1MvJ8N7 Jail time for government critics in Turkey: http://bit.ly/1oXBctf Effects of surveillance on our society: List of issues of power abuse since 9/11 by American Civil liberties union: http://bit.ly/1U6Rux4 General overview over the topic: http://bit.ly/1Pyj8uR http://bit.ly/1RVH2GF http://bit.ly/MZe4qY Safe and Sorry– Terrorism & Mass Surveillance Help us caption & translate this video! http://www.youtube.com/timedtext_cs_panel?c=UCsXVk37bltHxD1rDPwtNM8Q&tab=2
Targeting crimes and criminals through data, Dr Rick Adderley
 
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The Society of Data Miners, in association with the Alan Turing Institute, is delighted to announce the second in a series of practitioner seminars. This talk will discuss the challenges of mining Police data to provide operational intelligence. Rick will introduce the data and systems involved in day-to-day reporting, resource tasking and arresting offenders, including the issues of linking data across systems and the challenges of extracting useful information from free text. Digging into more advanced analytics, Rick will discuss criminal network analysis or CNA, an important tool in crime prevention and detection, and the differences between analysing overt networks (SNA) and covert networks (CNA). Rick will describe how supervised and unsupervised learning methods have been used in the identification of prolific and priority offenders, and how the results are used to solve crimes and target offenders, and to use resources effectively. Finally Rick will describe the EU-funded FP7 project Valcri (www.valcri.org), and its task to provide a Police data set that is suitable for release into the research community. Rick Adderley Bio: Rick is a retired Police Officer having served for 32 years in an operational capacity. His legacy to the Service is an intelligence product which was developed for the West Midlands region and is now used by all UK Police Forces; he specialises in profiling criminal activity. Rick retired in 2003 and started his data mining company, A E Solutions, focusing within the UK Emergency Services arena. Rick is also a director of the Society of Data Miners.
Data Science for Consultants - Data mining with Tableau Part 2
 
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This video expands the discussion of A-B analysis in Part 1 and illustrates an easy way to perform cluster or bin analysis using Tableau.
Views: 110 Rajeev Sinha
Basic SQL Queries - Data Analysis and Tools (DAT) Msc. I.T Part-I
 
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In this video we will Look at the Basic SQL Queries used in DAT practical and how will learn to execute those Queries in Linux. Do Subscribe.
Views: 209 HashTech Coders
SQL Server 2005 Data Mining Forecasting in Excel
 
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SQL Server 2005 Data Mining Forecasting in Excel
Views: 6896 GarryEdser
SAS Visual Data Mining and Machine Learning
 
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http://www.sas.com/vdmml Boost analytical productivity and solve your most complex problems faster with a single, integrated in-memory environment that's both open and scalable. SAS VISUAL DATA MINING AND MACHINE LEARNING SAS Visual Data Mining and Machine Learning supports the end-to-end data mining and machine-learning process with a comprehensive, visual (and programming) interface that handles all tasks in the analytical life cycle. It suits a variety of users and there is no application switching. From data management to model development and deployment, everyone works in the same, integrated environment. http://www.sas.com/vdmml SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 5850 SAS Software
SAS OnDemand for Academics:  Profile Creation  (Student Edition)
 
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http://www.sas.com/en_us/industry/higher-education/on-demand-for-academics.html Marshall and Rich walk you through how to create a SAS OnDemand for Academics profile. SAS OnDEMAND FOR ACADEMICS Get free access to powerful SAS software for statistical analysis, data mining and forecasting. Point-and-click functionality means there's no need to program. Like to program? You can do that, too. Either way, you'll advance your analytical skills, which will help secure your future. LEARN MORE AND ACCESS SAS OnDEMAND FOR ACADEMICS http://www.sas.com/en_us/industry/higher-education/on-demand-for-academics.html SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 1111 SAS Software
Data Profiling and Cleansing with DataCleaner
 
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Take a quick tour of DataCleaner - the premier commercial open source data quality solution. This video provides an overview of the applications user interface and a few features related to data profiling and cleansing. Be sure to visit www.datacleaner.org for more information.
Views: 5327 DataCleaner
BADM 3.1: PCA Part 1
 
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Principal Components Analysis (PCA) - Part 1 This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining. It is part of a series of 37 videos, all of which are available on YouTube. For more information: http://www.dataminingbook.com https://www.twitter.com/gshmueli https://www.facebook.com/dataminingbook Here is the complete list of the videos: • Welcome to Business Analytics Using Data Mining (BADM) • BADM 1.1: Data Mining Applications • BADM 1.2: Data Mining in a Nutshell • BADM 1.3: The Holdout Set • BADM 2.1: Data Visualization • BADM 2.2: Data Preparation • BADM 3.1: PCA Part 1 • BADM 3.2: PCA Part 2 • BADM 3.3: Dimension Reduction Approaches • BADM 4.1: Linear Regression for Descriptive Modeling Part 1 • BADM 4.2 Linear Regression for Descriptive Modeling Part 2 • BADM 4.3 Linear Regression for Prediction Part 1 • BADM 4.4 Linear Regression for Prediction Part 2 • BADM 5.1 Clustering Examples • BADM 5.2 Hierarchical Clustering Part 1 • BADM 5.3 Hierarchical Clustering Part 2 • BADM 5.4 K-Means Clustering • BADM 6.1 Classification Goals • BADM 6.2 Classification Performance Part 1: The Naive Rule • BADM 6.3 Classification Performance Part 2 • BADM 6.4 Classification Performance Part 3 • BADM 7.1 K-Nearest Neighbors • BADM 7.2 Naive Bayes • BADM 8.1 Classification and Regression Trees Part 1 • BADM 8.2 Classification and Regression Trees Part 2 • BADM 8.3 Classification and Regression Trees Part 3 • BADM 9.1 Logistic Regression for Profiling • BADM 9.2 Logistic Regression for Classification • BADM 10 Multi-Class Classification • BADM 11 Ensembles • BADM 12.1 Association Rules Part 1 • BADM 12.2 Association Rules Part 2 • Neural Networks: Part I • Neural Nets: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 1089 Galit Shmueli
Basic Data Analysis with Java : Business Intelligence | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2w2lQqE]. The aim of this video is to deal with Business Intelligence. It will use Apache POI for creating and reading spreadsheets, as well as show what users will do in MS Excel o Understand why as a data analyst, you need to save time using MS Excel o Perform some reads and writes of existing MS Excel spreadsheets For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 3003 Packt Video
Data Mining 7 concepts demonstrated in SAS Eminer
 
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7 concepts discussed in the video: 1) Know your data (has two parts in video- Data Visualization and Descriptive statistical analysis of data) 2) Partitioning of data 3) Selection of Important variables 4) Handling overfitting and underfitting in decision trees 5) Goodness of split in a decision tree 6) Handling weights in the neural network 7) Evaluation of models
Views: 42 shangruff raina
3 - ETL Tutorial | Extract Transform and Load
 
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This video aims to provide an overview of #ETL (Extract Load Transformation ) process and covers: #extraction Process and its Strategies Transformation and various tasks performed Loading Process and its Strategies ETL tools and its features. ETL Tools: Talend Open Studio, Jaspersoft ETL, Ab initio, Informatica, Datastage, Clover ETL, Pentaho ETL, Kettle ETL Tools Features: Source and Target Data System Connectivity Scalability and Performance Easy Transformation connectors Data Profiling Data Cleaning and Quality Easy integration with Web services Logging and Exception Handling Robust Administration features Efficient Batch and Real time processing For more details visit: http://www.vikramtakkar.com/2015/10/what-is-etl-extract-transformation-and.html Datawarehouse Playlist: https://www.youtube.com/playlist?list=PLJ4bGndMaa8FV7nrvKXeHCLRMmIXVCyOG
Views: 115894 Vikram Takkar
Feature Engineering in SAS Visual Data Mining & Machine Learning
 
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http://support.sas.com/software/products/visual-data-mining-machine-learning/index.html Presenter: Radhikha Myneni Radhikha Myneni discusses some feature engineering techniques available in SAS Visual Data Mining and Machine Learning 8.3. SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 1103 SAS Software
Mining Structured and Unstructured Data
 
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Oracle Advanced Analytics (OAA) Database Option leverages Oracle Text, a free feature of the Oracle Database, to pre-process (tokenize) unstructured data for ingestion by the OAA data mining algorithms. By moving, parallelized implementations of machine learning algorithms inside the Oracle Database, data movement is eliminated and we can leverage other strengths of the Database such as Oracle Text (not to mention security, scalability, auditing, encryption, back up, high availability, geospatial data, etc.. This YouTube video presents an overview of the capabilities for combing and data mining structured and unstructured data, includes several brief demonstrations and instructions on how to get started--either on premise or on the Oracle Cloud.
Views: 2681 Charlie Berger