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Search results “Advances in database and data mining 2013”
In-Database Data Mining for Retail Market Basket Analysis Using Oracle Advanced Analytics
 
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Market Basket Analysis presentation and demo using Oracle Advanced Analytics
Views: 10244 Charles Berger
Data Mining in SQL Server Analysis Services
 
01:29:25
Presenter: Brian Knight
Views: 96642 PASStv
In-Database Data Mining Using Oracle Advanced Analytics for Classificaton using Insurance Use Case
 
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In-Database Data Mining Using Oracle Advanced Analytics Option for Classificaton using Insurance Use Case
Views: 4851 Charles Berger
Mining Association Rules in Large Database
 
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Basic concepts of Association Rules and Stretagies
Views: 975 Dr.Anamika Bhargava
Introduction to Database Management Systems 1: Fundamental Concepts
 
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This is the first chapter in the web lecture series of Prof. dr. Bart Baesens: Introduction to Database Management Systems. Prof. dr. Bart Baesens holds a PhD in Applied Economic Sciences from KU Leuven University (Belgium). He is currently an associate professor at KU Leuven, and a guest lecturer at the University of Southampton (United Kingdom). He has done extensive research on data mining and its applications. For more information, visit http://www.dataminingapps.com In this lecture, the fundamental concepts behind databases, database technology, database management systems and data models are explained. Discussed topics entail: applications, definitions, file based vs. databased data management approaches, the elements of database systems and the advantages of database design.
Views: 282704 Bart Baesens
Dynamic Query Forms for Database Queries Video
 
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Data Mining Ieee Project 2013
Excel 2013 - Data Mining - Analyze Key Influencers
 
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Apresentação de como usar a ferramenta de mineração de dados do Excel 2013. Analise de Principais Influenciadores Para uma tabela com dados de compra de bicicletas, este video mostra como usar a ferramenta de mineração de dados para descobrir principais perfis que compraram mediante variaveis declaradas ( estado civil, genero, região, etc). Para mais acesse: http://excelb2b.com/
Views: 1851 ExcelB2B
Database Mining
 
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Views: 160 Sean Patrick Altea
Data Mining with Weka (5.1: The data mining process)
 
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Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 1: The data mining process http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/5DW24X https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 15015 WekaMOOC
Forecast the Price of Gold with Excel and SQL Server - Data Mining Tutorial
 
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Learn about data mining with SQL Server 2012 Analysis Services and Excel 2013, using historical gold pricing data, to predict future prices. To follow this tutorial, you should have SSAS and the Data Mining Add-in for Excel.
Views: 6153 Edward Kench
Big Data: Mining Football Statistics
 
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Data Mining Final Project for Big Data INSY 4970 at Auburn University
Views: 30718 wwl0002
Data Mining  Association Rule - Basic Concepts
 
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short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
Excel Data Analysis: Sort, Filter, PivotTable, Formulas (25 Examples): HCC Professional Day 2012
 
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Download workbook: http://people.highline.edu/mgirvin/ExcelIsFun.htm Learn the basics of Data Analysis at Highline Community College Professional Development Day 2012: Topics in Video: 1. What is Data Analysis? ( 00:53 min mark) 2. How Data Must Be Setup ( 02:53 min mark) Sort: 3. Sort with 1 criteria ( 04:35 min mark) 4. Sort with 2 criteria or more ( 06:27 min mark) 5. Sort by color ( 10:01 min mark) Filter: 6. Filter with 1 criteria ( 11:26 min mark) 7. Filter with 2 criteria or more ( 15:14 min mark) 8. Filter by color ( 16:28 min mark) 9. Filter Text, Numbers, Dates ( 16:50 min mark) 10. Filter by Partial Text ( 20:16 min mark) Pivot Tables: 11. What is a PivotTable? ( 21:05 min mark) 12. Easy 3 step method, Cross Tabulation ( 23:07 min mark) 13. Change the calculation ( 26:52 min mark) 14. More than one calculation ( 28:45 min mark) 15. Value Field Settings (32:36 min mark) 16. Grouping Numbers ( 33:24 min mark) 17. Filter in a Pivot Table ( 35:45 min mark) 18. Slicers ( 37:09 min mark) Charts: 19. Column Charts from Pivot Tables ( 38:37 min mark) Formulas: 20. SUMIFS ( 42:17 min mark) 21. Data Analysis Formula or PivotTables? ( 45:11 min mark) 22. COUNTIF ( 46:12 min mark) 23. Formula to Compare Two Lists: ISNA and MATCH functions ( 47:00 min mark) Getting Data Into Excel 24. Import from CSV file ( 51:21 min mark) 25. Import from Access ( 54:00 min mark) Highline Community College Professional Development Day 2012 Buy excelisfun products: https://teespring.com/stores/excelisfun-store
Views: 1448450 ExcelIsFun
Sales Forecasting with Excel and the SQL Server 2012 Data Mining Add-in Tutorial
 
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Use the Excel Data Mining add-in for SQL 2012 Analysis Services. See how simple it is to build a predictive model that forecasts sales or other values based on historical data.
Views: 12421 Edward Kench
DATA MINING
 
35:03
Views: 36491 iimtnew
A Data Mining Project -- Discovering association rules using the Apriori algorithm
 
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Graduate student Jing discusses her data mining term project which uses the Apriori algorithm (market basket analysis) to mine association rules from a set of database transactions.
Views: 14433 CSDepartment St. Joes
Data Mining using the Excel Data Mining Addin
 
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The Excel Data Mining Addin can be used to build predictive models such as Decisions Trees within Excel. The Excel Data Mining Addin sends data to SQL Server Analysis Services (SSAS) where the models are built. The completed model is then rendered within Excel. 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: 71977 Steve Fox
Advanced Analytics in Excel 2013 [AV-302]
 
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Speaker: Dejan Sarka Excel is “the” analytical tool in Microsoft’s suite for advanced analysts. Of course, you know that Excel 2013 includes PowerPivot and Power View add-ins out of the box. You also may have heard that you can use big data and Azure DataMarket data in Excel and that you can mash up data from different sources. However, you probably don’t know how to use PowerPivot data for data mining, how to combine big data with PowerPivot data, how to use data mining models in PowerPivot, or how to mash up data when you don’t have common identification. This session is not about introducing the cool new features; instead, it will focus on the most advanced part of Excel analytics: data mining with Excel.
Views: 271 PASStv
Data Mining   KDD Process
 
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KDD - knowledge discovery in Database. short introduction on Data cleaning,Data integration, Data selection,Data mining,pattern evaluation and knowledge representation.
Advanced Keyword SEO Management Tool: Web-based Software for Cloud-based Data Mining
 
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Back Azimuth http://www.back-azimuth.com now offers its VOCDMS (Voice of the Consumer Data Management System) as part of its advanced keyword management services. Back Azimuth's VOCDMS is a cloud-based data-mining platform that aggregates keyword and social media data into a single web-based application enabling marketers to better understand their prospects while identifying performance gaps that are overlooked by other tools. Search Marketers have dreamed of a single database for managing all of their keyword data -- paid, organic and site search. It is now possible. Using the scale of the cloud and Back Azimuth's VOCDMS database solution, we are able to combine the data and then mine it in hundreds of different ways to help marketers find opportunities which were never possible before. In this video, Bill Hunt, founder and president, Back Azimuth, discusses his company's consulting for a huge online retailer, Yummie Tummie. 0:09 Bill Hunt, founder and president, Back Azimuth Consulting, discusses the ideal target customer who has more than a million keywords that can benefit from Back Azimuth advanced keyword management software or VOCD MS. These are large travel, computer, etail, and retail companies that get a large number of keyword queries that would bring people to their products. 0:22 Bill Hunt speaks to a group of digital marketers at SES. Bill asks the audience about their client-base and how many keywords they monitor and where the opportunities that are not being taken advantage of are. 0:47 Bill expands on the challenges faced by companies that have a million or more keywords and how there are hundreds of thousands of ways in which consumers will look for their product and each of these keywords has a different value. Some of the keyword queries will convert, some won't. The best thing VOCDMS can do for companies is to help them understand how to find those keywords that will convert and generate the most value for them. 01:11 Yummie Tummie (www.yummielife.com/) case study -- Bill begins by presenting a company product which could be referenced using different descriptions. These product names included girdle, shapewear, shaping underwear, shapewear intimates, and Spanx. The age differential is significant, according to Bill, because anyone over the age of 30 would describe the product as a girdle and anyone under the age of 30 would call it spanx or shapewear. The brand, in this case, wants to call it, Shapewear Intimate. Bill describes how the VOCDMS keyword management software discovered that 75% of the entire search related to shapewear came down to a simple formula: clothing type + shapewear. Bill says you have to match the interest and intent to what your prospective customer wants. 04:15 Bill describes how his company revamped Yummie Tummie's keyword research by rebuilding the company's entire website creating a whole new site taxonomy which translates into an experience that provides the user with progressive levels of information about shapewear. 07:14 Bill discusses how the VOCDMS solution mines big keyword data, by managing it in a single database with filters that look for the needle in a haystack. VOCDMS is used by large travel companies, electronic manufacturers, similar to an HP or Dell, that have large sets of data. Travelocity is a client that has tons of keyword volume from different destinations. It takes advantage of VOCDMS by targeting their product to the sets of keywords that their customer base uses for searching. 08:37 To learn more about how your company can benefit from advanced keyword management and VOCDMS, please visit: http://back-azimuth.com/contact Phone: 866-599-8225 If you are interested in a pilot project and want to see how Back Azimuth Consulting's Voice of the Consumer Data Management System can work for you, all that is required is access to your main data such as web analytics, paid search data, and any kind of segmentation or keyword work that you have already done previously.
Views: 790 Bill Hunt
Data Mining-Structured Data, Unstructured data and Information Retrieval
 
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Structured Data, Unstructured data and Information Retrieval
Views: 1251 John Paul
Types of databases
 
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This video is telling about different types of databases. Next video will be about different types of attributes. #datamining #database #types
Views: 210 yachana bhawsar
Bioinformatics part 2 Databases (protein and nucleotide)
 
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For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html This video is about bioinformatics databases like NCBI, ENSEMBL, ClustalW, Swisprot, SIB, DDBJ, EMBL, PDB, CATH, SCOPE etc. Bioinformatics Listeni/ˌbaɪ.oʊˌɪnfərˈmætɪks/ is an interdisciplinary field that develops and improves on methods for storing, retrieving, organizing and analyzing biological data. A major activity in bioinformatics is to develop software tools to generate useful biological knowledge. Bioinformatics uses many areas of computer science, mathematics and engineering to process biological data. Complex machines are used to read in biological data at a much faster rate than before. Databases and information systems are used to store and organize biological data. Analyzing biological data may involve algorithms in artificial intelligence, soft computing, data mining, image processing, and simulation. The algorithms in turn depend on theoretical foundations such as discrete mathematics, control theory, system theory, information theory, and statistics. Commonly used software tools and technologies in the field include Java, C#, XML, Perl, C, C++, Python, R, SQL, CUDA, MATLAB, and spreadsheet applications. In order to study how normal cellular activities are altered in different disease states, the biological data must be combined to form a comprehensive picture of these activities. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This includes nucleotide and amino acid sequences, protein domains, and protein structures.[9] The actual process of analyzing and interpreting data is referred to as computational biology. Important sub-disciplines within bioinformatics and computational biology include: the development and implementation of tools that enable efficient access to, use and management of, various types of information. the development of new algorithms (mathematical formulas) and statistics with which to assess relationships among members of large data sets. For example, methods to locate a gene within a sequence, predict protein structure and/or function, and cluster protein sequences into families of related sequences. The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches, however, is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: pattern recognition, data mining, machine learning algorithms, and visualization. Major research efforts in the field include sequence alignment, gene finding, genome assembly, drug design, drug discovery, protein structure alignment, protein structure prediction, prediction of gene expression and protein--protein interactions, genome-wide association studies, and the modeling of evolution. Bioinformatics now entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data. Over the past few decades rapid developments in genomic and other molecular research technologies and developments in information technologies have combined to produce a tremendous amount of information related to molecular biology. Bioinformatics is the name given to these mathematical and computing approaches used to glean understanding of biological processes. Source of the article published in description is Wikipedia. I am sharing their material. Copyright by original content developers of Wikipedia. Link- http://en.wikipedia.org/wiki/Main_Page
Views: 79079 Shomu's Biology
RevenueRadar Data Mining Software
 
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RevenueRadar is a new database-mining software integrated with DealerSocket CRM . Watch to see how this powerful tool will help dealers sell more vehicles, close more ROs into the service drive, and increase customer satisfaction.
Views: 4113 DealerSocket
Database Lesson #8 of 8 - Big Data, Data Warehouses, and Business Intelligence Systems
 
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Dr. Soper gives a lecture on big data, data warehouses, and business intelligence systems. Topics covered include big data, the NoSQL movement, structured storage, the MapReduce process, the Apache Cassandra data model, data warehouse concepts, multidimensional databases, business intelligence (BI) concepts, and data mining,
Views: 74212 Dr. Daniel Soper
Pokermetrics Advanced Database Analysis
 
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For more information go to: http://tinyurl.com/acf5v2x
Views: 2152 AJacksonPoker
MS SQL Server Data mining- decision tree
 
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A quick example on how to do data mining using decision tree algorithm within MS SQL Server . We analyze patterns in data that is heavily skewed for specific cases so that we can validate the model.
Views: 4400 Jayanth Kurup
Data Mining Full Bangla Tutorial 2017 || Data Entry Lesson- 4||
 
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Data Entry And Web Research Bangla Tutorial (2017) this tutorial i am going to teach you about data entry and web research.this is a tutorial for begginers data entry and web research have a great place in freelancing marketplaces.so after learning you guys can easily start doing this type of jobs ## Outsourcing Working Tips 2017 https://www.youtube.com/playlist?list=PLCTj6SR5wKSJqRnUVx7CSi1Cdc2KdD-F0 ## Data Entry Job A to Z Tutorial 2017 https://www.youtube.com/playlist?list=PLCTj6SR5wKSJNMpoVIxEcQLZscDRwkI3l ## Advance Internet Tricks https://www.youtube.com/playlist?list=PLCTj6SR5wKSJkKunzQUi8xTls2X5M9yZH ## Basic SEO Full Bangla Tutorial 2017 https://www.youtube.com/playlist?list=PLCTj6SR5wKSLjfUstoM8MDpc_MEvU24Ow ## SEO Full Bangla Tutorial 2017 https://www.youtube.com/playlist?list=PLCTj6SR5wKSKYTnjGvxvry98WhlyzecJb ## Basic To Advance Computer Operating And Internet Browsing Full Tutorial 2017 https://www.youtube.com/playlist?list=PLCTj6SR5wKSKbFCaphVP_Bij9JyOKz8-- In This Playlist 1. Simple Data Entry Job Full Bangla Tutorial 2017 || Data Entry Lesson- 1 https://youtu.be/9lhzyYRBJVg 2. Data Collection Full Bangla Tutorial (2017) || Data Entry Lesson- 2 https://youtu.be/eI2ktApDYew 3. BPO Data Entry Full Bangla Tutorial 2017 || Data Entry Lesson- 3 https://youtu.be/1nbtMVJpi38 4. Data Mining Full Bangla Tutorial 2017 || Data Entry Lesson- 4 https://youtu.be/P-WvkOMiLGA 5. Data Scraping Full Bangla Tutorial 2017 || Data Entry Lesson- 5 https://youtu.be/LpZ9XU1RogQ 6. Data Processing Full Bangla Tutorial 2017 || Data Entry Lesson- 6 7. Data Research Full Bangla Tutorial 2017 || Data Entry Lesson- 7 https://youtu.be/xCB4STWNTdg 8. Data Entry For Ecommerce Site Full Bangla Tutorial 2017 || Data Entry Lesson- 8 https://youtu.be/f8qb43o2wFA 9. Magento data entry Full Bangla Tutorial 2017 || Data Entry Lesson- 9 https://youtu.be/IWIB_GYqFYc 10. ERP Software Full Bangla Tutorial 2017 || Data Entry Lesson- 10 https://youtu.be/Wn3RpvElMFU 11. Data Convert Full Bangla Tutorial 2017 || Data Entry Lesson- 11 https://youtu.be/tYVjdwTOwGk #web research bangla tutorial #data entry bangla tutrorial #how to start doing data entry bangla tutorial #how to do data entry job bangla tutorial #Data Entry And Web Research Bangla Tutorial (2017) Data Entry And Web Research Bangla Tutorial ডাটা এন্টি জব, ডাটা এন্টি শিখবো, ডাটা এন্ট্রি বাংলা টিউটোরিয়াল,
Views: 1110 Silent Expo
Data Mining in Excel
 
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How to implement data mining in excel? For more information please visit https://www.koenig-solutions.com/microsoft-training-certification-courses.aspx
Views: 614 Koenig Solutions
Overview presentation and demonstration of Oracle Advanced Analytics Option
 
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Overview presentation & demonstration of Oracle Advanced Analytics Option.
Views: 7701 Charles Berger
Oracle Big Data Analytics Demo mining remote sensor data from HVACs for better customer service
 
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Oracle Big Data Analytics Demo mining remote sensor data from HVACs for better customer service. Oracle Advanced Analytics's Data Mining GUI is used to mine data from remote devices to find problems and improve product customer service. In the scenario, Oracle's Big Data Appliance is positioned to be the initial data collector/aggregator and then the data that is loaded into the Oracle Database. We perform our data mining/predictive analytics on the data while it resides inside the Oracle Database thereby transforming the Database into an Analytical Database.
Views: 2606 Charles Berger
Grace System
 
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Grace Systems - uses a radical new architecture, where Data Mining, Data Science and Data Analytics are all core elements of one intelligent system. - Data Mining software discovers patterns in large data sets with methods that combine artificial intelligence, machine learning, statistics and innovative database systems. - is based on Data Science principles and highly advanced algorithms to extract knowledge and insights from data. - enables the end user to create Analytics of meaningful patterns in data. This means that advanced algorithms are applicable within different market segments and disciplines for example healthcare, finance and logistics. Onze aanpak op vraagstukken in de Zorg: ---------------------------------------------------------------------- Predictive Analytics: Optimalisatievraagstukken in de gezondheidszorg door het doorgronden van kosten en budgetten in relatie tot zorgproducten en activiteiten. We maken hierbij gebruik van data van specialisten, verzekeraars, NZA en RIVM. VIPP: Data validatie en profiling tot op registratiebron van alle benodigde data in transmurale informatieketens waardoor onderbouwing van VIPP aansluiting mogelijk wordt. AVG: Hoe op data niveau de impact van privacy en security wetgeving tot op patient record wordt aangetoond. (DPIA Data protection impact assessment) Implementatie: Migratie en implementatie is automatiseren van zorg; data profiling, data kwaliteit en data migratie is uiteindelijk de succesfactor in elk EPD traject. Innovatie: Data Driven Healthcare zal dé factor zijn voor vernieuwing in de zorg, dit is alleen mogelijk met toepassing van Data Science methoden en technieken.
Views: 179 Grace System
Вебинар «Практические задачи Data Mining: проблемы и решения»
 
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Вебинар посвящен современным подходам и стратегиям применения методов Data Mining для решения актуальных задач в различных областях: бизнесе/маркетинге, финансах, банковской области, телекоммуникациях и др. http://www.statsoft.ru/products/STATISTICA_Data_Miner/
Views: 1984 StatSoftRussia
Microsoft Excel Complete Training Video :  DATA TAB in Ms-excel in Hindi
 
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इस विडियो सीरीज मे आप सीखेंगे MS -EXCEL के सभी TABS के सभी OPTIONS के बारे में , आगे आने वाली वीडियो में और भी ADVANCE और IMPORTANT जानकारी , MISS न करें और अभी सब्सक्राइब करें , सब्सक्राइब करने के लिए नीचे दिए लिंक पे क्लिक करें https://www.youtube.com/channel/UCtX_kEx0pkQR49Zedu1CeKA?sub_confirmation=1 We are committed to provide best and new videos every week , subscribe to make sure not to miss videos , just click and subscribe using the above link. To download free : E-BOOK + SPOKEN ENGLISH CRASH COURSE OF 20 DAYS , CLICK BELOW http://ishtworld.com/CompleteTraining http://smartosystems.in/CompleteTraining To know about Author click on below link http://ishtworld.com/authorsinfo http://smartosystems.in/authorsinfo To know more complete trainings http://ishtworld.com/shop/ Join our Facebook page https://www.facebook.com/smartosystems Join our YouTube channel https://www.youtube.com/c/ISHTAIMS-SmartoSystemS To subscribe our channel https://www.youtube.com/channel/UCtX_kEx0pkQR49Zedu1CeKA?sub_confirmation=1 Join our Google + Circle https://plus.google.com/+ISHTAIMS-SmartoSystemS ADVANCE Microsoft Excel Microsoft Excel is great spread sheet based software S.NO PARTICULARS 1 INTRO TO MIRCROSOFT EXCEL 2 EXCEL SCREEN PARTS 3 LET'S START HOME TAB 4 SELECTION IN MICROSOFT EXCEL 5 FONT GROUP IN MICROSOFT EXCEL 6 ALIGNMENT GROUP 7 NUMBER GROUP 8 STYLES 9 TABLE TOOLS ,DESIGN TAB 10 CELL STYLES 11 CELLS GROUP 12 EDITING SECTION 13 FORMULAS INTRODUCTION IN EXCEL 14 USING AUTOSUM 15 USING INSERT FUNCTION 16 AVG , MIN , MAX , IF FUNCTION 17 FILL OPTIONS IN EXCEL 18 FILL OPTIONS WITH DATES , NUMBER 19 SORT AND FILTER IN EXCEL 20 FIND AND SELECT IN EXCEL S.NO PARTICULARS 1 USING AUTOSUM 2 USING INSERT FUNCTION 3 AVG , MIN , MAX , IF FUNCTION 4 BASICS OF FUNCTIONS AND FORMULAS 5 USE OF INSERT FUNCTION 6 HOW TO TYPE FORMULAS 7 BASICS OF FORMULAS 8 TOTAL ON STATUS BAR FINANCIAL FORMULAS 9 RATE FUNCTION (USING LOAN INSTALLMENTS EXAMPLE) 10 PMT FUNCTION (USING MONTHLY REPAYMENT EXAMPLE) 11 FUTURE VALUE FUNCTION (USING DEPOSIT AMOUNT EXAMPLE) 12 NPER FUNCTION ( USING LOAN TIME CALCULATION EXAMPLE) 13 DB FUNCTION (DEPRICIATION CALCULATION EXAMPLE) LOGICAL FORMULAS 14 LOGICAL IF (USING RESULT EXAMPLE) 15 LOGICAL AND (USING AGE EXAMPLE) 16 LOGICAL IF & AND (USING AGE EXAMPLE) 17 LOGICAL COMBINED IF & AND (USING AGE EXAMPLE) 18 LOGICAL OR (USING TEST EXAMPLE) 19 LOGICAL IF & OR (USING AGE EXAMPLE) 20 LOGICAL COMBINED IF & OR (USING AGE EXAMPLE) TEXT FORMULAS 21 TRIM FORMULA(REMOVE EXTRA SPACES EXAMPLE) 22 LEFT FORMULA(USING IN EXTRACT LEFT CHARACTERS) 23 RIGHT FORMULA(USING IN EXTRACT RIGHT CHARACTERS) 24 REPLACE FORMULA( USING CHARACTER REMOVING EXAMPLE) 25 MID FORMULA (WITH CHARACTER EXTRACTOR EXAMPLE) 26 CONCATENATE FUNCTION( WITH TEXT JOINING EXAMPLE) 27 UPPER FUNCTION ( USE TO UPPERCASE CHARACTERS) 28 UPPER AND CONCATENATE 29 FIND FUNCTION 30 LEFT AND FIND FUNCTION( USE TO SEPRATE NAMES) DATE FORMULAS 31 HOW TO SET DATE FORMATS 32 DATE FUNCTION 33 DAYS 360 (USING LIBRARY RECORD EXAMPLE) 34 NOW FUNCTION 35 DAY FUNCTION 36 MONTH FUNCTION 37 YEAR FUNCTION 38 HOUR FUNCTION 39 MINUTE FUNCTION 40 NETWORKDAYS (TO COUNT NETWORKING DAYS ) LOOKUP FORMULAS 41 VLOOKUP FORMULA ( WITH EXACT MATCH EXAMPLE) 42 VLOOKUP FORMULA ( WITH APPROXIMATE EXAMPLE) 43 VLOOKUP RANGE ACCEPTANCE 44 HLOOKUP FORMULA 45 MATCH FORMULA 46 INDEX FORMULA 47 INDEX AND MATCH FORMULA TOGETHER 48 TRANSPOSE (ROWS TO COLUMNS) MATHS AND TRIGNOMATERY 49 ABS FUNCTION 50 . 51 POWER FUNCTION 52 FACT FUNCTION 53 RAND FUNCTION 54 RAND WITH RANGE 55 RANBETWEEN 56 SUMIF(USING NUMBER AS CONDITION) 57 SUMIF(USING CHARACTER AS CONDTION) MORE FUNCTIONS 1 DEFINED NAMES AND USING NAME MANAGER 2 FORMULA AUDITING 3 CALCULATON GROUP S.NO PARTICULARS 1 GET EXTERNAL DATA GROUP 2 FROM ACCESS 3 FROM WEB 4 CONNECTIONS GROUP 5 SORT AND FILTER-SORT OPTIONS 6 SORT AND FILTER-FILTER OPTIONS 7 ADVANCE FILTER OPTIONS 8 DATA TOOLS 9 OUTLINE GROUP INSERT TAB S.NO PARTICULARS 1 PIVOT TABLES AND ITS OPTIONS 2 TABLE AND ITS OPTIONS 3 CHARTS AND ITS OPTIONS 4 SPARKLINES AND ITS OPTIONS 5 FILTER SLICER 6 LINKS 7 TEXT OPTIONS 8 SYMBOLS GROUP PAGELAYOUT TAB S.NO PARTICULARS 1 THEMES GROUP 2 PAGE SETUP GROUP SETTINGS IN EXCEL 3 ORIENTATION 4 SCALE TO FIT 5 SHEET OPTIONS 6 ARRANGE REVIEW TAB S.NO PARTICULARS 1 PROFFING 2 LANGUAGE 3 COMMENT 4 CHANGES GROUP WITH PASSWORD PROTECTION VIEW TAB S.NO PARTICULARS 1 WORKBOOK VIEWS 2 SHOW GROUP 3 ZOOM GROUP 4 WINDOW GROUP 5 MACROS GROUP IN EXCEL FILE TAB S.NO PARTICULARS 1 FILE TAB OPTIONS , CONTAINS , NEW, OPEN ,SAVE AND BASICS FILE TAB COMMANDS Click below given linke to Subscribe https://www.youtube.com/channel/UCtX_kEx0pkQR49Zedu1CeKA?sub_confirmation=1 #ishtworld.com #smartosystems
Views: 109433 SmartO Systems
Oracle Fast Data Mining: Real Time Knowledge Discovery for Predictive Decision Making
 
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Fast Data as a different approach to Big Data for managing large quantities of "in-flight" data that help organizations get a jump on those business-critical decisions. Difference between Big Data and Fast Data is comparable to the amount of time you wait downloading a movie from an online store and playing the dvd instantly. Data Mining as a process to extract info from a data set and transform it into an understandable structure in order to deliver predictive, advanced analytics to enterprises and operational environments. The combination of Fast Data and Data Mining are changing the "Rules"
Views: 885 Nino Guarnacci
Data Mining Applications: from Winemaking to Counterterrorism
 
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Data Mining, from Theory to Practice, Lecture of Prof. Mark Last, Head of the Data Mining and Software Quality Engineering Group, Ben-Gurion University of the Negev, "Data Mining Applications: from Winemaking to Counterterrorism" Data Mining for Business Intelligence - Bridging the Gap Ben-Gurion University of the Negev
Views: 493 BenGurionUniversity
Big Data Analytics - Utilizing in-Database Analytics & Reporting
 
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This demo covers how Alteryx can be utilized at the center of the Teradata Unified Data Architecture to access data sources such as Teradata data warehouse and Aster data warehouse, to take advantage of the in-database analysis to create different reports base on the results.
Views: 2786 Alteryx
Analyzing And Visualizing Data With Excel 2016
 
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In this workshop, get an introduction to the latest analysis and visualization capabilities in Excel 2016. See how to import data from different sources, create mash/ups between data sources, and prepare the data for analysis. After preparing the data, learn about how business calculations - from simple to more advanced - can be expressed using DAX, how the result can be visualized and shared.
Views: 26432 Microsoft Power BI
Tanagra Data Mining
 
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an "open source project" as every researcher can access to the source code, and add his own algorithms, as far as he agrees and conforms to the software distribution license.
Views: 13499 Emmanuel Felipe
Using Data Mining in Forecasting Problems
 
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In this presentation, Analytics 2012 keynote speaker, Tim Rey from Dow Chemical Company, shares methodologies for using data mining to get the most value out of time series data.
Views: 8674 SAS Software
GraphLab: Large-Scale Machine Learning on Graphs (BDT204) | AWS re:Invent 2013
 
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GraphLab is like Hadoop for graphs in that it enables users to easily express and execute machine learning algorithms on massive graphs. In this session, we illustrate how GraphLab leverages Amazon EC2 and advances in graph representation, asynchronous communication, and scheduling to achieve orders-of-magnitude performance gains over systems like Hadoop on real-world data.
Views: 5595 Amazon Web Services
Mining Spatial Data using FDL Query
 
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A Project on Data Mining
Views: 141 Aniel Ronald
Visual Exploration of Market Basket Analysis with JMP
 
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Association rules are a popular data mining technique for exploring relationships in databases. These rules use a variety of algorithms and attempt to identify strong rules or associations among variables. One example is the classic market basket case, which finds that when bread and cheese are purchased, wine is more often purchased. Rules can also easily serve as supervised learning algorithms by directing that one element be a target variable of interest. JMP does not include association rule methods -- but does offer connectivity and flexibility, in addition to great interactive visualization tools. This presentation, by Matthew Flynn, PhD, Marketing Manager at Aetna, demonstrates that strength by connecting JMP to other software tools -- such as SAS® Enterprise Miner™, open-source R, Weka and (now with JMP 11) MATLAB -- to access association rules methods and enliven them by visually exploring the generated rule results in JMP. This presentation was recorded at Discovery Summit 2013 in San Antonio, Texas.
Views: 2626 JMPSoftwareFromSAS
Data mining в реальном проекте
 
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Data mining - классический пример того, когда нас просят из ничего сделать что-то, извлечь скрытый смысл из имеющейся информации. На примерах ситуаций, возникавших в ходе разработки реального проекта, будут рассмотрены проблемы, с которыми мы столкнулись, а также воплощаемые в коде решения, необходимые для создания сбалансированно работающей системы. По материалам конференции .NET разработчиков - http://dotnetconf.ru/Materialy/DataMining
Views: 815 Alexander Byndyu
Introduction to the KNIME data mining system (tutorial)
 
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Tutorial regarding how to build a workflow in the KNIME data mining and predictive analytics system. For more information or to download KNIME, please visit: http://www.knime.org/ Brought to you by Rapid Progress Marketing and Modeling, LLC (RPM Squared) http://www.RPMSquared.com/
Views: 38307 Predictive Analytics
Microsoft Excel Data Manipulation (Excel Ace Tips)
 
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Some 'Simple' but very 'Effective' techniques to manipulate data in Microsoft Excel Spreadsheets........and save lots of Time!!!!
Views: 20715 Excel Ace
Import Data and Analyze with MATLAB
 
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Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip
Views: 324797 APMonitor.com