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Overview of Query Languages | Database Management System
 
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To ask your doubts on this topic and much more, click on this Direct Link: http://www.techtud.com/video-lecture/lecture-query-language IMPORTANT LINKS: 1) Official Website: http://www.techtud.com/ 2) Virtual GATE: http://virtualgate.in/login/index.php Both of the above mentioned platforms are COMPLETELY FREE, so feel free to Explore, Learn, Practice & Share! Our Social Media Links: Facebook Page: https://www.facebook.com/techtuduniversity Facebook Group: https://www.facebook.com/groups/virtualgate Google+ Page: https://plus.google.com/+techtud/posts Last but not the least, SUBSCRIBE our YouTube channel to stay updated about the regularly uploaded new videos.
Views: 21194 Techtud
Introduction to data mining and architecture  in hindi
 
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#datamining #datawarehouse #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 212540 Last moment tuitions
Last Minute Tutorials | Data mining | Introduction | Examples
 
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Please feel free to get in touch with me :) If it helped you, please like my facebook page and don't forget to subscribe to Last Minute Tutorials. Thaaank Youuu. Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ Website: www.lmtutorials.com For any queries or suggestions, kindly mail at: [email protected]
Views: 44619 Last Minute Tutorials
KDD ( knowledge data discovery )  in data mining in hindi
 
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#kdd #datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 71964 Last moment tuitions
Introduction to Datawarehouse in hindi | Data warehouse and data mining Lectures
 
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#datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 284694 Last moment tuitions
Lecture -37 Object Oriented Databases II
 
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Lecture Series on Database Management System by Dr.S.Srinath IIIT Bangalore .For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 23962 nptelhrd
Data Mining Tool: query multiple datasets
 
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Seraching across multiple datasets, in this case mouse and human array data.
Views: 34 QMRIBioinf
K mean clustering algorithm with solve example
 
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#kmean datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 354168 Last moment tuitions
Data mining  harvesting and analytics. ( All you  need to know)
 
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There is a whirlwind of videos and info on this but none that explained it properly to me. I went online and found out everything I needed to know about the data breaches and the implications of those breaches! I provided some links below in case you wish to educate yourself on whats happening with YOUR data! https://www.quora.com/What-is-the-difference-between-data-analytics-and-data-mining-1 https://www.connotate.com/are-you-screen-scraping-or-data-mining/ http://searchdatamanagement.techtarget.com/definition/data-scrubbing What is the difference between data warehousing and data mining? The main difference between data warehousing and data mining is that data warehousing is the process of compiling and organizing data into one common database, whereas data mining is the process of extracting meaningful data from that database.
Views: 50 Elle's place
OLAP vs OLTP in hindi
 
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#olap #oltp #datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 112134 Last moment tuitions
Lecture - 5 Structured Query Language
 
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Lecture Series on Database Management System by Dr.S.Srinath, IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 124501 nptelhrd
Decision Tree with Solved Example in English | DWM | ML | BDA
 
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Take the Full Course of Artificial Intelligence What we Provide 1) 28 Videos (Index is given down) 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in Artificial Intelligence Sample Notes : https://goo.gl/aZtqjh To buy the course click https://goo.gl/H5QdDU if you have any query related to buying the course feel free to email us : [email protected] Other free Courses Available : Python : https://goo.gl/2gftZ3 SQL : https://goo.gl/VXR5GX Arduino : https://goo.gl/fG5eqk Raspberry pie : https://goo.gl/1XMPxt Artificial Intelligence Index 1)Agent and Peas Description 2)Types of agent 3)Learning Agent 4)Breadth first search 5)Depth first search 6)Iterative depth first search 7)Hill climbing 8)Min max 9)Alpha beta pruning 10)A* sums 11)Genetic Algorithm 12)Genetic Algorithm MAXONE Example 13)Propsotional Logic 14)PL to CNF basics 15) First order logic solved Example 16)Resolution tree sum part 1 17)Resolution tree Sum part 2 18)Decision tree( ID3) 19)Expert system 20) WUMPUS World 21)Natural Language Processing 22) Bayesian belief Network toothache and Cavity sum 23) Supervised and Unsupervised Learning 24) Hill Climbing Algorithm 26) Heuristic Function (Block world + 8 puzzle ) 27) Partial Order Planing 28) GBFS Solved Example
Views: 225706 Last moment tuitions
Meta data  in 5 mins hindi
 
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#metadata #datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 109134 Last moment tuitions
Excel Tutorial: What is Business Intelligence and an OLAP Cube? | ExcelCentral.com
 
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This video lesson fully explains the concepts of Business Intelligence, OLAP, MDX and and how they apply to Excel 2013. At http://ExcelCentral.com you can view over 850 free Excel video lessons just like this one. All in full HD with vari-speed and human-transcribed subtitles providing the perfect Excel learning environment. You can also track your progress through the course and print a certificate upon completion. Separate videos are provided for Excel 2007, Excel 2010 and Excel 2013. The lesson begins with an explanation of OLAP and its purpose. You'll learn about OLAP Cubes and how they are divided into Dimensions, Measure and Hierarchies to create a multidimensional data structure. You'll also learn about how the MDX query language is used to extract values from OLAP cubes. This lesson also explains the concept of Business Intelligence and how it applies to OLAP. This video comes from the Data Model, OLAP, MDX and BI session (Session 6 in our Excel 2013 Expert Skills free video training course). This session includes the following video lessons: ▪ Lesson 6-1: Understand primary and foreign keys (11m 27s) http://excelcentral.com/excel2013/expert/lessons/06010-understand-primary-key-foreign-key-relationships.html ▪ Lesson 6-2: Create a simple data model (6m 31s) http://excelcentral.com/excel2013/expert/lessons/06020-create-a-simple-data-model.html ▪ Lesson 6-3: Understand OLAP, MDX and Business Intelligence (10m 17s) http://excelcentral.com/excel2013/expert/lessons/06030-what-is-business-intelligence-and-an-olap-cube.html ▪ Lesson 6-4: Use the GETPIVOTDATA function (4m 31s) http://excelcentral.com/excel2013/expert/lessons/06040-use-the-getpivotdata-function.html ▪ Lesson 6-5: Use the CUBEVALUE function to query an OLAP cube (5m 40s) http://excelcentral.com/excel2013/expert/lessons/06050-use-the-cubevalue-function-to-query-an-olap-cube.html ▪ Lesson 6-6: Convert CUBEVALUE functions to include MDX expressions (5m 48s) http://excelcentral.com/excel2013/expert/lessons/06060-convert-cubevalue-functions-to-include-mdx-expressions.html ▪ Lesson 6-7: Understand OLAP pivot table limitations (10m 52s) http://excelcentral.com/excel2013/expert/lessons/06070-understand-olap-pivot-table-limitations.html ▪ Lesson 6-8: Create an asymmetric OLAP pivot table using Named Sets (4m 57s) http://excelcentral.com/excel2013/expert/lessons/06080-create-an-asymmetric-olap-pivot-table-using-named-sets.html ▪ Lesson 6-9: Understand many-to-many relationships (11m 5s) http://excelcentral.com/excel2013/expert/lessons/06090-understand-many-to-many-relationships.html ▪ Lesson 6-10: Create an OLAP pivot table using a many-to-many relationship (12m 47s) http://excelcentral.com/excel2013/expert/lessons/06100-create-an-olap-pivot-table-using-a-many-to-many-relationship.html You can watch any of the 850 Excel video lessons, free and without any required registration at http://excelcentral.com/excel2013/expert/tutorials/default.html.
Views: 249273 ExcelCentral.com
Query language Meaning
 
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Video shows what query language means. Any of several generalized computer languages in which users may extract data from selected records in a database. Query language Meaning. How to pronounce, definition audio dictionary. How to say query language. Powered by MaryTTS, Wiktionary
Views: 260 ADictionary
Determiners in English Grammar: Articles, Demonstratives , Possessives & Quantifiers(in hindi)
 
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Views: 346621 BE BANKER
Macro Definition ll Calling a Macro ll Expansion of Macro Explained with Examples in Hindi
 
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Views: 7362 5 Minutes Engineering
Relational Database Concepts
 
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Basic Concepts on how relational databases work. Explains the concepts of tables, key IDs, and relations at an introductory level. For more info on Crow's Feet Notation: http://prescottcomputerguy.com/tmp/crows-foot.png
Views: 585228 Prescott Computer Guy
SQL Bangla Tutorial  Part6  (Data Definition)
 
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SQL Data Definition by using Create,Alter,Add and Drop command..
Views: 906 Mehedi Russel
What is SQL in Data Analytics - Data Science Jargon for Beginners
 
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In this episode of data analytics terms for beginners I am going to explain what SQL, or structured query language is in data analytics. ► Full Playlist Explaining Data Jargon ( https://www.youtube.com/playlist?list=PL_9qmWdi19yDhnzqVCAhA4ALqDoqjeUOr ) ► http://jobsinthefuture.com/index.php/2017/11/28/what-is-sql-data-science-jargon-for-beginners/ There are a lot of terms in the data science industry and I wanted to create this beginner series to help explain the large mass of data analytics terms. Previously we defined what a database was, and if you missed that article click here, then come back and read about SQL. It is important that you understand each one and their relation. What is SQL? Structured Query Langauge Structure: Database or an organized collection of items, specifically data for our situation. Query: The way in which you call an item or information out from a database or collection to be accessed. In the physical world I would say, "I queried my friend Jim about the steak sandwich he eat at the new restaurant in Bronx." I gathered information from him. Language: A specific dialect in which you can communicate (whether that be systems or people). In the computer science world: Python, Java Script, HTML5, etc... Once you breakdown SQL it is a very simple concept for a complex system. You are simply extracting data from a very large database. You are using that data to help companies formulate a plan of action for their business. SQL = Structure: Database full of data, Query: you call out the data, Language: using a computer science language. ------- SOCIAL Twitter ► @jobsinthefuture Facebook ►/jobsinthefuture Instagram ►@Jobsinthefuture WHERE I LEARN: (affiliate links) Lynda.com ► http://bit.ly/2rQB2u4 edX.org ► http://fxo.co/4y00 MY FAVORITE GEAR: (affiliate links) Camera ► http://amzn.to/2BWvE9o CamStand ► http://amzn.to/2BWsv9M Compute ► http://amzn.to/2zPeLvs Mouse ► http://amzn.to/2C0T9hq TubeBuddy ► https://www.tubebuddy.com/bengkaiser ► Download the Ultimate Guide Now! ( https://www.getdrip.com/forms/883303253/submissions/new ) Thanks for Supporting Our Channel! DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This help support the channel and allows us to continue to make videos like this. Thank you for the support!
Views: 815 Ben G Kaiser
MDX Query Basics (Analysis Services 2012)
 
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This video is part of LearnItFirst's SQL Server 2012: A Comprehensive Introduction course. More information on this video and course is available here: http://www.learnitfirst.com/Course170 In this video, we walk through the basics of the MDX Query language. It is a very logical language, however, is somewhat large in syntax. If you enjoy writing Transact-SQL, you will really enjoy the MDX language. The AdventureWorks2012 multidimensional models need to be installed on your SSAS Multidimensional mode instance from the CodePlex web site. Highlights from this video: - The basics of an MDX query - What is the basic format of the MDX query language? - Is it necessary to have a WHERE clause in an MDX query? - How to signal the end of a statement in the MDX query language - Using the Internet Order Count and much more...
Views: 106935 LearnItFirst.com
L2: Data Warehousing and Data Mining |Enterprise data Warehousing|Data mart|Warehousing Terminology
 
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Join My official Whatsapp group by following link https://chat.whatsapp.com/F9XFi6QYFYOGA9JGw4gc1o L2: Data Warehousing and Data Mining |Enterprise data Warehousing|Data mart|Warehousing Terminology Namaskar, In the Today's lecture i will cover Introduction to Data Warehousing and Data Mining of subject Data Warehousing and Data Mining which is one of the important subject of computer science and engineering Syllabus Unit1: Data Warehousing: Overview, Definition, Data Warehousing Components, Building a Data Warehouse, Warehouse Database, Mapping the Data Warehouse to a Multiprocessor Architecture, Difference between Database System and Data Warehouse, Multi Dimensional Data Model, Data Cubes, Stars, Snow Flakes, Fact Constellations, Concept. Unit 2: Data Warehouse Process and Technology: Warehousing Strategy, Warehouse /management and Support Processes, Warehouse Planning and Implementation, Hardware and Operating Systems for Data Warehousing, Client/Server Computing Model & Data Warehousing. Parallel Processors & Cluster Systems, Distributed DBMS implementations, Warehousing Software, Warehouse Schema Design. Unit 3: Data Mining: Overview, Motivation, Definition & Functionalities, Data Processing, Form of Data Pre-processing, Data Cleaning: Missing Values, Noisy Data, (Binning, Clustering, Regression, Computer and Human inspection), Inconsistent Data, Data Integration and Transformation. Data Reduction:-Data Cube Aggregation, Dimensionality reduction, Data Compression, Numerosity Reduction, Discretization and Concept hierarchy generation, Decision Tree. Unit 4: Classification: Definition, Data Generalization, Analytical Characterization, Analysis of attribute relevance, Mining Class comparisons, Statistical measures in large Databases, Statistical-Based Algorithms, Distance-Based Algorithms, Decision Tree-Based Algorithms. Clustering: Introduction, Similarity and Distance Measures, Hierarchical and Partitional Algorithms. Hierarchical Clustering- CURE and Chameleon. Density Based Methods-DBSCAN, OPTICS. Grid Based Methods- STING, CLIQUE. Model Based Method –Statistical Approach, Association rules: Introduction, Large Item sets, Basic Algorithms, Parallel and Distributed Algorithms, Neural Network approach. Unit 5: Data Visualization and Overall Perspective: Aggregation, Historical information, Query Facility, OLAP function and Tools. OLAP Servers, ROLAP, MOLAP, HOLAP, Data Mining interface, Security, Backup and Recovery, Tuning Data Warehouse, Testing Data Warehouse. Warehousing applications and Recent Trends: Types of Warehousing Applications, Web Mining, Spatial Mining and Temporal Mining I am Sandeep Vishwakarma (www.universityacademy.in) from Raj Kumar Goel Institute of Technology Ghaziabad. I have started a YouTube Channel Namely “University Academy” Teaching Training and Informative. On This channel am providing following services. 1 . Teaching: Video Lecture of B.Tech./ M.Tech. Technical Subject who provide you deep knowledge of particular subject. Compiler Design: https://www.youtube.com/playlist?list=PL-JvKqQx2Ate5DWhppx-MUOtGNA4S3spT Principle of Programming Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdIkEFDrqsHyKWzb5PWniI1 Theory of Automata and Formal Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdhlS7j6jFoEnxmUEEsH9KH 2. Training: Video Playlist of Some software course like Android, Hadoop, Big Data, IoT, R programming, Python, C programming, Java etc. Android App Development: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdBj8aS-3WCVgfQ3LJFiqIr 3. Informative: On this Section we provide video on deep knowledge of upcoming technology, Innovation, tech news and other informative. Tech News: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdFG5kMueyK5DZvGzG615ks Other: https://www.youtube.com/playlist?list=PL-JvKqQx2AtfQWfBddeH_zVp2fK4V5orf Download You Can Download All Video Lecture, Lecture Notes, Lab Manuals and Many More from my Website: http://www.universityacademy.in/ Regards University Academy UniversityAcademy Website: http://www.universityacademy.in/ YouTube: https://www.youtube.com/c/UniversityAcademy Facebook: https://www.facebook.com/UniversityAcademyOfficial Twitter https://twitter.com/UniAcadofficial Instagram https://www.instagram.com/universityacademyofficial Google+: https://plus.google.com/+UniversityAcademy
Views: 493 University Academy
What is data & Types of data -Explained in detail
 
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Views: 10649 Quality HUB India
Data Warehousing and Data Mining
 
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This course aims to introduce advanced database concepts such as data warehousing, data mining techniques, clustering, classifications and its real time applications. SlideTalk video created by SlideTalk at http://slidetalk.net, the online solution to convert powerpoint to video with automatic voice over.
Views: 4726 SlideTalk
Research Methodology Meaning Types Objectives [Hindi]
 
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Methodology is the systematic, theoretical analysis of the methods applied to a field of study. A research method is a systematic plan for conducting research. Sociologists draw on a variety of both qualitative and quantitative research methods, including experiments, survey research, participant observation, and secondary data.
Views: 145374 Manager Sahab
Frequent Pattern (FP) growth Algorithm for Association Rule Mining
 
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The FP-Growth Algorithm, proposed by Han, is an efficient and scalable method for mining the complete set of frequent patterns by pattern fragment growth, using an extended prefix-tree structure for storing compressed and crucial information about frequent patterns named frequent-pattern tree (FP-tree).
Views: 98903 StudyKorner
INTRODUCTION TO DATA MINING IN HINDI
 
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Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 111167 LearnEveryone
Data Warehouses: Snowflake | The Daily Segment
 
05:25
Snowflake is a relatively new entrant to the cloud data warehouse space that’s competing with IaaS providers. How are they pulling off such growth?
Views: 305 Segment
What is INFORMATION RETRIEVAL? What does INFORMATION RETRIEVAL mean? INFORMATION RETRIEVAL meaning
 
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✪✪✪✪✪ WORK FROM HOME! Looking for WORKERS for simple Internet data entry JOBS. $15-20 per hour. SIGN UP here - http://jobs.theaudiopedia.com ✪✪✪✪✪ ✪✪✪✪✪ The Audiopedia Android application, INSTALL NOW - https://play.google.com/store/apps/details?id=com.wTheAudiopedia_8069473 ✪✪✪✪✪ What is INFORMATION RETRIEVAL? What does INFORMATION RETRIEVAL mean? INFORMATION RETRIEVAL meaning - INFORMATION RETRIEVAL definition - INFORMATION RETRIEVAL explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Information retrieval (IR) is the activity of obtaining information resources relevant to an information need from a collection of information resources. Searches can be based on full-text or other content-based indexing. Automated information retrieval systems are used to reduce what has been called "information overload". Many universities and public libraries use IR systems to provide access to books, journals and other documents. Web search engines are the most visible IR applications. An information retrieval process begins when a user enters a query into the system. Queries are formal statements of information needs, for example search strings in web search engines. In information retrieval a query does not uniquely identify a single object in the collection. Instead, several objects may match the query, perhaps with different degrees of relevancy. An object is an entity that is represented by information in a content collection or database. User queries are matched against the database information. However, as opposed to classical SQL queries of a database, in information retrieval the results returned may or may not match the query, so results are typically ranked. This ranking of results is a key difference of information retrieval searching compared to database searching. Depending on the application the data objects may be, for example, text documents, images, audio, mind maps or videos. Often the documents themselves are not kept or stored directly in the IR system, but are instead represented in the system by document surrogates or metadata. Most IR systems compute a numeric score on how well each object in the database matches the query, and rank the objects according to this value. The top ranking objects are then shown to the user. The process may then be iterated if the user wishes to refine the query.
Views: 13680 The Audiopedia
Database and Big Data
 
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This course introduces important database concepts, including data modeling, database design, and data extraction. Students will also learn data analysis skills they need to transform raw data into useful business information and knowledge for decision-making and problem solving. Students explore relational design, data warehousing, data mining, data visualization, data search, knowledge management, business intelligence, data querying, basic analytics, and reporting.
Views: 814 [email protected]
0/1 knapsack problem-Dynamic Programming | Data structure and algorithms
 
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In this video, I have explained 0/1 knapsack problem with dynamic programming approach. Given a bag of a certain capacity, W. Given some items with their weights and profit(values). How do you fill this bag so that you get the maximum profit? Jenny’s Lectures CS/IT NET&JRF is a Free YouTube Channel providing Computer Science / Information Technology / Computer-related tutorials including Programming Tutorials, NET & JRF Coaching Videos, Algorithms, GATE Coaching Videos, UGC NET, NTA NET, JRF, BTech, MTech, Ph.D., tips and other helpful videos for Computer Science / Information Technology students to advanced tech theory and computer science lectures, Teaching Computer Science in Informal Space. Learning to teach computer scienceoutside the classroom…. YouTube a top choice for users that want to learn computer programming, but don't have the money or the time to go through a complete college/ Institute / Coaching Centre course. ... Jenny’s Lectures CS/IT NET&JRF is aFree YouTube Channel providing computer-related ... and educate students in science, technology and other subjects. If you have any further questions, query, topic, please don't hesitate to contact me. Please feel free to comment or contact by ([email protected]), if you require any further information. Main Topics: Algorithms, Applied Computer Science, Artificial Intelligence, Coding, Computer Engineering, Computer Networking,Design and Analysis Of Algorithms, Data Structures, Digital Electronics, Object Oriented Programming using C++/Java/Python, Discrete Mathematical Structures, Operating Systems Computer Simulation, Computing, Bit Torrent, Abstract, C, C++, Acrobat, Ada, Pascal, ADABAS, Ad-Aware, Add-in, Add-on, Application Development, Adobe Acrobat, Automatic Data Processing, Adware, Artificial Intelligence, AI, Algorithm, Alphanumeric, Apache, Apache Tomcat, API, Application Programming Interface, Applet, Application, Application Framework, Application Macro, Application Package, Application Program, Application Programmer, Application Server, Application Software, Application Stack, Application Suite, System Administrator, Ada Programming, Architecture, computer software, ASP, Active Server Pages, Assembly, Assembly Language, Audacity, AutoCAD, Autodesk, Auto sketch, Backup, Restore, Backup & Recovery, BASH, BASIC, Beta Version, Binary Tree, Boolean, Boolean Algebra, Boolean AND, Boolean logic, Boolean OR, Boolean value, Binary Search Tree, BST, Bug, Business Software, C Programming Language, Computer Aided Design, Auto CAD, National Testing Agency, NTA,CAD, Callback, Call-by-Reference, Call by reference, Call-by-Value, Call by Value, CD/DVD, Encoding, Mapping, Character, Class, Class Library, ClearCase, ClearQuest, Client, Client-Side, cmd.exe, Cloud computing, Code, Codec, ColdFusion, Command, Command Interpreter, Command.com, Compiler, Animation, Computer Game, Computer Graphics, Computer Science, CONFIG.SYS, Configuration, Copyright, Customer Relationship Management, CRM, CVS, Data, Data Architect, Data Architecture, Data Cleansing, Data Conversion, Data Element, Data Mapping, Data Migration, Data Modeling, Data Processing, Data Scrubbing, Data Structure , Data Transformation, Database Administration, Database Model, Query Language, Database Server, Data log, Debugger, Database Management System, DBMS, Data Definition Language, DDL, Dead Code, Debugger, Decompile, Defragment, Delphi, Design Compiler, Device Driver, Distributed, Data Mart, Data Mining, Data Manipulation Language, DML, DOS, Disk Operating System, Dreamweaver, Drupal, Data Warehouse, Extensible Markup Language, XML, ASCII, Fibonacci , Firefox, Firmware, GUI, Graphical User Interface, LINUX, UNIX, J2EE, Java 2 Platform, Enterprise Edition, Java, Java EE, Java Beans, Java Programming Language, JavaScript, JDBC, Java Database Connectivity, Kernel, Keyboard, Keygen, LAMP, MySQL, Perl, PHP, Python, Logic Programming, Locator, Fusion, Fission, Low-Level Language, Mac OS, Macintosh Operating System, Machine Code, Machine Language, Metadata, Microsoft Access, Microsoft .Net Framework, Microsoft .Net, Microsoft SQL Server, Microsoft Windows, Middleware, MIS, Management Information systems, Module, Mozilla, MS-DOS,Microsoft Disk Operating System, Magic User Interface, MUI, MySQL, Normalization, Numerical, Object-Oriented, Open Source, Solaris, Parallel Processing, Parallel, Patch, Pascal, PDF, Portable Document Format, Postgres, Preemptive, Program, Programming Language, QuickTime, Report Writer, Repository, Rewind, Runtime, Scripting Languages, Script, Search Engine, Software Life-Cycle, VBScript, Virtual Basic Script, Classes, Queues, Stack, B-Tree, Computer Science, Information Technology, IT, CSE
Part 2.1 | Entity relationship model diagram in dbms in hindi introduction and basics syllabus
 
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Thank you friends for watching the video. Please Like the video if it has helped you in any way & do subscribe to the channel. This video discusses about Entity Relationship Model, generally called ER model or ER diagram in dbms. Important topics of dbms like What is the need of entity relationship model,Introduction of ER diagrams, Basics of Entity relationship diagram in hindi, Introduction to Entity in ER diagram, Usefulness of ER diagram in DBMS, When we use ER diagrams in database, How to convert a relation in er diagram, How to make ER diagram, Basic of attributes in ER diagram, What are the relationships in ER diagram in dbms,What are traps in ER diagram in dbms are discussed. E-R DIAGRAM/model i) Introduced in 1976 by Dr peter chen. ii) It is non-technical design method works on conceptual level based on the perception of the real world iii) It consists of collections of basic objects, called entities and of relationships among these objects and attributes which defines their properties. iv) It is free from ambiguities and provides a standard and logical way of visualizing the data. v) It is Conceptual design and relational database is representation design Please spare some time and fill this form so that we can know about you and what you think about us: https://goo.gl/forms/b5ffxRyEAsaoUatx2 Your review/recommendation and some words can help validating our quality of content and work.. so Please do the following: - 1) Give us a 5 star review with comment on Google https://goo.gl/maps/sLgzMX5oUZ82 2) Follow our Facebook page and give us a 5 star review with comments https://www.facebook.com/pg/knowledgegate.in/reviews 3) Follow us on Instagram https://www.instagram.com/mail.knowledgegate/ DBMS blueprint, DataBase Management system,database,DBMS, RDBMS, Relations, Table, Query, Normalization, Normal forms,Database design,Relational Model,Instance,Schema,Data Definition Language, SQL queries, ER Diagrams, Entity Relationship Model,Constraints,Entity,Attributes,Weak entity, Types of entity,DataBase design, database architecture, Degree of relation,Cardinality ratio,One to many relationship,Many to many relationships,Relational Algebra,Relational Calculus, Tuples, Natural Join, Join operations,Database Architecture,database Schema, Keys in DBMS, Primary keys, Candidate keys, Foreign keys,Data redundancy, Duplicacy in data, Data Inconsistency, Normalization, First Normal Form,Second Normal Form, third normal forms, Boye codd's normal form,1NF,2NF,3NF,BCNF, Normalization rules, Decomposition of relation, Functional Dependency,Partial Dependency, Multivalued dependency,Indexing,Hashing, B tree,B+ tree,Ordered Indexing,Select operation,Join operations, Natural joins, SQL commands,File structure in DBMS,Primary Indexing,Clustered Indexing,Concurrency control protocols, Transaction Management in DBMS,ACID properties, Data Consistency, Concurrency in database,Deadlock in database, Deadlock handling, Database Recovery, Deadlock avoidance, Deadlock prevention,Scheduling in dbms, Conflict Serializability, Serial Schedules, Two phase locking,SQL commands,DBMS for gate , DBMS for net, DBMS lectures, DBMS tutorials, DBMS for beginners, learn DBMS, ER diagram in hindi,ER diagram in DBMS,ER diagram and relational schema,ER diagram design,ER diagram dbms example,Entity relationship diagram tutorial in hindi,entity relationship examples m hindi, Entity relationship to table, Entity relationship diagram attributes, Weak Entities,Strong entities,Tangible Entity in dbms, Intangible Entity in dbms,Blueprint of ER diagram for gate, Er diagram tutorial for net, ER diagram in hindi,ER diagram in DBMS,ER diagram and relational schema,ER diagram design,ER diagram dbms example,Entity relationship diagram tutorial in hindi,entity relationship examples m hindi, Entity relationship to table, Entity relationship diagram attributes, Weak Entities,Strong entities,Tangible Entity in dbms, Intangible Entity in dbms,Blueprint of ER diagram for gate, Er diagram tutorial for net, DBMS blueprint, DataBase Management system,database,DBMS, RDBMS, Relations, Table, Query, Normalization, Normal forms,Database design,Relational Model,Instance,Schema,Data Definition Language, SQL queries, ER Diagrams, Entity Relationship Model,Constraints,Entity,Attributes,Weak entity, Types of entity,DataBase design, database architecture, Degree of relation,Cardinality ratio,One to many relationship,Many to many relationships,Relational Algebra,Relational Calculus, Tuples, Natural Join, Join operations,Database Architecture,database Schema, Keys in DBMS, Primary keys, Candidate keys, Foreign keys,Data redundancy, Duplicacy in data, Data Inconsistency, Normalization, First Normal Form,Second Normal Form, third normal forms, Boye codd's normal form,1NF,2NF,3NF,BCNF, Normalization rules, Decomposition of relation, Functional Dependency,Partial Dependency, Multivalued dependency,Indexing,Hashing, B tree,B+ tree,
Views: 86798 KNOWLEDGE GATE
variance and standard deviatin calvulation in hindi urdu in data mining concepts
 
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http://www.t4tutorials.com/how-to-calculate-variance-of-data-data-mining-tutorials/ Thank you very much to https://t4tutorials.com Like Our Page: https://www.facebook.com/t4tutorialsOfficial/ For Business Queries: +923028700085 Email: [email protected]
Views: 15701 University Of Shamil
A Gentle Introduction to Wikidata for Absolute Beginners [including non-techies!]
 
03:04:33
This talk introduces the Wikimedia Movement's latest major wiki project: Wikidata. It covers what Wikidata is (00:00), how to contribute new data to Wikidata (1:09:34), how to create an entirely new item on Wikidata (1:27:07), how to embed data from Wikidata into pages on other wikis (1:52:54), tools like the Wikidata Game (1:39:20), Article Placeholder (2:01:01), Reasonator (2:54:15) and Mix-and-match (2:57:05), and how to query Wikidata (including SPARQL examples) (starting 2:05:05). The slides are available on Wikimedia Commons: https://commons.wikimedia.org/wiki/File:Wikidata_-_A_Gentle_Introduction_for_Complete_Beginners_(WMF_February_2017).pdf The video is available on Wikimedia Commons: https://commons.wikimedia.org/wiki/File:A_Gentle_Introduction_to_Wikidata_for_Absolute_Beginners_(including_non-techies!).webm And on YouTube: https://www.youtube.com/watch?v=eVrAx3AmUvA Contributing subtitles would be very welcome, and could help people who speak your language benefit from this talk!
Views: 6682 MediaWiki
How kNN algorithm works
 
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In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3. This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy
Views: 416082 Thales Sehn Körting
Candidate Generation - Chapter 4 Part 1
 
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Text Mining and Analytics Candidate Generation - Chapter 4 This video tutorials cover major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications. analytics | analytics tools | analytics software | data analysis programs | data mining tools | data mining | text analytics | strucutred data | unstructured data |text mining | what is text mining | text mining techniques | AQL | Annotation Query Language More Articles, Scripts and How-To Papers on http://www.aodba.com
Views: 550 AO DBA
What is TEXT MINING? What does TEXT MINING mean? TEXT MINING meaning, definition & explanation
 
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What is TEXT MINING? What does TEXT MINING mean? TEXT MINING meaning - TEXT MINING definition - TEXT MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities). Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The overarching goal is, essentially, to turn text into data for analysis, via application of natural language processing (NLP) and analytical methods. A typical application is to scan a set of documents written in a natural language and either model the document set for predictive classification purposes or populate a database or search index with the information extracted. The term text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation. The term is roughly synonymous with text mining; indeed, Ronen Feldman modified a 2000 description of "text mining" in 2004 to describe "text analytics." The latter term is now used more frequently in business settings while "text mining" is used in some of the earliest application areas, dating to the 1980s, notably life-sciences research and government intelligence. The term text analytics also describes that application of text analytics to respond to business problems, whether independently or in conjunction with query and analysis of fielded, numerical data. It is a truism that 80 percent of business-relevant information originates in unstructured form, primarily text. These techniques and processes discover and present knowledge – facts, business rules, and relationships – that is otherwise locked in textual form, impenetrable to automated processing.
Views: 2375 The Audiopedia
Introduction to SQL
 
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A brief introduction to structured query language for the IPT course. Focuses on the key words of SELECT, FROM, WHERE and ORDER BY.
ETL ( Extract Transform Load )   process fully explained  in hindi | Datawarehouse
 
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#etl #datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 61212 Last moment tuitions
create mysql database, tables and insert data using php functions
 
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create mysql database, tables and insert data using php functions in this video you will learn how you can create database and tables using wampserver and phpmyadmin....and you will be able to insert data using php and mysql... google,youtube,create,database,table,mysql,php,query,function,insert,into,delete,update,phpmyadmin,insert data,how to,learn,mysql database,create database,create table,insert data,using php functions,youtube,google,2017,new google,youtube,insert data into tables using php functions,learn how to,insert data,Table,Insert,PHP (Programming Language),Data (Dimension),Into,function,php functions,insert data into mysql,mysql,table,update,statement,query,fetch,connection,mysql functions,connectionn class,class,method,wampserver,wamp,using wamp server,training,tutorials,insert data tutorial,how to insert,phpmyadmin,learn,database,php language,ajax,html,jquery,phpmyadmin, learn how to create mysql database and tables, learn how to insert data into tables using php functions | phpmyadmin
Views: 665873 kasa sahar
Introduction to image processing in hindi #1  | Image processing Lectures
 
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Take the Full Course of Image Processing What we Provide 1) 28 Videos (Index is given down) + More Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in Image processing To buy the course click https://goo.gl/duY55P if you have amy query you can email us at [email protected] Sample Notes : https://goo.gl/v52KcR or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 Image Processing Index Lecture 1 Introduction to Image processing Lecture 2 Linear Stretching Lecture 3 Histogram Equalization Lecture 4 IGS ( Improved Grey scale ) Lecture 5 Bit Plane Slicing Lecture 6 Hough Transform Lecture 7 Homomorphic Filtering Lecture 8 DFT in Image processing Lecture 9 DIT-FFT in image processing Lecture 10 DIF-FFT in image processing Lecture 11 Hadamard Transform Lecture 12 Walsh Transform Lecture 13 DCT (Discrete Cosine Transform ) Lecture 14 Haar Transform Lecture 15 Dilation and Erosion (Morphological Operation ) Lecture 16 Opening and Closing (Morphological Operation ) Lecture 17 HIT and MISS (Morphological Operation ) Lecture 18 Data Compression (lossy and lossless) Lecture 19 Difference between Lossy vs lossless compression Lecture 20 Unitary matrix Lecture 21 Color models (RGB,CMY,HSI ) Lecture 22 Zero memory Point Operation Lecture 23 Fidelity Criteria Lecture 24 Moments with example Lecture 25 Thresholding in Image processing Lecture 26 Region Growing in Image Segmentation Lecture 27 Region Splitting in Image Segmentation Lecture 28 Region Merging in Image Segmentation More videos Coming soon
Views: 109455 Last moment tuitions
"Data Science" What Is Text Mining ? | Applications Of Text Mining And Clustering | Training -ExcelR
 
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What Is Text Mining ? | Applications Of Text Mining And Clustering #DataScience #TextMining #Clustering #TextMining In Datascience #Applications of Textmining #training #2018 Text Mining, Hmm before we dive into the water we need to make sure that we are ready. So in the same way before going into deep we should know what is text-mining and it`s applications? Daily a new data is being generated and mostly majority of data is unstructured. To convert/transform the unstructured data (unstructured text data) into structured, then the unstructured text data is to set for analysis using text-mining to get new generated information. Like we get daily 1. Call transcripts, 2. Emails that we sent to customer service 3. Social Media outreach (Facebook, twitter, Instagram and many more) 4. Speech transcripts 5. Filed agents, sales people 6. Interviews and survey`s The process of getting high quality of information deriving from the text data is text –mining. To examine the large amount of text data/Written data sources to generate new information. This quality information I typically derived through devising of patters and trends such as statistical pattern learning. Clustering in data mining is gathering set of abstract data and aggregating them based on their similarities. Here are some of the applications of text mining and clustering are: 1. Text categorization into particular domains 2. Organizing a set of documents automatically by text Clustering. 3. Identifying and extracting subject information in documents. In other words-sentiment analysis. 4. Extracting entity/concepts which can identify people, places, organisations and other entities. 5. Learning relations between named entities. In this video you will learn about 1. Text Mining and use of Clustering 2. Applications of Text Mining 3. What is Word Cloud? SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/excelr-solutions/ Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
The Basic Concept of Data Warehouse | What is DATA WAREHOUSING | Why #datawarehouse is important
 
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Data Warehousing a concept of storing Transformed data into a location where you can run your reports to make important business decisions. Many organizations use data warehouse to analyze sales, marketing etc. data to make important decisions. ETL is the tool that is used to transformed data from the initial load. If you have liked the video, please subscribe. Read our blogs - www.sqlultra.com Follow me on LinkedIn - https://www.linkedin.com/in/iqbalsqlexpert/
Views: 247 SQL ULTRA
Ontologies
 
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Dr. Michel Dumontier from Stanford University presents a lecture on "Ontologies." Lecture Description Ontology has its roots as a field of philosophical study that is focused on the nature of existence. However, today's ontology (aka knowledge graph) can incorporate computable descriptions that can bring insight in a wide set of compelling applications including more precise knowledge capture, semantic data integration, sophisticated query answering, and powerful association mining - thereby delivering key value for health care and the life sciences. In this webinar, I will introduce the idea of computable ontologies and describe how they can be used with automated reasoners to perform classification, to reveal inconsistencies, and to precisely answer questions. Participants will learn about the tools of the trade to design, find, and reuse ontologies. Finally, I will discuss applications of ontologies in the fields of diagnosis and drug discovery. View slides from this lecture: https://drive.google.com/open?id=0B4IAKVDZz_JUVjZuRVpMVDMwR0E About the Speaker Dr. Michel Dumontier is an Associate Professor of Medicine (Biomedical Informatics) at Stanford University. His research focuses on the development of methods to integrate, mine, and make sense of large, complex, and heterogeneous biological and biomedical data. His current research interests include (1) using genetic, proteomic, and phenotypic data to find new uses for existing drugs, (2) elucidating the mechanism of single and multi-drug side effects, and (3) finding and optimizing combination drug therapies. Dr. Dumontier is the Stanford University Advisory Committee Representative for the World Wide Web Consortium, the co-Chair for the W3C Semantic Web for Health Care and the Life Sciences Interest Group, scientific advisor for the EBI-EMBL Chemistry Services Division, and the Scientific Director for Bio2RDF, an open source project to create Linked Data for the Life Sciences. He is also the founder and Editor-in-Chief for a Data Science, a new IOS Press journal featuring open access, open review, and semantic publishing. Please join our weekly meetings from your computer, tablet or smartphone. Visit our website to learn how to join! http://www.bigdatau.org/data-science-seminars
Kdd process Knowledge Discovery In Databases(KDD)
 
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KDD process,database management,software engineering,data mining,knowledge process,artificial intelligence,machine learning
Views: 156 Edu World
Query Processing & Optimization Introduction | Query Processing Steps | Query Blocks
 
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Query Processing & Optimization Introduction | Query Processing Steps | Query Blocks Like Us on Facebook - https://www.facebook.com/Easy-Engineering-Classes-346838485669475/ DBMS Hindi Classes Database Management System Tutorial for Beginners in Hindi Database Management System Study Notes DBMS Notes Database Management System Notes
Named Entity Recognition - Natural Language Processing With Python and NLTK p.7
 
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Named entity recognition is useful to quickly find out what the subjects of discussion are. NLTK comes packed full of options for us. We can find just about any named entity, or we can look for specific ones. NLTK can either recognize a general named entity, or it can even recognize locations, names, monetary amounts, dates, and more. sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 76411 sentdex
LECTURE1 GIS INTRODUCTION IN HINDI
 
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Buy GIS books (affiliate): Remote Sensing and GIS https://amzn.to/2Ce41NL Advanced Surveying: Total Station, GPS, GIS & Remote Sensing by Pearson https://amzn.to/2wEAXcj An Introduction to Geographic Information Technology https://amzn.to/2Q2XuID Mastering QGIS https://amzn.to/2oFi717 QGIS Python Programming Cookbook https://amzn.to/2wHUkSu QGIS: Becoming a GIS Power User https://amzn.to/2PYCz9D Remote Sensing: Principles and Applications https://amzn.to/2Q4Wi7x Gis: Fundamentals, Applications and Implementations https://amzn.to/2Q5iFK6 Remote Sensing and Geographical Information Systems: Basics and Applications https://amzn.to/2Q2dI4y Textbook of Remote Sensing and Geographical Information Systems https://amzn.to/2Q748h9 Remote Sensing and GIS in Environment Resource Management https://amzn.to/2Q2fpPs ------------------------------------- Notes of Geographical Information Systems Fundamentals on this link - https://viden.io/knowledge/geographical-information-systems-fundamentals?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=ajaze-khan-1
Views: 45077 LearnEveryone
SQL Database Administration Day 1
 
01:04:14
In this video we will learn what is SQL and SQL Components using the Church ERP Database built on Oracle Cloud - PowerEngine SQL Data Miner What is SQL ? Structured Query Language What are the components of SQL? 1. DDL (Data Definition Language) 2. DML (Data Manipulation Language) 3. Query Language or Data Mining Reporting Language Enjoy
Views: 56 BTC GHANA LTD