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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: 5520 MediaWiki
Data Mining | Web Scraping - Semalt
 
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Visit us - https://semalt.com/?ref=y #mining, #scraping, #web, #data, #mirrolure_web, #web_visita, #mining_social, #data_and, #cqajoschua_web, #piechart_data, #web_s, #sorteiotirolplus_data, #data_in, #abd_lhamit_web web scraping data mining data mining vs web scraping web scraping data extraction and web mining web data scraping web scraping data scraping web data data scraping report mining data scraping data mining project data scraping vs data mining data mining scraping software screen scraping data mining difference between web scraping and data mining stars web data scraping web data scraping freeware web data scraping tutorial web data scraping service web data scraping services structured web data scraping php web data scraping web data scraping c# web data scraping jobs web data scraping legal big data web scraping web data scraping python campus web data scraping harvesting web data scraping scraping web page data web page data scraping sensor web data scraping data extraction web scraping 150mb web data scraping web data scraping excel web crawler data scraping web data scraping tool web scraping post data free web data scraping scraping data web page web data scraping php web data scraping techniques web scraping data python web data scraping tools fuzzy web data scraping web data scraping software web scraping data extraction web mining data mining software web content mining scraping data mining web mining data mining web data web data data mining data web mining data mining web web data mining mining web data webpy web input web data scraping difference between data scraping and data mining data mining web mining text mining data mining mining mining text web data mining text mining web mining web scraping free web data scraping software big data web scraping free web data scraping c-130 big data web scraping open scraping web data with excel big data web scraping tutorial web data scraping c thomas web page data scraping php web page data scraping wikipedia is web data scraping legalzoom big data web scraping python data snarfing web scraping tools web crawler data scraping companies web data extraction scraping software web page data scraping free data snarfing web scraping software web data scraping c-span web data scraping c&a what is web data scraping is web data scraping legalization web data scraping open source web page data scraping services web scraping freeware mac data big data web scraping software web crawler data scraping php web page data scraping programs google earth web data scraping big data web scraping api big data web scraping tool web data scraping c wonder web data scraping c-diff web page data scraping r ppt web mining data mining web page data scraping legal is web data scraping legalize data extraction web scraping tool web page data scraping illegal web page data scraping tutorial web crawler data scraping legal web mining and data mining is web data scraping legal web data scraping c string data snarfing web scraping companies data snarfing web scraping python big data web scraping program scraping data web page excel scraping web data with r data mining web mining education data snarfing web scraping api big data web scraping definition web crawler data scraping tutorial web page data scraping companies web data scraping tools free data mining i web mining data mining web mining pdf data mining vs web mining data mining and web mining data mining web mining ppt web mining vs data mining web-scraping excel formulas web data scraping big data web scraping legal web crawler data scraping service data snarfing web scraping definition data snarfing web scraping free big data web scraping tools web page data scraping service data kdnuggets mining mining web web mining ppt data mining data snarfing web scraping open web crawler data scraping facebook web data scraping c spire web data scraping software mac data scraping data snarfing web scraping tool web crawler data scraping services web page data scraping software web scraping r tutorial data web crawler data scraping free web mining in data mining web mining versus data mining web page data scraping facebook web page data scraping tools web crawler data scraping programs data snarfing web scraping legal scraping data from the web web crawler data scraping software scraping web web data scraping software free data snarfing web scraping tutorial web crawler data scraping tools web data scraping c-section
Views: 1 Doizen Hota
What is DATA MINING? What does DATA MINING mean? DATA MINING meaning, definition & explanation
 
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What is DATA MINING? What does DATA MINING mean? DATA MINING meaning - DATA MINING definition - DATA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps. The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.
Views: 5325 The Audiopedia
Web Scraping Fun! Dexi.io Data Mining Big Data 2016 - Semalt
 
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Visit us - https://semalt.com/?ref=y #data, #data_and, #piechart_data, #sorteiotirolplus_data, #data_in, #data_transfer, #drawingconclusionsfromgraphicorganizers_data, #acabei_data, #mining, #fun, #scraping, #web, #big, #b_g web scraping data mining big data web scraping data mining vs web scraping data scraping data mining project data scraping vs data mining web data scraping web scraping data scraping web data big data web scraping free big data web scraping open big data web scraping tutorial big data web scraping python big data web scraping api big data web scraping software big data web scraping tool big data web scraping definition big data scraping big data web scraping program big data web scraping legal big data web scraping tools big data web scraping companies io data mining data scraping report mining data mining scraping software screen scraping data mining web scraping data extraction and web mining data mining web data web data data mining big data data mining data mining big data big data web scraping with r big data web scraping using python big data web scraping with python big data web scraping in r stars web data scraping web data scraping freeware web data scraping tutorial web data scraping service web data scraping services structured web data scraping php web data scraping web data scraping c# web data scraping legal web data scraping jobs campus web data scraping harvesting web data scraping scraping web page data web page data scraping web data scraping python sensor web data scraping data extraction web scraping 150mb web data scraping web data scraping excel web crawler data scraping web data scraping tool web scraping post data free web data scraping scraping data web page web data scraping php web data scraping tools web data scraping techniques web scraping data python fuzzy web data scraping web data scraping software web scraping data extraction web mining data mining io-data io-data io-data data web mining data mining web data mining web mining web data mining mining web data difference between web scraping and data mining data scraping scraping data mining big data big data mining difference between data scraping and data mining data mining data mining data mining data mining big data analytics data mining in big data data mining using big data big data in data mining data mining techniques big data big data data mining difference big data vs data mining data mining e big data big data e data mining big data with data mining data mining et big data data mining on big data mining data vs big data big data and data mining data mining for big data data mining vs big data data mining with big data big data data mining tools data mining and big data big data et data mining big data analytics data mining data mining mining data data mining data mining data data mining mining data scraping screen scraping data mining for fun free web data scraping software web data scraping c-130 scraping web data with excel web data scraping c thomas web page data scraping php web page data scraping wikipedia is web data scraping legalzoom data snarfing web scraping tools web crawler data scraping companies web data extraction scraping software data snarfing web scraping software web data scraping c-span web page data scraping free web data scraping c&a what is web data scraping is web data scraping legalization web data scraping open source web page data scraping services web scraping freeware mac data google earth web data scraping web crawler data scraping php web page data scraping programs ppt web mining data mining web data scraping c-diff web page data scraping r web data scraping c wonder webpy web input web data scraping is web data scraping legalize data extraction web scraping tool web page data scraping illegal web page data scraping tutorial web crawler data scraping legal web page data scraping legal web mining and data mining is web data scraping legal web data scraping c string data mining web mining education data snarfing web scraping api data snarfing web scraping companies data snarfing web scraping python scraping data web page excel scraping web data with r data mining i web mining web crawler data scraping tutorial web page data scraping companies web data scraping tools free data mining web mining pdf excel formulas web data scraping web crawler data scraping service data mining web mining ppt web mining vs data mining data mining vs web mining data mining and web mining
Views: 2 Ajay Kumar
The best stats you've ever seen | Hans Rosling
 
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http://www.ted.com With the drama and urgency of a sportscaster, statistics guru Hans Rosling uses an amazing new presentation tool, Gapminder, to present data that debunks several myths about world development. Rosling is professor of international health at Sweden's Karolinska Institute, and founder of Gapminder, a nonprofit that brings vital global data to life. (Recorded February 2006 in Monterey, CA.) TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes. TED stands for Technology, Entertainment, Design, and TEDTalks cover these topics as well as science, business, development and the arts. Closed captions and translated subtitles in a variety of languages are now available on TED.com, at http://www.ted.com/translate. Follow us on Twitter http://www.twitter.com/tednews Checkout our Facebook page for TED exclusives https://www.facebook.com/TED
Views: 2676856 TED
Build a Web Scraper (LIVE)
 
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In this video, we'll build a Python web scraper that retrieves the top 20 most frequent words with their percentages in an English Wikipedia article. Code for this video is here: https://github.com/llSourcell/web_scraper_live_demo Check out my friend Zoe Hong's Youtube channel for some cool fashion and illustration educational videos: https://www.youtube.com/channel/UCMQ_mPIBPi4IMpYEmuyOMqQ Please subscribe, comment, and like! That's what keeps me going. 2 more web scraping tutorials that are pretty good: http://web.stanford.edu/~zlotnick/TextAsData/Web_Scraping_with_Beautiful_Soup.html https://blog.miguelgrinberg.com/post/easy-web-scraping-with-python Let me know of what types of things you'd like me to code in the future for live sessions, always open to suggestions. And please support me on Patreon! https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 27252 Siraj Raval
How to Make a Text Summarizer - Intro to Deep Learning #10
 
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I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 131416 Siraj Raval
IBM Watson: How it Works
 
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Learn how IBM Watson works and has similar thought processes to a human. http://www.ibm.com/watson
Views: 1733206 IBM Watson
Weka Data Mining Tutorial for First Time & Beginner Users
 
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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 422063 Brandon Weinberg
Knowledge mining with Neuromation
 
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Democratizing access to the tools of Artificial Intelligence, generating synthetic data for deep learning applications, Neuromation puts to good use the mining power of the computers securing blockchain networks. In this conversation with Andrew Rabinovich, Director of Deep Learning at Magic Leap, David Orban discusses the implications of this approach for the development of innovative decentralized AI applications.
Views: 962 David Orban
Genesis Mining Wikipedia
 
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Genesis Mining Wikipedia Genesis-Mining: http://tinyurl.com/hij18bo47y85 Promo code HWvl6U Bitcoin is genuinely a world-wide currency which uses an open ledger technique to track record trades being submitted one person to another. Doing this happens without any central bank in the middle which is not operated by government, controlling body, individual business, or person. genesis mining twitter mining pool hashrate bitcoin how to trade it for serious profit pdf
Views: 33 neff ramey
Idea Mining with Federated Wiki
 
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A description of what we mean by collaborative journaling, and how journaling on wiki is different than capturing experience in other social media.
Views: 194 Mike Caulfield
Wikipedia Data Analysis Using SAP HANA One
 
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Analysis of Wikipedia Dump data using SAP HANA One -Created for Research Paper
Views: 262 Sharad Nadkarni
Linkedin Data Mining Made Easy!   FREE email list - Semalt
 
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Visit us - https://semalt.com/?ref=y Subscribe to get free educational videos here https://www.youtube.com/channel/UCBAjjiw53lUAm5YR7lgB4sQ?sub_confirmation=1 #list, #made, #mining, #linkedin, #l_nked_n, #email, #free, #data, #easy, #free_purchase, #nh_cbts_list, #mining_social, #data_and, #piechart_data data mining made easy data mining made easy pdf linkedin data mining mining linkedin data dynamic warehousing data mining made easy easy data mining free linkedin email list easy free data mining software email data list email list free free easy email list data mining email email data mining email data mining software free free email data mining software email data list email free email made easy mining email list list free data mining software free data mining tools list list free data mining tools data made easy bitcoin mining made easy bit mining made easy ccsu data mining linkedin data ccsu data mining linkedin imdb linkedin data mining mutanda mining linkedin data triple linkedin data mining geosparql linkedin data mining government linkedin data mining linkedin government data mining nhis linkedin data mining clustering linkedin data mining linkedin api data mining boteti mining linkedin data graffiti linkedin data mining lode linkedin data mining isbn linkedin data mining amara mining linkedin data wikidata linkedin data mining web linkedin data mining edubase linkedin data mining dynamically linkedin data mining licence linkedin data mining anonymised linkedin data mining hecla mining linkedin data bisha mining linkedin data lcsh linkedin data mining museum linkedin data mining diseasome linkedin data mining lundin mining linkedin data pelagios linkedin data mining geonovum linkedin data mining wikipedia linkedin data mining privacy linkedin data mining download linkedin data mining anvil mining linkedin data backup linkedin data mining data mining linkedin api adex mining linkedin data libraries linkedin data mining wiki linkedin data mining gemet linkedin data mining data mining linkedin profile kbl mining linkedin data karara mining linkedin data adani mining linkedin data ruashi mining linkedin data cae mining linkedin data opencorporates linkedin data mining mining data from linkedin conference linkedin data mining dereferenceable linkedin data mining linkedin data mining services linkedin data mining uses bodc linkedin data mining graph linkedin data mining mawarid mining linkedin data tso linkedin data mining easy data mining tools easy data mining exercise data mining easy notes easy data mining gmbh easy data mining projects easy data mining software easy data mining tool easy data mining example easy data mining wm linkedin email list email mining free data mining email interest email data mining tool data mining email university email marketing data mining yahoo email data mining data mining email zipcode b2c email data mining data mining email research email address data mining data mining email degree data mining email spam spam email data mining data mining email papers data free mining email data mining wikipedia data mining email students google email data mining google data mining email 247 email data mining facebook data mining email data mining email messages data mining email professor hashed email data mining outlook email data mining email data mining software data mining email marketing data mining email publications data mining email addresses free data mining college made easy linkedin data mining free easy email list list of free data mining tools data mining mining information business email data mining data mining data mining email data list email newsletters made easy encrypted email made easy easy email made marketing made easy email id email encryption made easy email etiquette made easy email copy made easy email signatures made easy email marketing made easy mobile email made easy easy data feed linkedin free made easy data structures made easy data visualization made easy geomancer data made easy data easy entry made data entry made easy data flows made easy made easy data structures data-entry-made-easy data made easy scam data recovery made easy data analysis made easy data enry made easy data migration made easy data collection made easy data quality made easy about data made easy big data made easy data easy feed made single list data mining data mining products list data mining conferences list data mining tool list data mining craigs list data mining software list
Views: 0 Anil Kumar
Google I/O 2012 - Knowledge-Based Application Design Patterns
 
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Shawn Simister In this talk we'll look at emerging design patterns for building web applications that take advantage of large-scale, structured data. We'll look at open datasets like Wikipedia and Freebase as well as structured markup like Schema.org and RDFa to see what new types of applications these technologies open up for developers. For all I/O 2012 sessions, go to https://developers.google.com/io/
Views: 7907 Google Developers
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: 81365 Shomu's Biology
Getting Wikipedia Tables into a JSON Format
 
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Can't find the data you need? Perhaps you're looking in the wrong place. Article from this video so you can follow along: http://en.wikipedia.org/wiki/List_of_U.S._state_abbreviations JSFiddle from the end of the video: http://jsfiddle.net/fE5Bw/
Views: 3132 aboutscript
Web Scraping With Python - Wikipedia Words Frequency Analysis Using Matplotlib
 
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Web Scraping With Python - Wikipedia Words Frequency Analysis Using Matplotlib
Views: 1669 Martin M
Install Maltego In Windows 10 | Forensic Hacking Tool | Digital Hacker
 
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Install Maltego In Windows 10 | Forensic Hacking Tool | Digital Hacker Maltego : From Wikipedia Maltego is proprietary software used for open-source intelligence and forensics, developed by Paterva. Maltego focuses on providing a library of transforms for discovery of data from open sources, and visualizing that information in a graph format, suitable for link analysis and data mining. Maltego permits creating custom entities, allowing it to represent any type of information in addition to the basic entity types which are part of the software. The basic focus of the application is analyzing real-world relationships between people, groups, websites, domains, networks, internet infrastructure, and affiliations with online services such as Twitter and Facebook. It is used by security researchers and private investigators. If any questions Ask me on Comment or Contact : Facebook : https://www.facebook.com/DigitalHack3r Twitter : https://twitter.com/Shehryar_DH Google+ : https://plus.google.com/+DigitalHacker Warning : This video is Educational purpose only. I am not responsible how you use this tool. Note : Copyright © 2017 by Digital Hacker All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the publisher.
Views: 1923 Digital Hacker
What is SOCIAL MEDIA MINING? What does SOCIAL MEDIA MINING mean? SOCIAL MEDIA MINING meaning
 
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What is SOCIAL MEDIA MINING? What does SOCIAL MEDIA MINING mean? SOCIAL MEDIA MINING meaning - SOCIAL MEDIA MINING definition - SOCIAL MEDIA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Social media mining is the process of representing, analyzing, and extracting actionable patterns and trends from raw social media data. The term "mining" is an analogy to the resource extraction process of mining for rare minerals. Resource extraction mining requires mining companies to sift through vast quanitites of raw ore to find the precious minerals; likewise, social media "mining" requires human data analysts and automated software programs to sift through massive amounts of raw social media data (e.g., on social media usage, online behaviours, sharing of content, connections between individuals, online buying behaviour, etc.) in order to discern patterns and trends. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as these organizations can use these patterns and trends to design their strategies or introduce new programs (or, for companies, new products, processes and services). Social media mining uses a range of basic concepts from computer science, data mining, machine learning and statistics. Social media miners develop algorithms suitable for investigating massive files of social media data. Social media mining is based on theories and methodologies from social network analysis, network science, sociology, ethnography, optimization and mathematics. It encompasses the tools to formally represent, measure, model, and mine meaningful patterns from large-scale social media data. In the 2010s, major corporations, as well as governments and not-for-profit organizations engage in social media mining to find out more about key populations of interest, which, depending on the organization carrying out the "mining", may be customers, clients, or citizens. As defined by Kaplan and Haenlein, social media is the "group of internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content." There are many categories of social media including, but not limited to, social networking (Facebook or LinkedIn), microblogging (Twitter), photo sharing (Flickr, Photobucket, or Picasa), news aggregation (Google reader, StumbleUpon, or Feedburner), video sharing (YouTube, MetaCafe), livecasting (Ustream or Twitch.tv), virtual worlds (Kaneva), social gaming (World of Warcraft), social search (Google, Bing, or Ask.com), and instant messaging (Google Talk, Skype, or Yahoo! messenger). The first social media website was introduced by GeoCities in 1994. It enabled users to create their own homepages without having a sophisticated knowledge of HTML coding. The first social networking site, SixDegree.com, was introduced in 1997. Since then, many other social media sites have been introduced, each providing service to millions of people. These individuals form a virtual world in which individuals (social atoms), entities (content, sites, etc.) and interactions (between individuals, between entities, between individuals and entities) coexist. Social norms and human behavior govern this virtual world. By understanding these social norms and models of human behavior and combining them with the observations and measurements of this virtual world, one can systematically analyze and mine social media. Social media mining is the process of representing, analyzing, and extracting meaningful patterns from data in social media, resulting from social interactions. It is an interdisciplinary field encompassing techniques from computer science, data mining, machine learning, social network analysis, network science, sociology, ethnography, statistics, optimization, and mathematics. Social media mining faces grand challenges such as the big data paradox, obtaining sufficient samples, the noise removal fallacy, and evaluation dilemma. Social media mining represents the virtual world of social media in a computable way, measures it, and designs models that can help us understand its interactions. In addition, social media mining provides necessary tools to mine this world for interesting patterns, analyze information diffusion, study influence and homophily, provide effective recommendations, and analyze novel social behavior in social media.
Views: 219 The Audiopedia
Wikigrabber - Tools To Get Wikipedia Links
 
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Wikigrabber - Tools To Get Wikipedia Links WikiGrabber - Tool To Get Links From Wikipedia Link Building is the core part Of Digital Marketing.In order to improve ranking in search engines we have to build quality links.Getting links from the paids sites or link farms is not recommended as it can impact negatively on your website. Do you know that we get links from wikipedia ? Yes ,You are Right I am talking about getting links From Wikipedia. I want to share a tool to get links from wikipedia .. The Name of Too is Wikigrabber. Go To http://wikigrabber.com Enter Your Keyword ( From Your Relevant Business ) Click On Search Button It will Show you wikipedia Anchor text Urls According to Your Keyword. Click On Dead Link Button. It will Show You the wikipedia Pages Having Dead Links Now Go To WikiPedia Page and search For Deadlinks. Now Insert Your Backlink. Thats Done . WikiGrabber - Tool To Get Links From Wikipedia
Views: 505 SeoCharcha
Enipedia-A Semantic Wiki for Energy and Industry Data
 
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Finalist Delft Innovation Award 2011
Views: 829 TU Delft
[Wikipedia] Logic Programming Associates
 
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Logic Programming Associates (LPA) is a company specializing in logic programming and artificial intelligence software. LPA was founded in 1980 and is widely known for its range of Prolog compilers and more recently for VisiRule. LPA was established to exploit research at Imperial College, London into logic programming carried out under the supervision of Prof Robert Kowalski. One of the first implementations made available by LPA was micro-PROLOG which ran on popular 8-bit home computers such as the Sinclair Spectrum and Apple II. This was followed by micro-PROLOG Professional one of the first Prolog implementations for MS-DOS. As well as continue with Prolog compiler technology development, LPA has a track record of creating innovative associated tools and products to address specific challenges and opportunities. In 1989, LPA developed the Flex expert system toolkit, which incorporated frame-based reasoning with inheritance, rule-based programming and data-driven procedures. Flex has its own English-like Knowledge Specification Language (KSL) which means that knowledge and rules are defined in an easy-to-read and understand way. In 1992, LPA helped set up the Prolog Vendors Group, a not-for-profit organization whose aim was to help promote Prolog by making people aware of its usage in industry. In 2000, LPA helped set up Business Integrity, now a leading supplier of document assembly and contract creation software solutions for the legal market. LPA's core product is LPA Prolog for Windows, a compiler and development system for the Microsoft Windows platform. The current LPA software range comprises an integrated AI toolset which covers various aspects of Artificial Intelligence including Logic Programming, Expert Systems, Knowledge-based Systems, Data Mining, Agents and Case-based reasoning etc. In 2004, LPA launched VisiRule a graphical tool for developing knowledge-based and decision support systems. VisiRule has been used in various sectors, to build legal expert systems, machine diagnostic programs, medical and financial advice systems, etc. https://en.wikipedia.org/wiki/Logic_Programming_Associates Please support this channel and help me upload more videos. Become one of my Patreons at https://www.patreon.com/user?u=3823907
Views: 3 WikiTubia
Social Network Analysis The Basics
 
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Explains the basic social network analysis vocabulary with a suggested reading list. Defines the Node and Link Condor MySQL tables for email, web, wikipedia, twitter, facebook and video databases. Duration: 15mins0secs. Links within video: http://moreno.ss.uci.edu/ http://moreno.ss.uci.edu/pubs.html http://www.amazon.com/dp/1594577145/ref=rdr_ext_tmb http://en.wikipedia.org/wiki/Centrality#Degree_centrality http://en.wikipedia.org/wiki/Dense_graph http://en.wikipedia.org/wiki/Bridge_(graph_theory) http://en.wikipedia.org/wiki/Social_network http://en.wikipedia.org/wiki/Betweenness#Betweenness_centrality http://www.insna.org/ http://coinsconference.org/ http://savannah09.coinsconference.org/ http://savannah10.coinsconference.org/ http://basel11.coinsconference.org http://galaxyadvisors.com/index.php Condor video page at: http://www.galaxyadvisors.com/science-of-swarms/condor-videos.html
Views: 14367 Ken Riopelle
Scrape Websites with Python + Beautiful Soup 4 + Requests -- Coding with Python
 
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Coding with Python -- Scrape Websites with Python + Beautiful Soup + Python Requests Scraping websites for data is often a great way to do research on any given idea. This tutorial takes you through the steps of using the Python libraries Beautiful Soup 4 (http://www.crummy.com/software/BeautifulSoup/bs4/doc/#) and Python Requests (http://docs.python-requests.org/en/latest/). Reference code available under "Actions" here: https://codingforentrepreneurs.com/projects/coding-python/scrape-beautiful-soup/ Coding for Python is a series of videos designed to help you better understand how to use python. Assumes basic knowledge of python. View all my videos: http://bit.ly/1a4Ienh Join our Newsletter: http://eepurl.com/NmMcr A few ways to learn Django, Python, Jquery, and more: Coding For Entrepreneurs: https://codingforentrepreneurs.com (includes free projects and free setup guides. All premium content is just $25/mo). Includes implementing Twitter Bootstrap 3, Stripe.com, django, south, pip, django registration, virtual environments, deployment, basic jquery, ajax, and much more. On Udemy: Bestselling Udemy Coding for Entrepreneurs Course: https://www.udemy.com/coding-for-entrepreneurs/?couponCode=youtubecfe49 (reg $99, this link $49) MatchMaker and Geolocator Course: https://www.udemy.com/coding-for-entrepreneurs-matchmaker-geolocator/?couponCode=youtubecfe39 (advanced course, reg $75, this link: $39) Marketplace & Dail Deals Course: https://www.udemy.com/coding-for-entrepreneurs-marketplace-daily-deals/?couponCode=youtubecfe39 (advanced course, reg $75, this link: $39) Free Udemy Course (80k+ students): https://www.udemy.com/coding-for-entrepreneurs-basic/ Fun Fact! This Course was Funded on Kickstarter: http://www.kickstarter.com/projects/jmitchel3/coding-for-entrepreneurs
Views: 394927 CodingEntrepreneurs
Intro to Count-Min Sketch in F#
 
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Let's build a simple count-min sketch to understand what they're all about. Wikipedia Page: https://en.wikipedia.org/wiki/Count%E2%80%93min_sketch Gist of Code: https://gist.github.com/mjgpy3/117f70413a9e065417f44035462951fe
Views: 772 Michael Gilliland
Don't Waste $1000 on Data Recovery
 
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Thanks to DeepSpar for sponsoring this video! Check out their RapidSpar Data Recovery Tool at http://geni.us/rapidspar RapidSpar is the first cloud-driven device built to help IT generalists and other non-specialized users recover client data from damaged or failing HDDs/SSDs Buy HDDs on Amazon: http://geni.us/sLlhDf Buy HDDs on Newegg: http://geni.us/a196 Linus Tech Tips merchandise at http://www.designbyhumans.com/shop/Linustechtips Linus Tech Tips posters at http://crowdmade.com/linustechtips Our Test Benches on Amazon: https://www.amazon.com/shop/linustechtips Our production gear: http://geni.us/cvOS Twitter - https://twitter.com/linustech Facebook - http://www.facebook.com/LinusTech Instagram - https://www.instagram.com/linustech Twitch - https://www.twitch.tv/linustech Intro Screen Music Credit: Title: Laszlo - Supernova Video Link: https://www.youtube.com/watch?v=PKfxm... iTunes Download Link: https://itunes.apple.com/us/album/sup... Artist Link: https://soundcloud.com/laszlomusic Outro Screen Music Credit: Approaching Nirvana - Sugar High http://www.youtube.com/approachingnir... Sound effects provided by http://www.freesfx.co.uk/sfx/
Views: 1327435 Linus Tech Tips
Web data extractor & data mining- Handling Large Web site Item | Excel data Reseller & Dropship
 
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Web scraping web data extractor is a powerful data, link, url, email tool popular utility for internet marketing, mailing list management, site promotion and 2 discover extractor, the scraper that captures alternative from any website social media sites, or content area on if you are interested fully managed extraction service, then check out promptcloud's services. Use casesweb data extractor extracting and parsing github wanghaisheng awesome web a curated list webextractor360 open source codeplex archive. It uses regular expressions to find, extract and scrape internet data quickly easily. Whether seeking urls, phone numbers, 21 web data extractor is a scraping tool specifically designed for mass gathering of various types. Web scraping web data extractor extract email, url, meta tag, phone, fax from download. Web data extractor pro 3. It can be a url, meta tags with title, desc and 7. Extract url, meta tag (title, desc, keyword), body text, email, phone, fax from web site, search 27 data extractor can extract of different kind a given website. Web data extraction fminer. 1 (64 bit hidden web data extractor semantic scholar. It is very web data extractor pro a scraping tool specifically designed for mass gathering of various types. The software can harvest urls, extracting and parsing structured data with jquery selector, xpath or jsonpath from common web format like html, xml json a curated list of promising extractors resources webextractor360 is free open source extractor. It scours the internet finding and extracting all relative. Download the latest version of web data extractor free in english on how to use pro vimeo. It can harvest urls, web data extractor a powerful link utility. A powerful web data link extractor utility extract meta tag title desc keyword body text email phone fax from site search results or list of urls high page 1komal tanejashri ram college engineering, palwal gandhi1211 gmail mdu rohtak with extraction, you choose the content are looking for and program does rest. Web data extractor free download for windows 10, 7, 8. Custom crawling 27 2011 web data extractor promises to give users the power remove any important from a site. A deep dive into natural language processing (nlp) web data mining is divided three major groups content mining, structure and usage. Web mining wikipedia web is the application of data techniques to discover patterns from world wide. This survey paper reports the basic web mining aims to discover useful information or knowledge from hyperlink structure, page, and usage data. Web data mining, 2nd edition exploring hyperlinks, contents, and web mining not just on the software advice. Data mining in web applications. Web data mining exploring hyperlinks, contents, and usage in web applications what is mining? Definition from whatis searchcrm. Web data mining and applications in business intelligence web humboldt universitt zu berlin. Web mining aims to dis cover useful data and web are not the same thing. Extracting the rapid growth of web in past two decades has made it larg est publicly accessible data source world. Web mining wikipedia. The web is one of the biggest data sources to serve as input for mining applications. Web data mining exploring hyperlinks, contents, and usage web mining, book by bing liu uic computer sciencewhat is mining? Definition from techopedia. Most useful difference between data mining vs web. As the name proposes, this is information gathered by web mining aims to discover useful and knowledge from hyperlinks, page contents, usage data. Although web mining uses many is the process of using data techniques and algorithms to extract information directly from by extracting it documents 19 that are generated systems. Web data mining is based on ir, machine learning (ml), statistics web exploring hyperlinks, contents, and usage (data centric systems applications) [bing liu] amazon. Based on the primary kind of data used in mining process, web aims to discover useful information and knowledge from hyperlinks, page contents, usage. Data mining world wide web tutorialspoint.
Views: 232 CyberScrap youpul
Social media data mining for counter-terrorism | Wassim Zoghlami | TEDxMünster
 
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Using public social media data from twitter and Facebook, actions and announcements of terrorists – in this case ISIS – can be monitored and even be predicted. With his project #DataShield Wassim shares his idea of having a tool to identify oncoming threats and attacks in order to protect people and to induce preventive actions. Wassim Zoghlami is a Tunisian Computer Engineering Senior focussing on Business Intelligence and ERP with a passion for data science, software life cycle and UX. Wassim is also an award winning serial entrepreneur working on startups in healthcare and prevention solutions in both Tunisia and The United States. During the past years Wassim has been working on different projects and campaigns about using data driven technology to help people working to uphold human rights and to promote civic engagement and culture across Tunisia and the MENA region. He is also the co-founder of the Tunisian Center for Civic Engagement, a strong advocate for open access to research, open data and open educational resources and one of the Global Shapers in Tunis. At TEDxMünster Wassim will talk about public social media data mining for counter-terrorism and his project idea DataShield. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 1737 TEDx Talks
Analyzing Text Data with R on Windows
 
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Provides introduction to text mining with r on a Windows computer. Text analytics related topics include: - reading txt or csv file - cleaning of text data - creating term document matrix - making wordcloud and barplots. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 8257 Bharatendra Rai
Text/Data Mining, Libraries, and Online Publishers
 
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As more researchers embrace text- and data-mining methodologies, publishers must provide flexible and workable terms and utilities as they surmount legal and technological barriers to the new practices. This July 2013 webinar featured updates on the latest industry developments, with speakers from the journal publishing world, including: Eefke Smit, Director of Standards and Technology, STM, "Content Mining:A Short Introduction to Practices and Policies"; Carol Anne Meyer, Business Development and Marketing, CrossRef, "Prospect by CrossRef"; and Mark Seeley, Senior Vice President and General Counsel, Elsevier, "Enabling TDM: Contract Forms" This webinar was presented in cooperation with STM (International Association of Scientific, Technical & Medical Publishers) and ALPSP (Association of Learned and Professional Society Publishers).
Views: 1130 CRLdotEDU
Facebook text analysis on R
 
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For more information, please visit http://web.ics.purdue.edu/~jinsuh/.
Views: 11121 Jinsuh Lee
Web scraping and parsing with Beautiful Soup & Python Introduction p.1
 
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Welcome to a tutorial on web scraping with Beautiful Soup 4. Beautiful Soup is a Python library aimed at helping programmers https://i9.ytimg.com/vi/aIPqt-OdmS0/0.jpg?sqp=CMTBuMAF&rs=AOn4CLCCdxLaQ0UDTyvhX3N87Txa2iGDZQ&time=1477320913969who are trying to scrape data from websites. To use beautiful soup, you need to install it: $ pip install beautifulsoup4. Beautiful Soup also relies on a parser, the default is lxml. You may already have it, but you should check (open IDLE and attempt to import lxml). If not, do: $ pip install lxml or $ apt-get install python-lxml. To begin, we need HTML. I have created an example page for us to work with: https://pythonprogramming.net/parsememcparseface/ Tutorial code: https://pythonprogramming.net/introduction-scraping-parsing-beautiful-soup-tutorial/ Beautiful Soup 4 documentation: https://www.crummy.com/software/BeautifulSoup/bs4/doc/ https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 166394 sentdex
Data science / Data scientist jobs / courses
 
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Subscribe to see more. How to Be a Data Scientist: Data Science Skill Development : In general terms, Data Science is the extraction of knowledge from large volumes of data that aren't structured, which is a continuation of the field data mining and predictive analytics, also known as knowledge discovery and data mining (KDD). Technology industry is a great career option, no matter how you look at it: Interesting work, high salaries and lots of opportunity. The data used by a Data Scientist can be both structured (metrics or raw numbers) and unstructured (e-mails, images, videos, or social data). The emerging Data Scientist needs to develop business domain knowledge. Frequently, candidates with stellar academic records fail on the job because they fail to apply their knowledge in real-world situations. Candidates must demonstrate quick aptitude for Data Science tools like R, Python, Hadoop, or SAS. Successful Data Scientists are usually convincing story tellers. They ought to be able to communicate the “story” hidden behind their findings. Visualization eases understanding. As Data Scientists often have to create algorithms to extract insights from such complex data, these “data magicians” are expected to be equipped with a variety of skills and experience levels. A Data scientist requires comprehensive mastery of a number of fields, such as software development, data munging, databases, statistics, machine learning and data visualization. Your job might consist of tasks like pulling data out of MySQL databases, becoming a master at Excel pivot tables, and producing basic data visualizations (e.g., line and bar charts). Data Scientists enjoy a median salary of $113,000. You may on occasion analyze the results of an A/B test or take the lead on your company’s Google Analytics account. A company like this is a great place for an aspiring data scientist to learn the ropes. Once you have a handle on your day-to-day responsibilities, a company like this can be a great environment to try new things and expand your skillset. A data scientist with a software engineering background might excel at a company, where it’s more important that a data scientist make meaningful data-like contributions to the production code and provide basic insights and analyses. Data Skills to Get You Hired : 1. Basic Tools 2. Basic Statistics 3. Machine Learning 4. Multivariable Calculus and Linear Algebra 5. Data Munging 6. Data Visualization & Communication 7. Software Engineering 8. Thinking Like A Data Scientist 9. Guide to Python and R, and which one is best Data scientists around the world are presented with exciting problems to solve. Within the complex questions they have to ask, a growing mountain of data rests a set of insights that can change entire industries. In order to get there, data scientists often rely on programming languages and tools. Python : Python is a versatile programming language that can do everything from data mining to plotting graphs. Its design philosophy is based on the importance of readability and simplicity. From the The Zen of Python: Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. As you can imagine, algorithms in Python are designed to be easy to read and write. Blocks of Python code are separated by indentations. Within each block, you’ll discover a syntax that wouldn’t be out of place in a technical handbook. Collect raw data Python supports all kinds of different data formats. You can play with comma-separated value documents (known as CSVs) or you can play with JSON sourced from the web. You can import SQL tables directly into your code. You can also create datasets. The Python requests library is a beautiful piece of work that allows you to take data from different websites with a line of code. It simplifies HTTP requests into a line of code. You’ll be able to take data from Wikipedia tables, and once you’ve organized the data you get with beautifulsoup, you’ll be able to analyze them in-depth. You can get any kind of data with Python. If you’re ever stuck, google Python and the dataset you’re looking for to get a solution. Also know about Python vs R and its Benefits. http://thenextweb.com/dd/2016/04/08/start-using-python-andor-r-data-science-one-best/#gref Thus, advanced analytics and Machine Learning skills are high on any industry leader’s list of wanted skills when looking for a Data Scientist to hire. The future business environment will also expect more speed of execution with Data Science projects, so future Data Scientists will not only be expected to have traditional math, statistics, and Machine Learning skills, but also sound knowledge of and experience in using data productivity tools, such as those to automate data cleaning or data modeling.
Views: 2954 Google Trends
What is DATA CLEANSING? What does DATA CLEANSING mean? DATA CLEANSING meaning & explanation
 
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What is DATA CLEANSING? What does DATA CLEANSING mean? DATA CLEANSING meaning - DATA CLEANSING definition - DATA CLEANSING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting. After cleansing, a data set should be consistent with other similar data sets in the system. The inconsistencies detected or removed may have been originally caused by user entry errors, by corruption in transmission or storage, or by different data dictionary definitions of similar entities in different stores. Data cleansing differs from data validation in that validation almost invariably means data is rejected from the system at entry and is performed at the time of entry, rather than on batches of data. The actual process of data cleansing may involve removing typographical errors or validating and correcting values against a known list of entities. The validation may be strict (such as rejecting any address that does not have a valid postal code) or fuzzy (such as correcting records that partially match existing, known records). Some data cleansing solutions will clean data by cross checking with a validated data set. A common data cleansing practice is data enhancement, where data is made more complete by adding related information. For example, appending addresses with any phone numbers related to that address. Data cleansing may also involve activities like, harmonization of data, and standardization of data. For example, harmonization of short codes (st, rd, etc.) to actual words (street, road, etcetera). Standardization of data is a means of changing a reference data set to a new standard, ex, use of standard codes. Administratively, incorrect or inconsistent data can lead to false conclusions and misdirected investments on both public and private scales. For instance, the government may want to analyze population census figures to decide which regions require further spending and investment on infrastructure and services. In this case, it will be important to have access to reliable data to avoid erroneous fiscal decisions. In the business world, incorrect data can be costly. Many companies use customer information databases that record data like contact information, addresses, and preferences. For instance, if the addresses are inconsistent, the company will suffer the cost of resending mail or even losing customers. The profession of forensic accounting and fraud investigating uses data cleansing in preparing its data and is typically done before data is sent to a data warehouse for further investigation. There are packages available so you can cleanse/wash address data while you enter it into your system. This is normally done via an application programming interface (API)......
Views: 3515 The Audiopedia
What is CLUSTER ANALYSIS? What does CLUSTER ANALYSIS mean? CLUSTER ANALYSIS meaning & explanation
 
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What is CLUSTER ANALYSIS? What does CLUSTER ANALYSIS mean? CLUSTER ANALYSIS meaning - CLUSTER ANALYSIS definition - CLUSTER ANALYSIS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics. Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It is often necessary to modify data preprocessing and model parameters until the result achieves the desired properties. Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek ß????? "grape") and typological analysis. The subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. This often leads to misunderstandings between researchers coming from the fields of data mining and machine learning, since they use the same terms and often the same algorithms, but have different goals. Cluster analysis was originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology by Zubin in 1938 and Robert Tryon in 1939 and famously used by Cattell beginning in 1943 for trait theory classification in personality psychology.
Views: 5571 The Audiopedia
Big Data and Hadoop Developer 2018 | Big Data as Career Path | Introduction to Big Data and Hadoop
 
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https://acadgild.com/big-data/big-data-development-training-certification/ Big Data and Hadoop Developer 2018 | Big Data as Career Path | Introduction to Big Data and Hadoop Big Data is growing explosively bigger & bigger every day. Get to know what makes Big Data the next big thing. What is Big Data all about? Big Data has been described in multiple ways by the industry experts. Let’s have a look at what Wikipedia has to say. Big Data is a term for datasets that are so large or complex that traditional data processing applications are inadequate. To put it in simple words, Big Data is the large volumes of structured and unstructured data. Did You Know? • According to Wikibon and IDC, 2.4 quintillion bits are generated every day? • Did you know, the data from our digital world will grow from 4.4 trillion gigabytes in 2013 to 44 trillion gigabytes in 2020? • In addition to that, data from embedded systems will grow from 2% in 2013 to 10% in 2020 The sheer volume of the data generated these days has made it absolutely necessary to re-think how we handle them. And with growing implementation of Big Data in sectors like banking, logistics, retail, e-commerce and social media, the volume of data is expected to grow even higher. Other than its Sheer size, what else makes Big data so important? • Mountains of data that can be gathered and analyzed to discover insights ad make better decisions. • Using Big data, social media comments can be analyzed in a far timelier and relevant manner offering a richer data set. • With Big Data, banks can now use information to constantly monitor their client’s transaction behaviors in real time. • Big Data is used for trade analytics, pre-trade decision-support analytics, sentiment measurement, predictive Analytics etc. • Organizations in media and entertainment industry use Big Data to analyze customer data along with behavioral data to create detailed customer profiles. • In the manufacturing and natural resources industry, Big Data allows for predictive modeling to support decision making. What is Big Data and its importance in the near future Gartner analyst Doug Laney introduced the 3Vs concept in 2001. Since then, Big Data has been further classified, based on the 5Vs. • Volume – The vast amounts of data generated every second. • Variety – The different types of data which contribute to the problem, such as text, videos, Images, audio files etc. • Velocity – The speed at which new data is generated and moves around. • Value – Having an access to Big Data in no good unless we turn it into value • Veracity - Refers to the messiness or trustworthiness of the data. Why is Big Data considered as an excellent career path? According to IDC forecast, the Big Data market is predicted to be worth of $46.34 billion by 2018 and is expected to have a sturdy growth over the next five years. Big Data Salary: As per Indeed, the average salary for Big Data professionals is about 114,000 USD per annum, which is around 98% higher than average salaries for all job postings nationwide. And Glassdoor quotes the median salary for Big Data professionals to be 104,850 USD per annum. Big Data professionals get a high percentage of hike in salary and Data scientists get a very good hike up to 8.90% YOY. As the Big Data market grows, so does the demand for the skilled workforce? According to Wanted Analytics, the demand for Big Data skills is to increase by 118% over the previous year. Do you need more reasons to believe in the power of Big Data? EMC, IBM, Cisco, Oracle, Adobe, Amazon, Accenture are just a few of the top companies who are constantly looking for Big Data skills. With the right training and hands-on experience, you too can find your dream career in one of these top companies. The path to your dream job is no longer a mystery. Sign up now & get started with your dream career. For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 46255 ACADGILD
What is STRUCTURE MINING? What does STRUCTURE MINING mean? STRUCTURE MINING meaning & explanation
 
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What is STRUCTURE MINING? What does STRUCTURE MINING mean? STRUCTURE MINING meaning - STRUCTURE MINING definition - STRUCTURE MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Structure mining or structured data mining is the process of finding and extracting useful information from semi-structured data sets. Graph mining, sequential pattern mining and molecule mining are special cases of structured data mining. The growth of the use of semi-structured data has created new opportunities for data mining, which has traditionally been concerned with tabular data sets, reflecting the strong association between data mining and relational databases. Much of the world's interesting and mineable data does not easily fold into relational databases, though a generation of software engineers have been trained to believe this was the only way to handle data, and data mining algorithms have generally been developed only to cope with tabular data. XML, being the most frequent way of representing semi-structured data, is able to represent both tabular data and arbitrary trees. Any particular representation of data to be exchanged between two applications in XML is normally described by a schema often written in XSD. Practical examples of such schemata, for instance NewsML, are normally very sophisticated, containing multiple optional subtrees, used for representing special case data. Frequently around 90% of a schema is concerned with the definition of these optional data items and sub-trees. Messages and data, therefore, that are transmitted or encoded using XML and that conform to the same schema are liable to contain very different data depending on what is being transmitted. Such data presents large problems for conventional data mining. Two messages that conform to the same schema may have little data in common. Building a training set from such data means that if one were to try to format it as tabular data for conventional data mining, large sections of the tables would or could be empty. There is a tacit assumption made in the design of most data mining algorithms that the data presented will be complete. The other necessity is that the actual mining algorithms employed, whether supervised or unsupervised, must be able to handle sparse data. Namely, machine learning algorithms perform badly with incomplete data sets where only part of the information is supplied. For instance methods based on neural networks. or Ross Quinlan's ID3 algorithm. are highly accurate with good and representative samples of the problem, but perform badly with biased data. Most of times better model presentation with more careful and unbiased representation of input and output is enough. A particularly relevant area where finding the appropriate structure and model is the key issue is text mining. XPath is the standard mechanism used to refer to nodes and data items within XML. It has similarities to standard techniques for navigating directory hierarchies used in operating systems user interfaces. To data and structure mine XML data of any form, at least two extensions are required to conventional data mining. These are the ability to associate an XPath statement with any data pattern and sub statements with each data node in the data pattern, and the ability to mine the presence and count of any node or set of nodes within the document. As an example, if one were to represent a family tree in XML, using these extensions one could create a data set containing all the individuals in the tree, data items such as name and age at death, and counts of related nodes, such as number of children. More sophisticated searches could extract data such as grandparents' lifespans etc. The addition of these data types related to the structure of a document or message facilitates structure mining.
Views: 250 The Audiopedia
What is UNSTRUCTURED DATA? What does UNSTRUCTURED DATA mean? UNSTRUCTURED DATA meaning
 
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What is UNSTRUCTURED DATA? What does UNSTRUCTURED DATA mean? UNSTRUCTURED DATA meaning - UNSTRUCTURED DATA definition - UNSTRUCTURED DATA explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Unstructured data (or unstructured information) is information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well. This results in irregularities and ambiguities that make it difficult to understand using traditional programs as compared to data stored in fielded form in databases or annotated (semantically tagged) in documents. In 1998, Merrill Lynch cited a rule of thumb that somewhere around 80-90% of all potentially usable business information may originate in unstructured form. This rule of thumb is not based on primary or any quantitative research, but nonetheless is accepted by some. IDC and EMC project that data will grow to 40 zettabytes by 2020, resulting in a 50-fold growth from the beginning of 2010. The Computer World magazine states that unstructured information might account for more than 70%–80% of all data in organizations. The term is imprecise for several reasons: 1. Structure, while not formally defined, can still be implied. 2. Data with some form of structure may still be characterized as unstructured if its structure is not helpful for the processing task at hand. 3. Unstructured information might have some structure (semi-structured) or even be highly structured but in ways that are unanticipated or unannounced. Techniques such as data mining, natural language processing (NLP), and text analytics provide different methods to find patterns in, or otherwise interpret, this information. Common techniques for structuring text usually involve manual tagging with metadata or part-of-speech tagging for further text mining-based structuring. The Unstructured Information Management Architecture (UIMA) standard provided a common framework for processing this information to extract meaning and create structured data about the information. Software that creates machine-processable structure can utilize the linguistic, auditory, and visual structure that exist in all forms of human communication. Algorithms can infer this inherent structure from text, for instance, by examining word morphology, sentence syntax, and other small- and large-scale patterns. Unstructured information can then be enriched and tagged to address ambiguities and relevancy-based techniques then used to facilitate search and discovery. Examples of "unstructured data" may include books, journals, documents, metadata, health records, audio, video, analog data, images, files, and unstructured text such as the body of an e-mail message, Web page, or word-processor document. While the main content being conveyed does not have a defined structure, it generally comes packaged in objects (e.g. in files or documents, …) that themselves have structure and are thus a mix of structured and unstructured data, but collectively this is still referred to as "unstructured data". For example, an HTML web page is tagged, but HTML mark-up typically serves solely for rendering. It does not capture the meaning or function of tagged elements in ways that support automated processing of the information content of the page. XHTML tagging does allow machine processing of elements, although it typically does not capture or convey the semantic meaning of tagged terms. Since unstructured data commonly occurs in electronic documents, the use of a content or document management system which can categorize entire documents is often preferred over data transfer and manipulation from within the documents. Document management thus provides the means to convey structure onto document collections. Search engines have become popular tools for indexing and searching through such data, especially text.....
Views: 597 The Audiopedia
What is OLAP?
 
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This video explores some of OLAP's history, and where this solution might be applicable. We also look at situations where OLAP might not be a fit. Additionally, we investigate an alternative/complement called a Relational Dimensional Model. To Talk with a Specialist go to: http://www.intricity.com/intricity101/
Views: 356035 Intricity101
Web Scraping - How to dedude data - Semalt
 
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Visit us - https://semalt.com/?ref=y Subscribe to get free educational videos here https://www.youtube.com/channel/UCBAjjiw53lUAm5YR7lgB4sQ?sub_confirmation=1 #how_to, #scraping, #web, #how, #to, #data, #mirrolure_web, #web_visita, #data_and, #cqajoschua_web, #piechart_data, #bjp_to, #web_s, #sorteiotirolplus_data web data scraping web scraping data scraping web data web scraping how to how to web scraping stars web data scraping web data scraping freeware web data scraping tutorial web data scraping service web data scraping services structured web data scraping php web data scraping web data scraping c# web data scraping jobs web data scraping legal big data web scraping web data scraping python campus web data scraping harvesting web data scraping scraping web page data web page data scraping sensor web data scraping data extraction web scraping 150mb web data scraping web data scraping excel web crawler data scraping web data scraping tool web scraping post data free web data scraping scraping data web page web data scraping php web scraping data mining web data scraping techniques web scraping data python web data scraping tools fuzzy web data scraping web data scraping software web scraping data extraction how to stop web scraping how to prevent web scraping dedude how to learn web scraping how to do data scraping how to do web scraping web data scraping c to f webpy web input web data scraping how web scraping works web scraping free web data scraping software big data web scraping free web data scraping c-130 big data web scraping open scraping web data with excel big data web scraping tutorial web data scraping c thomas web page data scraping php web page data scraping wikipedia is web data scraping legalzoom big data web scraping python data snarfing web scraping tools web crawler data scraping companies web data extraction scraping software data mining vs web scraping web page data scraping free data snarfing web scraping software web data scraping c-span web data scraping c&a what is web data scraping is web data scraping legalization web data scraping open source web page data scraping services web scraping freeware mac data big data web scraping software web crawler data scraping php web page data scraping programs google earth web data scraping big data web scraping api big data web scraping tool web data scraping c wonder web data scraping c-diff web page data scraping r web page data scraping legal is web data scraping legalize data extraction web scraping tool web page data scraping illegal web page data scraping tutorial web crawler data scraping legal is web data scraping legal web data scraping c string data snarfing web scraping companies data snarfing web scraping python big data web scraping program scraping data web page excel scraping web data with r data snarfing web scraping api big data web scraping definition web crawler data scraping tutorial web page data scraping companies web data scraping tools free web-scraping excel formulas web data scraping big data web scraping legal web crawler data scraping service data snarfing web scraping definition data snarfing web scraping free big data web scraping tools web page data scraping service data snarfing web scraping open web crawler data scraping facebook web data scraping c spire web data scraping software mac data scraping data snarfing web scraping tool web crawler data scraping services web page data scraping software web scraping r tutorial data web crawler data scraping free web page data scraping facebook web page data scraping tools web crawler data scraping programs data snarfing web scraping legal scraping data from the web web crawler data scraping software scraping web web data scraping software free data snarfing web scraping tutorial web crawler data scraping tools web data scraping c-section free web data scraping tool data extraction web screen scraping data snarfing web scraping program scraping the web for data scraping web data into excel scraping web data with python web crawler data scraping wikipedia big data web scraping companies scraping data from web pages web crawler data scraping r web crawler data scraping illegal scraping data web scraping screen scraping data scraping screen scraping scraping how to web scraping to rss intro to web scraping introduction to web scraping guide to web scraping web scraping to excel dedude mikrotik web scraping data extraction and web mining how does web scraping work
Views: 2 Mirza Bilal
Web Scraping New Zealand's commercial landings data - Semalt
 
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Visit us - https://semalt.com/?ref=y Subscribe to get free educational videos here https://www.youtube.com/channel/UCBAjjiw53lUAm5YR7lgB4sQ?sub_confirmation=1 #commercial, #landings, #scraping, #new, #web, #data, #mirrolure_web, #web_visita, #data_and, #cqajoschua_web, #piechart_data, #web_s, #sorteiotirolplus_data, #data_in web data scraping web scraping data scraping web data commercial landings data stars web data scraping web data scraping freeware web data scraping tutorial web data scraping service web data scraping services structured web data scraping php web data scraping web data scraping c# web data scraping jobs web data scraping legal big data web scraping web data scraping python campus web data scraping harvesting web data scraping scraping web page data web page data scraping sensor web data scraping data extraction web scraping 150mb web data scraping web data scraping excel web crawler data scraping web data scraping tool web scraping post data free web data scraping scraping data web page web data scraping php web scraping data mining web data scraping techniques web scraping data python web data scraping tools fuzzy web data scraping web data scraping software web scraping data extraction noaa commercial landings data commercial fisheries landings data nmfs commercial landings data webpy web input web data scraping web scraping free web data scraping software big data web scraping free web data scraping c-130 big data web scraping open scraping web data with excel big data web scraping tutorial web data scraping c thomas web page data scraping php web page data scraping wikipedia is web data scraping legalzoom big data web scraping python data snarfing web scraping tools web crawler data scraping companies web data extraction scraping software data mining vs web scraping web page data scraping free data snarfing web scraping software web data scraping c-span web data scraping c&a what is web data scraping is web data scraping legalization web data scraping open source web page data scraping services web scraping freeware mac data big data web scraping software web crawler data scraping php web page data scraping programs google earth web data scraping big data web scraping api big data web scraping tool web data scraping c wonder web data scraping c-diff web page data scraping r web page data scraping legal is web data scraping legalize data extraction web scraping tool web page data scraping illegal web page data scraping tutorial web crawler data scraping legal is web data scraping legal web data scraping c string data snarfing web scraping companies data snarfing web scraping python big data web scraping program scraping data web page excel scraping web data with r data snarfing web scraping api big data web scraping definition web crawler data scraping tutorial web page data scraping companies web data scraping tools free web-scraping excel formulas web data scraping big data web scraping legal web crawler data scraping service data snarfing web scraping definition data snarfing web scraping free big data web scraping tools web page data scraping service data snarfing web scraping open web crawler data scraping facebook web data scraping c spire web data scraping software mac data scraping data snarfing web scraping tool web crawler data scraping services web page data scraping software web scraping r tutorial data web crawler data scraping free web page data scraping facebook web page data scraping tools web crawler data scraping programs data snarfing web scraping legal scraping data from the web web crawler data scraping software scraping web web data scraping software free data snarfing web scraping tutorial web crawler data scraping tools web data scraping c-section free web data scraping tool data extraction web screen scraping data snarfing web scraping program scraping the web for data scraping web data into excel scraping web data with python web crawler data scraping wikipedia big data web scraping companies scraping data from web pages web crawler data scraping r web crawler data scraping illegal scraping data web scraping screen scraping nmfs commercial landings data bases data scraping screen scraping nmfs commercial fishery landings data nmfs commercial landings data aviation commercial landings landings data web scraping data extraction and web mining web scraping vs screen scraping screen scraping vs web scraping web page data scraping in php
Views: 0 marianne235
Unstructured Data for Finance
 
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Financial analysis techniques for studying numeric, well structured data are very mature. While using unstructured data in finance is not necessarily a new idea, the area is still very greenfield. On this episode,Delia Rusu shares her thoughts on the potential of unstructured data and discusses her work analyzing Wikipedia to help inform financial decisions. Delia's talk at PyData Berlin can be watched on Youtube (Estimating stock price correlations using Wikipedia). The slides can be found here and all related code is available on github.
Views: 134 Data Skeptic
What is DATA VISUALIZATION? What does DATA VISUALIZATION mean? DATA VISUALIZATION meaning
 
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What is DATA VISUALIZATION? What does DATA VISUALIZATION mean? DATA VISUALIZATION meaning - DATA VISUALIZATION definition - DATA VISUALIZATION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Data visualization or data visualisation is viewed by many disciplines as a modern equivalent of visual communication. It involves the creation and study of the visual representation of data, meaning "information that has been abstracted in some schematic form, including attributes or variables for the units of information". A primary goal of data visualization is to communicate information clearly and efficiently via statistical graphics, plots and information graphics. Numerical data may be encoded using dots, lines, or bars, to visually communicate a quantitative message. Effective visualization helps users analyze and reason about data and evidence. It makes complex data more accessible, understandable and usable. Users may have particular analytical tasks, such as making comparisons or understanding causality, and the design principle of the graphic (i.e., showing comparisons or showing causality) follows the task. Tables are generally used where users will look up a specific measurement, while charts of various types are used to show patterns or relationships in the data for one or more variables. Data visualization is both an art and a science. It is viewed as a branch of descriptive statistics by some, but also as a grounded theory development tool by others. The rate at which data is generated has increased. Data created by internet activity and an expanding number of sensors in the environment, such as satellites, are referred to as "Big Data". Processing, analyzing and communicating this data present a variety of ethical and analytical challenges for data visualization. The field of data science and practitioners called data scientists have emerged to help address this challenge. Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines or bars) contained in graphics. The goal is to communicate information clearly and efficiently to users. It is one of the steps in data analysis or data science. According to Friedman (2008) the "main goal of data visualization is to communicate information clearly and effectively through graphical means. It doesn't mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex data set by communicating its key-aspects in a more intuitive way. Yet designers often fail to achieve a balance between form and function, creating gorgeous data visualizations which fail to serve their main purpose — to communicate information". Indeed, Fernanda Viegas and Martin M. Wattenberg have suggested that an ideal visualization should not only communicate clearly, but stimulate viewer engagement and attention. Not limited to the communication of an information, a well-crafted data visualization is also a way to a better understanding of the data (in a data-driven research perspective), as it helps uncover trends, realize insights, explore sources, and tell stories. Data visualization is closely related to information graphics, information visualization, scientific visualization, exploratory data analysis and statistical graphics. In the new millennium, data visualization has become an active area of research, teaching and development. According to Post et al. (2002), it has united scientific and information visualization.
Views: 2272 The Audiopedia
The Password Manager Special: Passwords, Two Factor Authentication, and Securing Your Life Online!
 
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------- Support us: http://www.patreon.com/tekthing Amazon Associates: http://amzn.to/1OTcDZn Subscribe: https://www.youtube.com/c/tekthing Website: http://www.tekthing.com RSS: http://feeds.feedburner.com/tekthing THANKS! Hak5!: http://hak5.org/ HakShop: https://hakshop.myshopify.com/ SOCIAL IT UP! Twitter: https://twitter.com/tekthing Facebook: https://www.facebook.com/TekThing Google+: https://plus.google.com/+Tekthing/ Reddit: https://www.reddit.com/r/tekthingers EMAIL US! [email protected] ------- 01:10 - Passwords! Theme episode! The theme is passwords. And password managers. And Two Factor Authentication... we're gonna talk about 'em all, some great rules for using 'em, sharing 'em, and more, 'cause all of these things work together to protect you, your data, your fiances, your Snapchat, Facebook, and Twitter accounts. https://en.wikipedia.org/wiki/Password https://en.wikipedia.org/wiki/Password_manager https://en.wikipedia.org/wiki/Two-factor_authentication 02:54 - What makes a good password? The ever so awesome KrebsOnSecurity blog has as good a set of rules as any... we talk you through 'em, why they mean most folks should use a password manager and more in the show! http://krebsonsecurity.com/password-dos-and-donts/ 07:45 - Write Down Your Passwords?!? Security pros like Bruce Schneier have been saying for over decade that you should write down your passwords and keep 'em hidden, say, in your wallet, if you can't remember complex enough passwords... is this crazy? We talk it out in the video. https://www.schneier.com/blog/archives/2005/06/write_down_your.html 11:20 - Password Managers LastPass, KeePass, Dashlane, 1Password, Roboform... what do these all have in common? They're all password managers! So they all help you make strong password managers, store 'em securely, and auto enter 'em when you load websites. Which should you use? We talk local password storage vs. storing 'em 'in the cloud' and more in the video! https://1password.com/ http://keepass.info/ https://www.roboform.com/ https://www.dashlane.com/ 21:25 - Moving Between Password Managers Wondering how to move between password managers? It's all about exporting and importing... we show you how it works with LastPass in the video. 23:26 - 2FA, aka Two Factor Authentication Whether you call it multi factor authentication, 2FA, two factor authentication, it's all about making your accounts online more secure. We explain what it is, how it works online, 2FA you probably already use (PIN numbers and ATM cards from the bank!) and hardware options like Yubikey in the video! https://www.yubico.com/products/yubikey-hardware/ https://www.rsa.com/en-us/products-services/identity-access-management/securid/hardware-tokens : http://2fa.com/tokens/ Do Something Analog! Remember ... once in awhile... put down the phone, step away from the screen, close the laptop... and do something analog, like go fishing!!!
Views: 27552 TekThing
IDA2013 - Data Mining and Machine Learning Tools for Combinatorial Material Science
 
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Full title: Data Mining and Machine Learning Tools for Combinatorial Material Science of All-Oxide Photovoltaic Cells By Abraham Yosipof, Assaf Y. Anderson, Hannah Noa Barad, Sven Rühle, Arie Zaban, and Hanoch Senderowitz.
Mining the Social Web - An Infographic
 
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Matthew Russell, author of Mining the Social Web, presents an infographic that presents the primary data sources and technologies as introduced in the book. Like mining the social web and download a high resolution image of the graphic shown in the video at http://on.fb.me/icFoXH
Views: 4381 Matthew Russell
The Art of Data Visualization | Off Book | PBS Digital Studios
 
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Viewers like you help make PBS (Thank you 😃) . Support your local PBS Member Station here: http://to.pbs.org/Donateoffbook Humans have a powerful capacity to process visual information, skills that date far back in our evolutionary lineage. And since the advent of science, we have employed intricate visual strategies to communicate data, often utilizing design principles that draw on these basic cognitive skills. In a modern world where we have far more data than we can process, the practice of data visualization has gained even more importance. From scientific visualization to pop infographics, designers are increasingly tasked with incorporating data into the media experience. Data has emerged as such a critical part of modern life that it has entered into the realm of art, where data-driven visual experiences challenge viewers to find personal meaning from a sea of information, a task that is increasingly present in every aspect of our information-infused lives. Featuring: Edward Tufte, Yale University Julie Steele, O'Reilly Media Josh Smith, Hyperakt Jer Thorp, Office for Creative Research Office of Creative Research: "Gate Change" by Ben Rubin w/ Mark Hansen & Jer Thorp "And That's The Way It Is" by Ben Rubin w/ Mark Hansen & Jer Thorp "Shakespeare Machine" by Ben Rubin w/ Mark Hansen & Jer Thorp "Moveable Type" by Ben Rubin & Mark Hansen "Listening Post" by Ben Rubin & Mark Hansen Sources: Facebook World Map - Produced by Facebook intern, Paul Butler. http://gigaom.com/2010/12/14/facebook-draws-a-map-of-the-connected-world/ Paris Subway Activity - Eric Fisher - http://www.flickr.com/photos/walkingsf/ Rich Blocks, Poor Blocks - http://www.richblockspoorblocks.com/ "Hurricanes since 1851" - by John Nelson, http://uxblog.idvsolutions.com/ "Flight Patterns" by Aaron Koblin - http://www.aaronkoblin.com/work/flightpatterns/ "We Feel Fine Project" by Jonathan Harris and Sep Kamvar - http://wefeelfine.org/ "Every McDonald's in the US" by Stephen Von Worley - http://www.datapointed.net/2009/09/distance-to-nearest-mcdonalds/ "Colours in Culture" by informationisbeautiful.net - http://www.informationisbeautiful.net/visualizations/colours-in-cultures/ Music: "The Blue Cathedral" by Talvihorros - http://freemusicarchive.org/music/Talvihorros/Bad_Panda_45/The_Blue_Cathedral "Sad Cyclops" by Podington Bear - http://freemusicarchive.org/music/Podington_Bear/Ambient/SadCyclops "Between Stations" by Rescue - http://archive.org/details/one026 "Tomie's Bubbles" by Candlegravity "Earth Breath" by Human Terminal - http://freemusicarchive.org/music/Human_Terminal/Press_Any_Key/01_Earth_Breath "Unreal (Album Version)" by Garmisch - http://freemusicarchive.org/music/Garmisch/Glimmer/02_-_Unreal_Album_Version More Off Book: The Future of Wearable Technology http://youtu.be/4qFW4zwXzLs Is Photoshop Remixing the World? http://youtu.be/egnB3teYiPQ Can Hackers be Heroes? http://www.youtube.com/watch?v=NVtrA7juc-w The Rise of Webcomics http://youtu.be/6redB3Xev14 Will 3D Printing Change The World? http://youtu.be/X5AZzOw7FwA Follow Off Book: Twitter: @pbsoffbook Tumblr: http://pbsarts.tumblr.com/ Produced by Kornhaber Brown: http://www.kornhaberbrown.com
Views: 267024 PBSoffbook