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Scrape data from wikipedia and put into Google Sheets by Chris Menard
 
04:06
Do you ever have Wikipedia data you need in a spreadsheet? Using Google Sheets you don't have to copy and paste. Instead, use the ImportHTML function in Google Sheets and get the data from Wikipedia. www.chrismenardtraining.com
Views: 763 Chris Menard
Wikipedia Infobox Dataset - Data Wranging with MongoDB
 
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This video is part of an online course, Data Wrangling with MongoDB. Check out the course here: https://www.udacity.com/course/ud032. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 1085 Udacity
Web Scraping - Data Mining #1
 
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Using LXML for web scraping to get data about Nobel prize winners from wikipedia. This is done using IPython Notebook and pandas for data analysis. Github/NBViewer Link: http://nbviewer.ipython.org/github/twistedhardware/mltutorial/blob/master/notebooks/data-mining/1.%20Web%20Scraping.ipynb
Views: 18661 Roshan
Data mining and integration with Python
 
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There is an abundance of data in social media sites (Wikipedia, Facebook, Instagram, etc.) which can be accessed through web APIs. But how do we know that the data from the Wikipedia article on "Golden Gate Bridge" goes along with the data from "Golden Gate Bridge" Facebook page? This represents an important question about integrating data from various sources. In this talk, I'll outline important aspects of structured data mining, integration and entity resolution methods in a scalable system.
Views: 5123 PyTexas
Knowledge Mining: use AI to search on your data, regardless of format
 
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Join Liam Cavanagh, PM on the Applied AI & Search team, and learn about the latest technologies and use cases intelligent search. For all the sessions: https://channel9.msdn.com/Events/Cognitive-Services/Cognitive-Services-Live
Views: 870 Microsoft Developer
How to use Wikipedia as a Data Source to prepare Power BI Report?
 
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In this video, we will talk how we can consume Wikipedia data as a data source and prepare power bi report for it. As we all know Wikipedia is a platform of very useful information. If data is available in a tabular manner in Wikipedia we can directly consume that data in Power BI. In this video, we will prepare a report for a pharma company sales report. We will also learn how to set visuals in power bi and how to do formatting for this visual. Below is a reference link which we have used to prepare a report. https://en.wikipedia.org/wiki/List_of_largest_selling_pharmaceutical_products If you have any doubt related this video Email: [email protected] WhatsApp: +91 9537981467 Facebook: https://www.facebook.com/learn2all https://www.facebook.com/codesol/ LinkedIn: https://www.linkedin.com/in/dhruvin-shah-2134a6117/
Views: 591 Dhruvin Shah
What is DATA STREAM MINING? What does DATA STREAM MINING mean? DATA STREAM MINING meaning
 
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What is DATA STREAM MINING? What does V mean? DATA STREAM MINING meaning - DATA STREAM MINING definition - DATA STREAM 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 Data Stream Mining is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities. In many data stream mining applications, the goal is to predict the class or value of new instances in the data stream given some knowledge about the class membership or values of previous instances in the data stream. Machine learning techniques can be used to learn this prediction task from labeled examples in an automated fashion. Often, concepts from the field of incremental learning are applied to cope with structural changes, on-line learning and real-time demands. In many applications, especially operating within non-stationary environments, the distribution underlying the instances or the rules underlying their labeling may change over time, i.e. the goal of the prediction, the class to be predicted or the target value to be predicted, may change over time. This problem is referred to as concept drift. Examples of data streams include computer network traffic, phone conversations, ATM transactions, web searches, and sensor data. Data stream mining can be considered a subfield of data mining, machine learning, and knowledge discovery.
Views: 813 The Audiopedia
What Is DATA MINING? DATA MINING Definition & Meaning
 
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What is DATA MINING? What does DATA MINING mean? DATA MINING meaning - DATA MINING definition - DATA MINING explanation. Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.[1] Data mining is an interdisciplinary subfield of computer science with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.[1][2][3][4] Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.[5] Aside from the raw analysis step, it also 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.[1] The term "data mining" is in fact a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself.[6] It also is a buzzword[7] 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 (e.g., machine learning) and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java[8] (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.[9] 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 semi-automatic or 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, sequential pattern 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. Source: Wikipedia.org
Views: 31 Audiopedia
How to Extract Data from Wikipedia and Wikidata
 
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How to Extract Data from Wikipedia and Wikidata
Views: 328 OneLine News
Linking Library Data to Wikipedia, Part II
 
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OCLC Research Wikipedian in Residence Max Klein (twitter @notconfusing) and Senior Program Officer Merrilee Proffitt (@merrileeIAm) discuss the impact of Max's new "VIAFbot" that is linking Virtual International Authority File records to Wikipedia references.
Views: 733 OCLCResearch
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: 145524 Siraj Raval
Wikidata - Semantic Wikipedia
 
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Denny Vrandečić, one of the original authors of Semantic MediaWiki and the project lead of Wikidata describes how to make Wikipedia machine readable. In his talk Denny explains the need of semantic data in Wikimedia projects, the stages of the Wikidata and its possible use cases in the future.
Views: 1520 Yury Katkov
Bitcoin mining wikipedia
 
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Views: 22 Narik Eveling
World Class Wiki Enables Rackspace Data Center Excellence
 
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This source of truth, along with our adherence to lean manufacturing principles, enables continuous improvement and helps elevate Rackspace data center operations into best in class industry leadership.
Views: 421 Rackspace
[Wikipedia] Systrip
 
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Systrip is a visual environment for the analysis of time-series data in the context of biological networks. Systrip gathers bioinformatics and graph theoretical algorithms that can be assembled in different ways to help biologists in their visual mining process. It had been used to analyze various real biological data https://en.wikipedia.org/wiki/Systrip Please support this channel and help me upload more videos. Become one of my Patreons at https://www.patreon.com/user?u=3823907
Views: 0 WikiTubia
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: 195 Mike Caulfield
Microsoft's Data Center Gamble, How You Will Die and Top Data Stories 2016 - Data Geek TV #1
 
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// Looking to Buy a Tesla? Get $1,000 Off + Free Supercharging Use our referral code and instantly get a discount plus free supercharging on your new Model S or X. *** Get Started https://teslanomics.co/td *** Happy New Years! In my first episode of Data Geek TV I take a look back at my favorite 5 data stories from 2016. Full details on my blog. Cheers! // Follow Me Online facebook: https://fb.com/ben.sullins.data twitter: http://twitter.com/bensullins web: http://bensullins.com // Sources 5. How You Will Die - http://flowingdata.com/2016/01/19/how-you-will-die/ 4. Microsoft’s Underwater Data Center - http://www.nytimes.com/2016/02/01/technology/microsoft-plumbs-oceans-depths-to-test-underwater-data-center.html 3. The Largest Ever Analysis of Film Dialogue by Gender: 2,000 scripts, 25,000 actors, 4 million lines - http://polygraph.cool/films/index.html 2. The Great A.I. Awakening - http://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html?_r=0 1. This tiny glass disc can store 360TB of data for 13.8 billion years - http://www.sciencealert.com/this-new-5d-data-storage-disc-can-store-360tb-of-data-for-14-billion-years // Bonus 6. How Trump Reshaped the Election Map - http://www.nytimes.com/interactive/2016/11/08/us/elections/how-trump-pushed-the-election-map-to-the-right.html Like Apollo by jimmysquare https://soundcloud.com/jimmysquare Creative Commons — Attribution 3.0 Unported— CC BY 3.0 http://creativecommons.org/licenses/b... Music provided by Audio Library https://youtu.be/oIpjGVBY9AM // What is Microsoft? (wikipedia) Microsoft Corporation /ˈmaɪkrəˌsɒft, -roʊ-, -ˌsɔːft/[6][7] (commonly referred to as Microsoft or MS) is an American multinational technology company headquartered in Redmond, Washington, that develops, manufactures, licenses, supports and sells computer software, consumer electronics and personal computers and services. Its best known software products are the Microsoft Windows line of operating systems, Microsoft Office office suite, and Internet Explorer and Edge web browsers. Its flagship hardware products are the Xbox video game consoles and the Microsoft Surface tablet lineup. As of 2011, it was the world's largest software maker by revenue,[8] and one of the world's most valuable companies.[9] Microsoft was founded by Paul Allen and Bill Gates on April 4, 1975, to develop and sell BASIC interpreters for the Altair 8800. It rose to dominate the personal computer operating system market with MS-DOS in the mid-1980s, followed by Microsoft Windows. The company's 1986 initial public offering (IPO), and subsequent rise in its share price, created three billionaires and an estimated 12,000 millionaires among Microsoft employees. Since the 1990s, it has increasingly diversified from the operating system market and has made a number of corporate acquisitions. In May 2011, Microsoft acquired Skype Technologies for $8.5 billion,[10] and in December 2016 bought LinkedIn for $26.2 billion.[11] As of 2015, Microsoft is market-dominant in the IBM PC-compatible operating system market and the office software suite market, although it has lost the majority of the overall operating system market to Android.[12] The company also produces a wide range of other software for desktops and servers, and is active in areas including Internet search (with Bing), the video game industry (with the Xbox, Xbox 360 and Xbox One consoles), the digital services market (through MSN), and mobile phones (via the operating systems of Nokia's former phones[13] and Windows Phone OS). In June 2012, Microsoft entered the personal computer production market for the first time, with the launch of the Microsoft Surface, a line of tablet computers. With the acquisition of Nokia's devices and services division to form Microsoft Mobile, the company re-entered the smartphone hardware market, after its previous attempt, Microsoft Kin, which resulted from their acquisition of Danger Inc.[14] The word "Microsoft" is a portmanteau of "microcomputer" and "software".[15] // What is Data Science? (wikipedia) Data science, also known as data-driven science, is an interdisciplinary field about scientific processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured,[1][2] which is a continuation of some of the data analysis fields such as statistics, machine learning, data mining, and predictive analytics,[3] similar to Knowledge Discovery in Databases (KDD). Turing award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge.[4][5]
Data science | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/Data_science 00:01:48 1 History 00:07:12 2 Relationship to statistics 00:11:05 3 See also Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. Listen on Google Assistant through Extra Audio: https://assistant.google.com/services/invoke/uid/0000001a130b3f91 Other Wikipedia audio articles at: https://www.youtube.com/results?search_query=wikipedia+tts Upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts Speaking Rate: 0.9746107240449066 Voice name: en-AU-Wavenet-D "I cannot teach anybody anything, I can only make them think." - Socrates SUMMARY ======= Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining. Data science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science. Turing award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge.In 2012, when Harvard Business Review called it "The Sexiest Job of the 21st Century", the term "data science" became a buzzword. It is now often used interchangeably with earlier concepts like business analytics, business intelligence, predictive modeling, and statistics. Even the suggestion that data science is sexy was paraphrasing Hans Rosling, featured in a 2011 BBC documentary with the quote, "Statistics is now the sexiest subject around." Nate Silver referred to data science as a sexed up term for statistics. In many cases, earlier approaches and solutions are now simply rebranded as "data science" to be more attractive, which can cause the term to become "dilute[d] beyond usefulness." While many university programs now offer a data science degree, there exists no consensus on a definition or suitable curriculum contents. To its discredit, however, many data-science and big-data projects fail to deliver useful results, often as a result of poor management and utilization of resources.
Views: 0 wikipedia tts
Data Science in Python: Exploring Wikipedia Data
 
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As part of a follow-up series to my Pycon 2014 talk "Realtime predictive analytics using scikit-learn & RabbitMQ", I present a step by step guide on how I created my language prediction model using Wikipedia and scikit-learn. This video shows a 3d visualization of the characters used in different languages. The 4 different clusters in this visualization were identified automatically using KMeans clustering. PCA was applied to the dataset to reduce the number of features from 10000 to 3 (for visualization purposes).
Views: 238 beckerfuffle
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: 6235 MediaWiki
"Exploring Wikipedia With Apache Spark" - Advanced Training by Sameer Farooqui (Databricks)
 
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Live Big Data Training from Spark Summit 2016 in San Francisco. "The real power and value proposition of Apache Spark is in building a unified use case that combines ETL, batch analytics, real­ time stream analysis, machine learning, graph processing and visualizations. In class we will explore various Wikipedia datasets while applying the ideal programming paradigm for each analysis. The class will comprise of about 50% lecture and 50% hands-on labs + demos." - Sameer Class covers: - Spark SQL and DataFrames - Spark Streaming - Machine Learning (NLP, k-means clustering, TF-IDF, PageRank, Shortest Path) - GraphFrames - Visualizations (Databricks, Matplotlib, Google Charts, D3.js) - Advanced Performance Tuning and Debugging - Spark UI Data sets that we explore: - Pageviews (March 2015) - 255 MB - Clickstream (Feb 2015) - 1.2 GB - Pagecounts (last hour) - ~550 MB - English Wikipedia (Mar 2016) - 54 GB - 6 Wikipedia Language Live Edit Streams (variable) // About the Presenter // Sameer Farooqui is a Technology Evangelist at Databricks where he helps promote the adoption of Apache Spark. As a founding member of the training team, he created and taught advanced Spark classes at private clients, meetups and conferences globally. Follow Sameer on - Twitter: https://twitter.com/blueplastic LinkedIn: https://www.linkedin.com/in/blueplastic
Views: 14203 Spark Summit
Forecasting Time Series Data in R | Facebook's Prophet Package 2017 & Tom Brady's Wikipedia data
 
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An example of using Facebook's recently released open source package prophet including, - data scraped from Tom Brady's Wikipedia page - getting Wikipedia trend data - time series plot - handling missing data and log transform - forecasting with Facebook's prophet - prediction - plot of actual versus forecast data - breaking and plotting forecast into trend, weekly seasonality & yearly seasonality components prophet procedure is an additive regression model with following components: - a piecewise linear or logistic growth curve trend - a yearly seasonal component modeled using Fourier series - a weekly seasonal component forecasting is an important tool related to analyzing big data or working in data science field. 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: 19805 Bharatendra Rai
WIKI DATA ANALYTICS
 
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Hello, Greetings!! Please post your queries/comments for this presentation. We would be happy to hear from you :)
Views: 104 Sundari Kaza
Natural Language Processing (NLP) & Text Mining Tutorial Using NLTK | NLP Training | Edureka
 
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** NLP Using Python: - https://www.edureka.co/python-natural-language-processing-course ** This Edureka video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and much more along with a demo on each one of the topics. The following topics covered in this video : 1. The Evolution of Human Language 2. What is Text Mining? 3. What is Natural Language Processing? 4. Applications of NLP 5. NLP Components and Demo Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV --------------------------------------------------------------------------------------------------------- Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ --------------------------------------------------------------------------------------------------------- - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learnt content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed. This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience. -------------------------- Who Should go for this course ? Edureka’s NLP Training is a good fit for the below professionals: From a college student having exposure to programming to a technical architect/lead in an organisation Developers aspiring to be a ‘Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Text Mining Techniques 'Python' professionals who want to design automatic predictive models on text data "This is apt for everyone” --------------------------------- Why Learn Natural Language Processing or NLP? Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data. --------------------------------- For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 16077 edureka!
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: 6713 The Audiopedia
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: 277 Sharad Nadkarni
Scraping Table Data from Web Pages Using R
 
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I now recommend using rvest to do scraping. See https://raw.githubusercontent.com/steviep42/youtube/master/YOUTUBE.DIR/rvest.R for a working code example. The older XML code is still available at https://raw.githubusercontent.com/steviep42/youtube/master/YOUTUBE.DIR/xml_readhtmltable.R
Views: 30374 Steve Pittard
What is EVOLUTIONARY DATA MINING? What does EVOLUTIONARY DATA MINING mean?
 
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What is EVOLUTIONARY DATA MINING? What does EVOLUTIONARY DATA MINING mean? EVOLUTIONARY DATA MINING meaning - EVOLUTIONARY DATA MINING definition - EVOLUTIONARY DATA 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 Evolutionary data mining, or genetic data mining is an umbrella term for any data mining using evolutionary algorithms. While it can be used for mining data from DNA sequences, it is not limited to biological contexts and can be used in any classification-based prediction scenario, which helps "predict the value ... of a user-specified goal attribute based on the values of other attributes." For instance, a banking institution might want to predict whether a customer's credit would be "good" or "bad" based on their age, income and current savings. Evolutionary algorithms for data mining work by creating a series of random rules to be checked against a training dataset. The rules which most closely fit the data are selected and are mutated. The process is iterated many times and eventually, a rule will arise that approaches 100% similarity with the training data. This rule is then checked against a test dataset, which was previously invisible to the genetic algorithm. Before databases can be mined for data using evolutionary algorithms, it first has to be cleaned, which means incomplete, noisy or inconsistent data should be repaired. It is imperative that this be done before the mining takes place, as it will help the algorithms produce more accurate results. If data comes from more than one database, they can be integrated, or combined, at this point. When dealing with large datasets, it might be beneficial to also reduce the amount of data being handled. One common method of data reduction works by getting a normalized sample of data from the database, resulting in much faster, yet statistically equivalent results. At this point, the data is split into two equal but mutually exclusive elements, a test and a training dataset. The training dataset will be used to let rules evolve which match it closely. The test dataset will then either confirm or deny these rules. Evolutionary algorithms work by trying to emulate natural evolution. First, a random series of "rules" are set on the training dataset, which try to generalize the data into formulas. The rules are checked, and the ones that fit the data best are kept, the rules that do not fit the data are discarded. The rules that were kept are then mutated, and multiplied to create new rules. This process iterates as necessary in order to produce a rule that matches the dataset as closely as possible. When this rule is obtained, it is then checked against the test dataset. If the rule still matches the data, then the rule is valid and is kept. If it does not match the data, then it is discarded and the process begins by selecting random rules again.
Views: 141 The Audiopedia
JESS3: The State of Wikipedia
 
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The State of Wikipedia not only explores the rich history and inner-workings of the web-based encyclopedia, but it's also a celebration of its 10th anniversary. With more than 17 million articles in over 270 languages, Wikipedia has undoubtedly become one of the most visited and relied upon sites on the web today. The fourth video in our the "State of" series, JESS3 is proud to release The State of Wikipedia as our first video of 2011. And, as if it weren't good enough, the video features none other than one of the co-founders himself, Jimmy Wales, as the narrator.
Views: 174204 JESS3
Coding With Python :: Learn API Basics to Grab Data with Python
 
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Coding With Python :: Learn API Basics to Grab Data with Python This is a basic introduction to using APIs. APIs are the "glue" that keep a lot of web applications running and thriving. Without APIs much of the internet services you love might not even exist! APIs are easy way to connect with other websites & web services to use their data to make your site or application even better. This simple tutorial gives you the basics of how you can access this data and use it. If you want to know if a website has an api, just search "Facebook API" or "Twitter API" or "Foursquare API" on google. Some APIs are easy to use (like Locu's API which we use in this video) some are more complicated (Facebook's API is more complicated than Locu's). More about APIs: http://en.wikipedia.org/wiki/Api Code from the video: http://pastebin.com/tFeFvbXp If you want to learn more about using APIs with Django, learn at http://CodingForEntrepreneurs.com for just $25/month. We apply what we learn here into a Django web application in the GeoLocator project. The Try Django Tutorial Series is designed to help you get used to using Django in building a basic landing page (also known as splash page or MVP landing page) so you can collect data from potential users. Collecting this data will prove as verification (or validation) that your project is worth building. Furthermore, we also show you how to implement a Paypal Button so you can also accept payments. Django is awesome and very simple to get started. Step-by-step tutorials are to help you understand the workflow, get you started doing something real, then it is our goal to have you asking questions... "Why did I do X?" or "How would I do Y?" These are questions you wouldn't know to ask otherwise. Questions, after all, lead to answers. View all my videos: http://bit.ly/1a4Ienh Get Free Stuff with our Newsletter: http://eepurl.com/NmMcr The Coding For Entrepreneurs newsletter and get free deals on premium Django tutorial classes, coding for entrepreneurs courses, web hosting, marketing, and more. Oh yeah, it's free: A few ways to learn: 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 (40k+ 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: 415678 CodingEntrepreneurs
What is DATA REDUCTION? What does DATA REDUCTION mean? DATA REDUCTION meaning & explanation
 
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What is DATA REDUCTION? What does DATA REDUCTION mean? DATA REDUCTION meaning - DATA REDUCTION definition - DATA REDUCTION 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 reduction is the transformation of numerical or alphabetical digital information derived empirically or experimentally into a corrected, ordered, and simplified form. The basic concept is the reduction of multitudinous amounts of data down to the meaningful parts. When information is derived from instrument readings there may also be a transformation from analog to digital form. When the data are already in digital form the 'reduction' of the data typically involves some editing, scaling, coding, sorting, collating, and producing tabular summaries. When the observations are discrete but the underlying phenomenon is continuous then smoothing and interpolation are often needed. Often the data reduction is undertaken in the presence of reading or measurement errors. Some idea of the nature of these errors is needed before the most likely value may be determined. An example in astronomy is the data reduction in the Kepler satellite. This satellite records 95-megapixel images once every six seconds, generating tens of megabytes of data per second, which is orders of magnitudes more than the downlink bandwidth of 550 KBps. The on-board data reduction encompasses co-adding the raw frames for thirty minutes, reducing the bandwidth by a factor of 300. Furthermore, interesting targets are pre-selected and only the relevant pixels are processed, which is 6% of the total. This reduced data is then sent to Earth where it is processed further. Research has also been carried out on the use of data reduction in wearable (wireless) devices for health monitoring and diagnosis applications. For example, in the context of epilepsy diagnosis, data reduction has been used to increase the battery lifetime of a wearable EEG device by selecting, and only transmitting, EEG data that is relevant for diagnosis and discarding background activity.
Views: 790 The Audiopedia
Indexing Wikipedia as a Benchmark of Single Machine Performance Limits
 
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Presented by Paddy Mullen,Independent Contractor This talk walks through using the wikipedia_Solr and wikipedia_elasticsearch repositories to quickly get up to speed with search at scale. When choosing a search solution, a common question is "Can this architecture handle my volume of data", figuring out how to answer that problem without integrating with your existing document store saves a lot of time. If your document corpus is similar to Wikipedia's document corpus, you can save a lot of time using wikipedia_Solr/wikipedia_elasticsearch as comparison points. Wikipedia is a great source for a tutorial such as mine because of it's familiarity and free availability. The uncompressed Wikipedia data dump I used was 33GB, it had 12M documents. The documents can be further split into paragraphs and links to test search over a large number of small items. To add extra scale, prior revisions can be used bringing the corpus size into terabytes.
Data Mining
 
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ref: http://ctrucios.bligoo.com/content/view/1494227/Data-Mining-Predecir-y-explicar.html y de wikipedia
Text Mining in R Tutorial: Term Frequency & Word Clouds
 
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This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game' Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.
Views: 65572 deltaDNA
Ben Goertzel - The Future of A.I.
 
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Source ► https://goo.gl/zu1mjB Ben Goertzel ► https://en.wikipedia.org/wiki/Ben_Goertzel Ben Goertzel is Chief Scientist of financial prediction firm Aidyia Holdings; Chairman of AI software company Novamente LLC, which is a privately held software company, and bioinformatics company Biomind LLC, which is a company that provides advanced AI for bioinformatic data analysis (especially microarray and SNP data); Chairman of the Artificial General Intelligence Society and the OpenCog Foundation; Vice Chairman of futurist nonprofit Humanity+; Scientific Advisor of biopharma firm Genescient Corp.; Advisor to the Singularity University; Research Professor in the Fujian Key Lab for Brain-Like Intelligent Systems at Xiamen University, China; and general Chair of the Artificial General Intelligence conference series, an American author and researcher in the field of artificial intelligence. He is an advisor to the Machine Intelligence Research Institute (formerly the Singularity Institute) and formerly its Director of Research. His research work encompasses artificial general intelligence, natural language processing, cognitive science, data mining, machine learning, computational finance, bioinformatics, virtual worlds and gaming and other areas. He has published a dozen scientific books, 100+ technical papers, and numerous journalistic articles. He actively promotes the OpenCog project that he co-founded, which aims to build an open source artificial general intelligence engine. He is focused on creating benevolent superhuman artificial general intelligence; and applying AI to areas like financial prediction, bioinformatics, robotics and gaming. --------- Facebook: https://www.facebook.com/agingreversed Twitter: https://twitter.com/Aging_Reversed Support the Channel: https://goo.gl/ciSpg1 Channel t-shirt: https://teespring.com/aging-reversed
Views: 18913 Aging Reversed
Data-Hacking with Wikimedia Projects: Learn by Example, Including Wikipedia, WikiData and Beyond!
 
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csv,conf 2014 http://csvconf.com/ Data-Hacking with Wikimedia Projects: Learn by Example, Including Wikipedia, WikiData and Beyond! Max Klein https://twitter.com/notconfusing Matt Senate https://twitter.com/wrought Matt believes in the moral imperative to share knowledge far and wide. He is a Californian; he lives in Oakland and collaborates at the Sudo Room, a creative community and hacker space. How do Wikimedia project communities work? How do data hackers interface and interact with these communities? What is at stake and who are the stakeholders? Join this talk to learn by example, through the story of the Open Access Signalling Project. This project's focus is to improve existing Wikipedia citations of Open Access research articles and other such academic works. This is one path among parallel initiatives (past and present) to improve how references work on Wikipedia, and across Wikimedia projects. "A fact is only as reliable as the ability to source that fact, and the ability to weigh carefully that source." - WikiScholar proposal 'A free and universal bibliography for the world' (circa 2006 - 2010, status: closed) video recording cc0 public domain
Views: 769 Aaron Schumacher
Enipedia-A Semantic Wiki for Energy and Industry Data
 
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Finalist Delft Innovation Award 2011
Views: 834 TU Delft
Chinese Power Plant Data Pentaho Agile ETL
 
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Chinese Power Plant Data from Wikipedia loaded and displayed using Instaview Agile ETL within the Pentaho Big Data system.
Views: 139 ChangingEnergy
Game AI | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/Artificial_intelligence_in_video_games 00:00:43 1 Overview 00:02:30 2 History 00:07:09 3 Views 00:09:13 4 Usage 00:09:22 4.1 In computer simulations of board games 00:10:08 4.2 In modern video games 00:12:57 4.2.1 Video game combat AI 00:15:38 4.3 Uses in games beyond NPCs 00:17:27 5 Cheating AI 00:19:52 6 Examples 00:23:53 7 See also Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. Listen on Google Assistant through Extra Audio: https://assistant.google.com/services/invoke/uid/0000001a130b3f91 Other Wikipedia audio articles at: https://www.youtube.com/results?search_query=wikipedia+tts Upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts "There is only one good, knowledge, and one evil, ignorance." - Socrates SUMMARY ======= In video games, artificial intelligence (AI) is used to generate responsive, adaptive or intelligent behaviors primarily in non-player characters (NPCs) similar to human-like intelligence. Artificial intelligence has been an integral part of video games since their inception in the 1950s. The role of AI in video games has expanded greatly since its introduction. Modern games often implement existing techniques from the field of artificial intelligence such as pathfinding and decision trees to guide the actions of NPCs. Additionally, AI is often used in mechanisms which are not immediately visible to the user, such as data mining and procedural-content generation.
Views: 1 wikipedia tts
Intro to Web Scraping with Python and Beautiful Soup
 
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Web scraping is a very powerful tool to learn for any data professional. With web scraping the entire internet becomes your database. In this tutorial we show you how to parse a web page into a data file (csv) using a Python package called BeautifulSoup. In this example, we web scrape graphics cards from NewEgg.com. Sublime: https://www.sublimetext.com/3 Anaconda: https://www.continuum.io/downloads#wi... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f6wzS0 See what our past attendees are saying here: https://hubs.ly/H0f6wzY0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 445809 Data Science Dojo
LDA Topic Models
 
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LDA Topic Models is a powerful tool for extracting meaning from text. In this video I talk about the idea behind the LDA itself, why does it work, what are the free tools and frameworks that can be used, what LDA parameters are tuneable, what do they mean in terms of your specific use case and what to look for when you evaluate it.
Views: 73908 Andrius Knispelis
Data Mining & Data Warehouse
 
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Mata kuliah Sistem Basis Data. Universitas Gunadarma. Dita Herawati (52409749) Ika Sulitiowati (56409303) Vierdhani Kusuma Ningrum (54409524) Sumber: http://id.wikipedia.org/wiki/Metadata http://computermaniax.wordpress.com/2011/09/23/konsep-data-warehouse/ http://wargabasdat2009.wordpress.com/2009/06/10/data-mining-firdi/ http://andyku.wordpress.com/2008/11/21/konsep-data-mining/ ilmukomputer.com
Views: 704 ikasule
What is DATA PRE-PROCESSING? What does DATA PRE-PROCESSING mean? DATA PRE-PROCESSING meaning
 
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What is DATA PRE-PROCESSING? What does DATA PRE-PROCESSING mean? DATA PRE-PROCESSING meaning - DATA PRE-PROCESSING definition - DATA PRE-PROCESSING 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 pre-processing is an important step in the data mining process. The phrase "garbage in, garbage out" is particularly applicable to data mining and machine learning projects. Data-gathering methods are often loosely controlled, resulting in out-of-range values (e.g., Income: -100), impossible data combinations (e.g., Sex: Male, Pregnant: Yes), missing values, etc. Analyzing data that has not been carefully screened for such problems can produce misleading results. Thus, the representation and quality of data is first and foremost before running an analysis. If there is much irrelevant and redundant information present or noisy and unreliable data, then knowledge discovery during the training phase is more difficult. Data preparation and filtering steps can take considerable amount of processing time. Data pre-processing includes cleaning, Instance selection, normalization, transformation, feature extraction and selection, etc. The product of data pre-processing is the final training set. Kotsiantis et al. (2006) present a well-known algorithm for each step of data pre-processing.
Views: 27 The Audiopedia
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: 184670 sentdex
What is CHIEF DATA OFFICER? What does CHIEF DATA OFFICER mean? CHIEF DATA OFFICER meaning
 
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What is CHIEF DATA OFFICER? What does CHIEF DATA OFFICER mean? CHIEF DATA OFFICER meaning - CHIEF DATA OFFICER definition - CHIEF DATA OFFICER explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. A chief data officer (CDO) is a corporate officer responsible for enterprise wide governance and utilization of information as an asset, via data processing, analysis, data mining, information trading and other means. CDOs report mainly to the chief executive officer (CEO). Depending on the area of expertise this can vary. CDO is a member of the executive management team and manager of enterprise-wide data processing & data mining. The Chief Data Officer title shares its acronym with the Chief Digital Officer but the two are not the same job. The Chief Data Officer has a significant measure of business responsibility for determining what kinds of information the enterprise will choose to capture, retain and exploit and for what purposes. However, the similar-sounding Chief Digital Officer or Chief Digital Information Officer often does not bear that business responsibility, but rather is responsible for the information systems through which data is stored and processed. The role of manager for data processing was not elevated to that of senior management prior to the 1980s. As organizations have recognized the importance of information technology as well as business intelligence, data integration, master data management and data processing to the fundamental functioning of everyday business, this role has become more visible and crucial. This role includes defining strategic priorities for the company in the area of data systems and opportunities, identifying new business opportunities pertaining to data, optimizing revenue generation through data, and generally representing data as a strategic business asset at the executive table. With the rise in service-oriented architectures (SOA), large-scale system integration, and heterogeneous data storage/exchange mechanisms (databases, XML, EDI, etc.), it is necessary to have a high-level individual, who possesses a combination of business knowledge, technical skills, and people skills, guide data strategy. Besides the revenue opportunities, acquisition strategy, and customer data policies, the chief data officer is charged with explaining the strategic value of data and its important role as a business asset and revenue driver to executives, employees, and customers. This contrasts with the older view of data systems as mere back-end IT systems. More recently, with the adoption of data science the Chief Data Officer is sometimes looked upon as the key strategy person either reporting to the Chief Strategy Officer or serving the role of CSO in lieu of one. This person has the responsibility of measurement along various business lines and consequently defining the strategy for the next growth opportunities, product offerings, markets to pursue, competitors to look at etc. This is seen in organizations like Chartis, AllState and Fidelity.
Views: 1304 The Audiopedia
Anova Mining Project Area
 
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This is a video showing the area of interest for a project with Anova Mining fall of 2016. Data post processing will provide Digital Elevation Maps, Topographic Maps, 2D and 3D representations of the project area. Music: Cascade by Hyper https://en.wikipedia.org/wiki/We_Control
Views: 156 AboveGeo

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