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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: 5696 PyTexas
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: 1216 Udacity
Scrape data from wikipedia and put into Google Sheets by Chris Menard
 
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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: 1115 Chris Menard
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: 19754 Roshan
الـ Data Mining | #عتاد_صلب
 
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في هذه الحلفة نتحدث عن Data Mining وكيف تعمل وماهي خطوات عملها ؟ شُكر خاص لـ فراس الطويل - عتاد صلب ، نُثري المحتوى العربي في مجال الهاردوير خصوصاً والحاسب بشكل عام. _ عبدالرحمن البلوي https://twitter.com/@abdulrhmanb - https://twitter.com/3tad_slb http://www.facebook.com/3tad.Slb - مراجع https://en.wikipedia.org/wiki/Data_mining#cite_note-Fayyad-4 https://en.wikipedia.org/wiki/Data_pre-processing http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/datamining.htm https://www.youtube.com/watch?v=W44q6qszdqY&t=4s https://www.tutorialspoint.com/data_mining/dm_knowledge_discovery.htm http://www.kdnuggets.com/2016/01/businesses-need-one-million-data-scientists-2018.html https://www2.deloitte.com/content/dam/html/us/analytics-trends/2016-analytics-trends/pdf/analytics-trends.pdf - تواصل معنا على : [email protected]
Views: 6502 عتاد صلب
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: 23909 Bharatendra Rai
Deepti Ameta | Relation Extraction from Wikipedia articles using DeepDive | PyData Meetup 2
 
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PyData meetups are a forum for members of the PyData community to meet and share new approaches and emerging technologies for data management and analytics. This was the second meet-up of PyData Gandhinagar hosted at IIT Gandhinagar on October 27, 2018. Speaker – Deepti Ameta Bio: Junior Research Fellow at DAIICT Title – Relation Extraction from Wikipedia articles using DeepDive Short Description – Information Extraction is one of the challenging research areas of Computer Science today. The talk focuses on three problems: how to extract the information (relations between two named entities) from unstructured or semi-structured text documents (Wikipedia); to recognize the techniques of storage in Knowledge Base so that the information can be easily utilized and how to construct end to end data pipelines using a tool: DeepDive. A simple example will be used to understand the tool functionality and working. Further focus is on its real world applications.
Views: 444 IIT Gandhinagar
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: 1418 The Audiopedia
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: 8452 The Audiopedia
What is DATA?
 
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What is DATA (Decentralized AI-powered Trust Alliance)? DATA is a blockchain that will empower users, content developers, advertisers while fighting “ad fraud” using machine learning. Technology Used DHT - Distributed Hash Tables Erasure Code - Used for reducing storage Ethermint - Ethereum with DPoS State Channels - Raiden Network LSTM - Long Short-Term Memory (P.31) Coin Specs Supply: 11.5 billion (no max) (P.27) Speed: 10,000 TPS for Tendermint (P.26), but Ethermint caps out at 200TPS (can be horizontally scaled, but limit not tested) Protocol: PoA - Proof of Attention (DPoS with 10% to validators, 20% to publishers, 70% to end users) (P.27) Launch: Native Token Conversion Q4 2018, Mainnet Q4 2019 (P.48) Positives for DATA - Team has extensive experience in Ad Tech - The technology for the blockchain is next generation - Trading lower than ICO price Negatives for DATA - Team doesn’t have significant experience with blockchain - No Github - Realistic but boring roadmap Bottom Line: DATA is a super risky project that belongs in the IPO world. While the team has tons of experience in Mobile/Ad Tech, the fact that there is no prototype means they have a lot to do and not a lot to announce in 2018. Links http://data.eco/ https://en.wikipedia.org/wiki/Distributed_hash_table https://en.wikipedia.org/wiki/Erasure_code Erasure Code (YouTube Video): https://www.youtube.com/watch?v=f9ntIbw43xI Ethermint: https://blog.cosmos.network/a-beginners-guide-to-ethermint-38ee15f8a6f4 Every Friday I will be giving away $5 in Litecoin or $10 to one winner if you send me your Cryptobridge ID instead of your Litecoin address. How do you win? Answer a cryptocurrency trivia question (I will ask it every Wednesday) based on the topics that I covered from the 100+ videos I have made thus far. If no one comes up with the correct answer, the giveaway jackpot is moved to next week's question. Good luck, everyone! SteemIt: https://steemit.com/@crypto49er Twitter: https://twitter.com/Crypto49er ,.-~*´¨¯¨`*·~-.¸-( Cloud Mining )-,.-~*´¨¯¨`*·~-.¸ Mine Bitcoin with me on HashFlare! https://hashflare.io/r/DF04F7E3 Genesis Mining Use my code "NTgBjm" to get 3% off Genesis Mining! https://www.genesis-mining.com/ Leave your Genesis mining referral code in the discussion section and I will randomly pick one to use when I upgrade my mining contracts. ,.-~*´¨¯¨`*·~-.¸-( Crypto Binary Options )-,.-~*´¨¯¨`*·~-.¸ Free 500 Satoshis for signing up: http://bit.ly/CryptoBinaryOptions ,.-~*´¨¯¨`*·~-.¸-( Buy your first Bitcoin )-,.-~*´¨¯¨`*·~-.¸ Get $10 free from Coinbase when you buy $100 Crypto: http://bit.ly/FreeMoneyCoinBase ,.-~*´¨¯¨`*·~-.¸-( Cryptocurrency T-Shirts )-,.-~*´¨¯¨`*·~-.¸ Get a Bitcoin long sleeve T-Shirt: http://bit.ly/BitcoinLongSleeveTShirt ,.-~*´¨¯¨`*·~-.¸-( Google Advanced Protection )-,.-~*´¨¯¨`*·~-.¸ Feitian Bluetooth FIDO Security Key: http://bit.ly/FeitianBluetoothSecurityKey Yubico FIDO U2F Security Key: http://bit.ly/YubicoSecurityKey ,.-~*´¨¯¨`*·~-.¸-( Hardware Wallets )-,.-~*´¨¯¨`*·~-.¸ Ledger Nano S (also a FIDO U2F device): http://bit.ly/BuyLedgerWalletNanoS ,.-~*´¨¯¨`*·~-.¸-( 30% Monthly Profits Challenge )-,.-~*´¨¯¨`*·~-.¸ Rules: http://bit.ly/30MonthlyProfitChallengeRules Tracking: http://bit.ly/30MonthlyProfitChallengeTracking ,.-~*´¨¯¨`*·~-.¸-( Exchanges )-,.-~*´¨¯¨`*·~-.¸ Join Binance: http://bit.ly/BinanceCryptoExchange Join KuCoin: http://bit.ly/JoinKuCoin Join CoinExchange.io: http://bit.ly/JoinCoinExchange-IO ,.-~*´¨¯¨`*·~-.¸-( Spreadsheets )-,.-~*´¨¯¨`*·~-.¸ Crypto Giveaway Spreadsheet http://bit.ly/YouTubeCryptoGiveaway Crypto Scams I avoided/fell for http://bit.ly/CryptoScamsToAvoid Bitcoin/Altcoin Strategies based on News http://bit.ly/BitcoinAltcoinStrategiesBasedOnNews Crypto Price Predictions http://bit.ly/CryptoPricePredictions ,.-~*´¨¯¨`*·~-.¸-( Donations Addresses )-,.-~*´¨¯¨`*·~-.¸ BTC: 1FhS1cxTvnAaMEZcsTSndFJDwkAQZAfTfv LTC: LfeZYPJecyyPCJ2GzV2LWnUsoZoJKtx5mQ BitShares: Crypto49er ,.-~*´¨¯¨`*·~-.¸-( Credits )-,.-~*´¨¯¨`*·~-.¸ Music by Joakim Karud http://youtube.com/joakimkarud ,.-~*´¨¯¨`*·~-.¸-( Disclaimer )-,.-~*´¨¯¨`*·~-.¸ This video should not be taken as financial advice.
Views: 1002 Crypto49er
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: 765 OCLCResearch
Data mining | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/Data_mining 00:03:56 1 Etymology 00:06:57 2 Background 00:08:33 3 Process 00:10:12 3.1 Pre-processing 00:11:02 3.2 Data mining 00:12:47 3.3 Results validation 00:15:13 4 Research 00:16:34 5 Standards 00:17:59 6 Notable uses 00:18:23 7 Privacy concerns and ethics 00:21:38 7.1 Situation in Europe 00:22:24 7.2 Situation in the United States 00:23:57 8 Copyright law 00:24:07 8.1 Situation in Europe 00:25:48 8.2 Situation in the United States 00:26:43 9 Software 00:26:52 9.1 Free open-source data mining software and applications 00:29:55 9.2 Proprietary data-mining software and applications 00:31:54 9.3 Marketplace surveys 00:33:36 10 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: 0.8650267505126996 Voice name: en-US-Wavenet-F "I cannot teach anybody anything, I can only make them think." - Socrates SUMMARY ======= Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics 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. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. 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. The difference between data analysis and data mining is that data analysis is to summarize the history such as analyzing the effectiveness of a marketing campaign, in contrast, data mining focuses on using specific machine learning and statistical models to predict the future and discover the patterns among data.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. 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 (e.g., 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 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 ...
Views: 2 wikipedia tts
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: 778 Aaron Schumacher
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: 295 Sharad Nadkarni
What is Data extraction? Explain Data extraction, Define Data extraction, Meaning of Data extraction
 
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~~~ Data extraction ~~~ Title: What is Data extraction? Explain Data extraction, Define Data extraction, Meaning of Data extraction Created on: 2018-10-20 Source Link: https://en.wikipedia.org/wiki/Data_extraction ------ Description: Data extraction is the act or process of retrieving data out of data sources for further data processing or data storage . The import into the intermediate extracting system is thus usually followed by data transformation and possibly the addition of metadata prior to export to another stage in the data workflow.Usually, the term data extraction is applied when data is first imported into a computer from primary sources, like measuring or recording devices. Today's electronic devices will usually present an electrical connector through which 'raw data' can be streamed into a personal computer. ------ To see your favorite topic here, fill out this request form: https://docs.google.com/forms/d/e/1FAIpQLScU0dLbeWsc01IC0AaO8sgaSgxMFtvBL31c_pjnwEZUiq99Fw/viewform ------ Source: Wikipedia.org articles, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Support: Donations can be made from https://wikimediafoundation.org/wiki/Ways_to_Give to support Wikimedia Foundation and knowledge sharing.
Views: 309 Audioversity
Data Mining
 
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ref: http://ctrucios.bligoo.com/content/view/1494227/Data-Mining-Predecir-y-explicar.html y de wikipedia
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: 55 Audiopedia
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: 257 The Audiopedia
Enipedia-A Semantic Wiki for Energy and Industry Data
 
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Finalist Delft Innovation Award 2011
Views: 843 TU Delft
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 Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 170175 Siraj Raval
What is DATA WAREHOUSE? What does DATA WAREHOUSE mean? DATA WAREHOUSE meaning & explanation
 
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What is DATA WAREHOUSE? What does DATA WAREHOUSE mean? DATA WAREHOUSE meaning - DATA WAREHOUSE definition - DATA WAREHOUSE 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 In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place and are used for creating analytical reports for knowledge workers throughout the enterprise. The data stored in the warehouse is uploaded from the operational systems (such as marketing or sales). The data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the DW for reporting. The typical Extract, transform, load (ETL)-based data warehouse uses staging, data integration, and access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store (ODS) database. The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups, often called dimensions, and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data. The main source of the data is cleansed, transformed, catalogued and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support. However, the means to retrieve and analyze data, to extract, transform, and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata. A data warehouse maintains a copy of information from the source transaction systems. This architectural complexity provides the opportunity to: Integrate data from multiple sources into a single database and data model. Mere congregation of data to single database so a single query engine can be used to present data is an ODS. Mitigate the problem of database isolation level lock contention in transaction processing systems caused by attempts to run large, long running, analysis queries in transaction processing databases. Maintain data history, even if the source transaction systems do not. Integrate data from multiple source systems, enabling a central view across the enterprise. This benefit is always valuable, but particularly so when the organization has grown by merger. Improve data quality, by providing consistent codes and descriptions, flagging or even fixing bad data. Present the organization's information consistently. Provide a single common data model for all data of interest regardless of the data's source. Restructure the data so that it makes sense to the business users. Restructure the data so that it delivers excellent query performance, even for complex analytic queries, without impacting the operational systems. Add value to operational business applications, notably customer relationship management (CRM) systems. Make decision–support queries easier to write. Optimized data warehouse architectures allow data scientists to organize and disambiguate repetitive data. The environment for data warehouses and marts includes the following: Source systems that provide data to the warehouse or mart; Data integration technology and processes that are needed to prepare the data for use; Different architectures for storing data in an organization's data warehouse or data marts; Different tools and applications for the variety of users; Metadata, data quality, and governance processes must be in place to ensure that the warehouse or mart meets its purposes. In regards to source systems listed above, Rainer states, "A common source for the data in data warehouses is the company's operational databases, which can be relational databases"....
Views: 1813 The Audiopedia
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: 198 Mike Caulfield
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: 105156 Shomu's Biology
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: 543 Rackspace
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: 240 beckerfuffle
What is Data Mining?
 
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NJIT School of Management professor Stephan P Kudyba describes what data mining is and how it is being used in the business world.
Views: 434933 YouTube NJIT
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: 68818 deltaDNA
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: 419494 CodingEntrepreneurs
Wikitag: Generating in-text links to Wikipedia (part 1)
 
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Tomaž Šolc at Wikimania 2008, Alexandria, Egypt A common use of Wikipedia in web publishing is to provide explanations for various terms in published texts with which the reader may not be familiar. This is usually done in form of in-text hyperlinks to relevant pages in Wikipedia. Building on the existing research we have created a system that automatically adds such explanatory links to a plain text article. Combined with structured data extracted from linked Wikipedia articles, the system can also provide links to other websites concerning the subject and semantic tagging that can be used in any further processing. This talk is about the research that resulted in Wikitag, a system that is currently running as part of Zemanta (www.zemanta.com) service. An overview of the algorithm is given with descriptions of its basic building blocks and discussion of the primary problems we encountered: how to get link candidates, automatically disambiguate terms, estimate link desirability and select only the most appropriate links for the final result.
Views: 627 avian6
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: 3123 The Audiopedia
WIKI DATA ANALYTICS
 
25:27
Hello, Greetings!! Please post your queries/comments for this presentation. We would be happy to hear from you :)
Views: 108 Sundari Kaza
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: 7212 MediaWiki
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: 2 wikipedia tts
Advanced Data Mining with Weka (4.2: Installing with Apache Spark)
 
13:01
Advanced Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 2: Installing with Apache Spark http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/msswhT https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 2742 WekaMOOC
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: 211537 sentdex
Student's t-test
 
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Excel file: https://dl.dropboxusercontent.com/u/561402/TTEST.xls In this video Paul Andersen explains how to run the student's t-test on a set of data. He starts by explaining conceptually how a t-value can be used to determine the statistical difference between two samples. He then shows you how to use a t-test to test the null hypothesis. He finally gives you a separate data set that can be used to practice running the test. Do you speak another language? Help me translate my videos: http://www.bozemanscience.com/translations/ Music Attribution Intro Title: I4dsong_loop_main.wav Artist: CosmicD Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/ Creative Commons Atribution License Outro Title: String Theory Artist: Herman Jolly http://sunsetvalley.bandcamp.com/track/string-theory All of the images are licensed under creative commons and public domain licensing: 1.3.6.7.2. Critical Values of the Student’s-t Distribution. (n.d.). Retrieved April 12, 2016, from http://www.itl.nist.gov/div898/handbook/eda/section3/eda3672.htm File:Hordeum-barley.jpg - Wikimedia Commons. (n.d.). Retrieved April 11, 2016, from https://commons.wikimedia.org/wiki/File:Hordeum-barley.jpg Keinänen, S. (2005). English: Guinness for strenght. Retrieved from https://commons.wikimedia.org/wiki/File:Guinness.jpg Kirton, L. (2007). English: Footpath through barley field. A well defined and well used footpath through the fields at Nuthall. Retrieved from https://commons.wikimedia.org/wiki/File:Footpath_through_barley_field_-_geograph.org.uk_-_451384.jpg pl.wikipedia, U. W. on. ([object HTMLTableCellElement]). English: William Sealy Gosset, known as “Student”, British statistician. Picture taken in 1908. Retrieved from https://commons.wikimedia.org/wiki/File:William_Sealy_Gosset.jpg The T-Test. (n.d.). Retrieved April 12, 2016, from http://www.socialresearchmethods.net/kb/stat_t.php
Views: 563858 Bozeman Science
Hadoop Vs Traditional Database Systems | Hadoop Data Warehouse | Hadoop and ETL | Hadoop Data Mining
 
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http://www.edureka.co/hadoop Email Us: [email protected],phone : +91-8880862004 This short video explains the problems with existing database systems and Data Warehouse solutions, and how Hadoop based solutions solves these problems. Let's Get Going on our Hadoop Journey and Join our 'Big Data and Hadoop' course. - - - - - - - - - - - - - - How it Works? 1. This is a 10-Module Instructor led Online Course. 2. We have a 3-hour Live and Interactive Sessions every Sunday. 3. We have 4 hours of Practical Work involving Lab Assignments, Case Studies and Projects every week which can be done at your own pace. We can also provide you Remote Access to Our Hadoop Cluster for doing Practicals. 4. 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. 5. 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 Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, MapReduce, Advance MapReduce, PIG, HIVE, HBase, Zookeeper, SQOOP, Hadoop 2.0 , YARN etc. will be covered in the course. - - - - - - - - - - - - - - Course Objectives After the completion of the Hadoop Course at Edureka, you should be able to: Master the concepts of Hadoop Distributed File System. Understand Cluster Setup and Installation. Understand MapReduce and Functional programming. Understand How Pig is tightly coupled with Map-Reduce. Learn how to use Hive, How you can load data into HIVE and query data from Hive. Implement HBase, MapReduce Integration, Advanced Usage and Advanced Indexing. Have a good understanding of ZooKeeper service and Sqoop, Hadoop 2.0, YARN, etc. Develop a working Hadoop Architecture. - - - - - - - - - - - - - - Who should go for this course? This course is designed for developers with some programming experience (preferably Java) who are looking forward to acquire a solid foundation of Hadoop Architecture. Existing knowledge of Hadoop is not required for this course. - - - - - - - - - - - - - - Why Learn Hadoop? BiG Data! A Worldwide Problem? According to Wikipedia, "Big data is collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications." In simpler terms, Big Data is a term given to large volumes of data that organizations store and process. However, It is becoming very difficult for companies to store, retrieve and process the ever-increasing data. If any company gets hold on managing its data well, nothing can stop it from becoming the next BIG success! The problem lies in the use of traditional systems to store enormous data. Though these systems were a success a few years ago, with increasing amount and complexity of data, these are soon becoming obsolete. The good news is - Hadoop, which is not less than a panacea for all those companies working with BIG DATA in a variety of applications and has become an integral part for storing, handling, evaluating and retrieving hundreds of terabytes, and even petabytes of data. - - - - - - - - - - - - - - Some of the top companies using Hadoop: The importance of Hadoop is evident from the fact that there are many global MNCs that are using Hadoop and consider it as an integral part of their functioning, such as companies like Yahoo and Facebook! On February 19, 2008, Yahoo! Inc. established the world's largest Hadoop production application. The Yahoo! Search Webmap is a Hadoop application that runs on over 10,000 core Linux cluster and generates data that is now widely used in every Yahoo! Web search query. Opportunities for Hadoopers! Opportunities for Hadoopers are infinite - from a Hadoop Developer, to a Hadoop Tester or a Hadoop Architect, and so on. If cracking and managing BIG Data is your passion in life, then think no more and Join Edureka's Hadoop Online course and carve a niche for yourself! Happy Hadooping! Please write back to us at [email protected] or call us at +91-8880862004 for more information. http://www.edureka.co/big-data-and-hadoop
Views: 15018 edureka!
Data scientist | Wikipedia audio article
 
16:39
This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/Data_science 00:02:51 History 00:10:44 Relationship to statistics 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: 0.7791391824876839 Voice name: en-US-Wavenet-C "I cannot teach anybody anything, I can only make them think." - Socrates SUMMARY ======= Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data science is the same concept as data mining and big data: "use the most powerful hardware, the most powerful programming systems, and the most efficient algorithms to solve problems".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: 3 wikipedia tts
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: 284 CyberScrap youpul
5 Minute Metadata - What is metadata?
 
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In this video we explore the definition of metadata, and how it can be broken into two separation ideas and how they relate. These two ideas are: * Descriptive metadata - where metadata is used to add additional detail to a unique piece of data * Structural metadata - where metadata define the structure of how many pieces of related data are stored. If you want to know more visit http://www.aristotlemetadata.com Sources and useful links: [1] Wikipedia - https://en.wikipedia.org/wiki/Metadata [2] Australian National Data Service - http://www.ands.org.au/working-with-data/metadata [3] Understanding Metadata - http://www.niso.org/publications/press/UnderstandingMetadata.pdf
What is DATA CUBE? What does DATA CUBE mean? DATA CUBE meaning, definition & explanation
 
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What is DATA CUBE? What does DATA CUBE mean? DATA CUBE meaning - DATA CUBE definition - DATA CUBE 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 In computer programming contexts, a data cube (or datacube) is a multi-dimensional array of values, commonly used to describe a time series of image data. The data cube is used to represent data along some measure of interest. Even though it is called a 'cube', it can be 1-dimensional, 2-dimensional, 3-dimensional, or higher-dimensional. Every dimension represents a new measure whereas the cells in the cube represent the facts of interest. The EarthServer initiative has established requirements which a datacube service should offer. Many high-level computer languages treat data cubes and other large arrays as single entities distinct from their contents. These languages, of which APL, IDL, NumPy, PDL, and S-Lang are examples, allow the programmer to manipulate complete film clips and other data en masse with simple expressions derived from linear algebra and vector mathematics. Some languages (such as PDL) distinguish between a list of images and a data cube, while many (such as IDL) do not. Array DBMSs (Database Management Systems) offer a data model which generically supports definition, management, retrieval, and manipulation of n-dimensional datacubes. This database category has been pioneered by the rasdaman system since 1994. Multi-dimensional arrays can meaningfully represent spatio-temporal sensor, image, and simulation data, but also statistics data where the semantics of dimensions is not necessarily of spatial or temporal nature. Generally, any kind of axis can be combined with any other into a datacube. In mathematics, a one-dimensional array corresponds to a vector, a two-dimensional array resembles a matrix; more generally, a tensor may be represented as an n-dimensional data cube. For a time sequence of color images, the array is generally four-dimensional, with the dimensions representing image X and Y coordinates, time, and RGB (or other color space) color plane. For example, the EarthServer initiative unites data centers from different continents offering 3-D x/y/t satellite image timeseries and 4-D x/y/z/t weather data for retrieval and server-side processing through the Open Geospatial Consortium WCPS geo datacube query language standard. A data cube is also used in the field of imaging spectroscopy, since a spectrally-resolved image is represented as a three-dimensional volume. In Online analytical processing (OLAP), data cubes are a common arrangement of business data suitable for analysis from different perspectives through operations like slicing, dicing, pivoting, and aggregation.
Views: 6216 The Audiopedia
What is DIRTY DATA? What does DIRTY DATA mean? DIRTY DATA meaning, definition & explanation
 
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What is DIRTY DATA? What does DIRTY DATA mean? DIRTY DATA meaning - DIRTY DATA definition - DIRTY 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 Dirty data, also known as rogue data, is inaccurate, incomplete or erroneous data, especially in a computer system or database. Dirty data can contain such mistakes as spelling or punctuation errors, incorrect data associated with a field, incomplete or outdated data, or even data that has been duplicated in the database. It can be cleaned through a process known as data cleansing. Following the definition of Gary T. Marx, Professor Emeritus of MIT, there are four types of data.
Views: 309 The Audiopedia
Delia Rusu - Estimating stock price correlations using Wikipedia
 
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PyData London 2016 Building an equities portfolio is a challenging task for a finance professional as it requires, among others, future correlations between stock prices. As this data is not always available, in this talk I look at an alternative to historical correlations as proxy for future correlations: using graph analysis techniques and text similarity measures based on Wikipedia data. According to Modern Portfolio Theory, assembling a portfolio involves forming expectations about the individual stock's future risk and return as well as future correlations between stock prices. These future correlations are typically estimated using historical stock price data. However, there are situations where this type of data is not available, such as the time preceding an IPO. In this talk I look at an alternative to historical correlations as proxy for future correlations: using graph analysis techniques and text similarity measures in order to estimate the correlation between stock prices. The focus of the analysis will be on companies listed on the London Stock Exchange which form the FTSE 100 Index. I am going to use Wikipedia articles in order to derive the textual description for each company. Additionally, I will use the Wikipedia category structure to derive a graph describing relations between companies. The analysis will be performed using the scikit-learn and networkX libraries and example code will be available to the audience. GitHub: https://github.com/deliarusu/wikipedia-correlation https://github.com/idio/wiki2vec
Views: 853 PyData
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: 160 AboveGeo