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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: 7482 The Audiopedia
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: 95464 Shomu's Biology
Delia Rusu - Estimating stock price correlations using Wikipedia
 
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PyData Berlin 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 Frankfurt Stock Exchange which form the DAX. 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. https://github.com/deliarusu/wikipedia-correlation Slides: https://speakerdeck.com/deliarusu/estimating-stock-price-correlations-using-wikipedia
Views: 1749 PyData
What is DATA WRANGLING? What does DATA WRANGLING mean? DATA WRANGLING meaning & explanation
 
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What is DATA WRANGLING? What does DATA WRANGLING mean? DATA WRANGLING meaning -DATA WRANGLING definition - DATA WRANGLING 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 wrangling (sometimes referred to as data munging) is the process of transforming and mapping data from one "raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics. A data wrangler describes the person who performs these transformation operations. This may include further munging, data visualization, data aggregation, training a statistical model, as well as many other potential uses. Data munging as a process typically follows a set of general steps which begin with extracting the data in a raw form from the data source, "munging" the raw data using algorithms (e.g. sorting) or parsing the data into predefined data structures, and finally depositing the resulting content into a data sink for storage and future use. The "wrangler" non-technical term is often said to derive from work done by the United States Library of Congress's National Digital Information Infrastructure and Preservation Program (NDIIPP) and their program partner the Emory University Libraries based MetaArchive Partnership. The term "mung" has roots in munging as described in the Jargon File. The term "Data Wrangler" was also suggested as the best analogy to coder for code for someone working with data. The terms data wrangling and data wrangler had sporadic use in the 1990s and early 2000s. One of the earliest business mentions of data wrangling was in an article in Byte Magazine in 1997 (Volume 22 issue 4) referencing “Perl’s data wrangling services”. In 2001 it was reported that CNN hired “a dozen data wranglers” to help track down information for news stories. One of the first mentions of data wrangler in a scientific context was by Donald Cline during the NASA/NOAA Cold Lands Processes Experiment. Cline stated the data wranglers “coordinate the acquisition of the entire collection of the experiment data.” Cline also specifies duties typically handled by a storage administrator for working with large amounts of data. This can occur in areas like major research projects and the making of films with a large amount of complex computer-generated imagery. In research, this involves both data transfer from research instrument to storage grid or storage facility as well as data manipulation for re-analysis via high performance computing instruments or access via cyberinfrastructure-based digital libraries. The data transformations are typically applied to distinct entities (e.g. fields, rows, columns, data values etc.) within a data set, and could include such actions as extractions, parsing, joining, standardizing, augmenting, cleansing, consolidating and filtering to create desired wrangling outputs that can be leveraged downstream. The recipients could be individuals, such as data architects or data scientists who will investigate the data further, business users who will consume the data directly in reports, or systems that will further process the data and write it into targets such as data warehouses, data lakes or downstream applications. Depending on the amount and format of the incoming data, data wrangling has traditionally been performed manually (e.g. via spreadsheets such as Excel) or via hand-written scripts in languages such as Python or SQL. R, a language often used in data mining and statistical data analysis, is now also often used for data wrangling. On a film or television production utilizing digital cameras that are not tape based, a data wrangler is employed to manage the transfer of data from a camera to a computer and/or hard drive.....
Views: 4478 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: 36 Audiopedia
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: 493119 Bozeman Science
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: 129 Audioversity
Statistical Aspects of Data Mining (Stats 202) Day 1
 
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Google Tech Talks June 26, 2007 ABSTRACT This is the Google campus version of Stats 202 which is being taught at Stanford this summer. I will follow the material from the Stanford class very closely. That material can be found at www.stats202.com. The main topics are exploring and visualizing data, association analysis, classification, and clustering. The textbook is Introduction to Data Mining by Tan, Steinbach and Kumar. Googlers are welcome to attend any classes which they think might be of interest to them. Credits: Speaker:David Mease
Views: 215864 GoogleTechTalks
What is STATISTICAL CLASSIFICATION? What does STATISTICAL CLASSIFICATION mean?
 
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What is STATISTICAL CLASSIFICATION? What does STATISTICAL CLASSIFICATION mean? STATISTICAL CLASSIFICATION meaning - STATISTICAL CLASSIFICATION definition - STATISTICAL CLASSIFICATION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. An example would be assigning a given email into "spam" or "non-spam" classes or assigning a diagnosis to a given patient as described by observed characteristics of the patient (gender, blood pressure, presence or absence of certain symptoms, etc.). Classification is an example of pattern recognition. In the terminology of machine learning, classification is considered an instance of supervised learning, i.e. learning where a training set of correctly identified observations is available. The corresponding unsupervised procedure is known as clustering, and involves grouping data into categories based on some measure of inherent similarity or distance. Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or features. These properties may variously be categorical (e.g. "A", "B", "AB" or "O", for blood type), ordinal (e.g. "large", "medium" or "small"), integer-valued (e.g. the number of occurrences of a particular word in an email) or real-valued (e.g. a measurement of blood pressure). Other classifiers work by comparing observations to previous observations by means of a similarity or distance function. An algorithm that implements classification, especially in a concrete implementation, is known as a classifier. The term "classifier" sometimes also refers to the mathematical function, implemented by a classification algorithm, that maps input data to a category. Terminology across fields is quite varied. In statistics, where classification is often done with logistic regression or a similar procedure, the properties of observations are termed explanatory variables (or independent variables, regressors, etc.), and the categories to be predicted are known as outcomes, which are considered to be possible values of the dependent variable. In machine learning, the observations are often known as instances, the explanatory variables are termed features (grouped into a feature vector), and the possible categories to be predicted are classes. Other fields may use different terminology: e.g. in community ecology, the term "classification" normally refers to cluster analysis, i.e. a type of unsupervised learning, rather than the supervised learning described in this article.
Views: 2574 The Audiopedia
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: 1019 The Audiopedia
003 - Binary Distance
 
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Get a little "bit" of binary in your life. Learn about binary and binary operators in this video. Some additional "light" reading: http://stackoverflow.com/a/12946226 http://stackoverflow.com/questions/867393/how-do-languages-such-as-python-overcome-cs-integral-data-limits/870429#870429 https://en.wikipedia.org/wiki/Hamming_weight#Efficient_implementation https://gcc.gnu.org/onlinedocs/gcc/Other-Builtins.html https://graphics.stanford.edu/~seander/bithacks.html https://svn.python.org/projects/python/trunk/Objects/longobject.c See you next time! Music: Antonio Vivaldi - Concerto in C major Op.8 No.12, European Archive, https://musopen.org/music/3087/antonio-vivaldi/concerto-in-c-major-op8-no12/ Some images: http://www.snappygoat.com
Views: 422 CoderSnacks
What is ASTROINFORMATICS? What does ASTROINFORMATICS mean? ASTROINFORMATICS meaning
 
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What is ASTROINFORMATICS? What does ASTROINFORMATICS mean? ASTROINFORMATICS meaning - ASTROINFORMATICS definition - ASTROINFORMATICS 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 Astroinformatics is an interdisciplinary field of study involving the combination of astronomy, data science, informatics, and information/communications technologies. Astroinformatics is primarily focused on developing the tools, methods, and applications of computational science, data science, and statistics for research and education in data-oriented astronomy. Early efforts in this direction included data discovery, metadata standards development, data modeling, astronomical data dictionary development, data access, information retrieval, data integration, and data mining in the astronomical Virtual Observatory initiatives. Further development of the field, along with astronomy community endorsement, was presented to the National Research Council (United States) in 2009 in the Astroinformatics "State of the Profession" Position Paper for the 2010 Astronomy and Astrophysics Decadal Survey. That position paper provided the basis for the subsequent more detailed exposition of the field in the Informatics Journal paper Astroinformatics: Data-Oriented Astronomy Research and Education. Astroinformatics as a distinct field of research was inspired by work in the fields of Bioinformatics and Geoinformatics, and through the eScience work of Jim Gray (computer scientist) at Microsoft Research, whose legacy was remembered and continued through the Jim Gray eScience Awards. Though the primary focus of Astroinformatics is on the large worldwide distributed collection of digital astronomical databases, image archives, and research tools, the field recognizes the importance of legacy data sets as well—using modern technologies to preserve and analyze historical astronomical observations. Some Astroinformatics practitioners help to digitize historical and recent astronomical observations and images in a large database for efficient retrieval through web-based interfaces. Another aim is to help develop new methods and software for astronomers, as well as to help facilitate the process and analysis of the rapidly growing amount of data in the field of astronomy. Astroinformatics is described as the Fourth Paradigm of astronomical research. There are many research areas involved with astroinformatics, such as data mining, machine learning, statistics, visualization, scientific data management, and semantic science. Data mining and machine learning play significant roles in Astroinformatics as a scientific research discipline due to their focus on "knowledge discovery from data" (KDD) and "learning from data". The amount of data collected from astronomical sky surveys has grown from gigabytes to terabytes throughout the past decade and is predicted to grow in the next decade into hundreds of petabytes with the Large Synoptic Survey Telescope and into the exabytes with the Square Kilometre Array. This plethora of new data both enables and challenges effective astronomical research. Therefore, new approaches are required. In part, due to this data-driven science is becoming a recognized academic discipline. Consequently, astronomy (and other scientific disciplines) are developing sub-disciplines information and data intensive to an extent that these sub-disciplines are now becoming (or have already become) stand alone research disciplines and full-fledged academic programs. While many institutes of education do not boast an astroinformatics program, the most likely will in the near future. Informatics has been recently defined as "the use of digital data, information, and related services for research and knowledge generation". However the usual, or commonly used definition is "informatics is the discipline of organizing, accessing, integrating, and mining data from multiple sources for discovery and decision support."....
Views: 120 The Audiopedia
Statistics | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/Statistics 00:05:11 1 Scope 00:06:32 1.1 Mathematical statistics 00:07:04 2 Overview 00:09:54 3 Data collection 00:10:04 3.1 Sampling 00:12:42 3.2 Experimental and observational studies 00:14:45 3.2.1 Experiments 00:17:54 3.2.2 Observational study 00:18:55 4 Types of data 00:22:42 5 Terminology and theory of inferential statistics 00:22:55 5.1 Statistics, estimators and pivotal quantities 00:25:53 5.2 Null hypothesis and alternative hypothesis 00:27:42 5.3 Error 00:31:08 5.4 Interval estimation 00:33:56 5.5 Significance 00:38:33 5.6 Examples 00:38:47 6 Misuse 00:42:57 6.1 Misinterpretation: correlation 00:44:15 7 History of statistical science 00:50:22 8 Applications 00:50:31 8.1 Applied statistics, theoretical statistics and mathematical statistics 00:51:28 8.2 Machine learning and data mining 00:51:51 8.3 Statistics in society 00:52:24 8.4 Statistical computing 00:54:07 8.5 Statistics applied to mathematics or the arts 00:56:38 9 Specialized disciplines 00:58:03 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: 0.7264142086864527 Voice name: en-US-Wavenet-E "I cannot teach anybody anything, I can only make them think." - Socrates SUMMARY ======= Statistics is a branch of mathematics dealing with data collection, organization, analysis, interpretation and presentation. In applying statistics to, for example, a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model process to be studied. Populations can be diverse topics such as "all people living in a country" or "every atom composing a crystal". Statistics deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments. See glossary of probability and statistics. When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can reasonably extend from the sample to the population as a whole. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation. Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation). Descriptive statistics are most often concerned with two sets of properties of a distribution (sample or population): central tendency (or location) seeks to characterize the distribution's central or typical value, while dispersion (or variability) characterizes the extent to which members of the distribution depart from its center and each other. Inferences on mathematical statistics are made under the framework of probability theory, which deals with the analysis of random phenomena. A standard statistical procedure involves the test of the relationship between two statistical data sets, or a data set and synthetic data drawn from an idealized model. A hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. Rejecting or disproving the null hypothesis is done using statistical tests that quantify the sense in which the null can be proven false, given the data that are used in the test. Working from a null hypothesis, two basic forms of error are recognized: Type I errors (null hypothesis is falsely rejected giving a "false positive") and Type II errors (null hypothesis fails to be rejected and an ac ...
Views: 3 wikipedia tts
The best stats you've ever seen | Hans Rosling
 
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http://www.ted.com With the drama and urgency of a sportscaster, statistics guru Hans Rosling uses an amazing new presentation tool, Gapminder, to present data that debunks several myths about world development. Rosling is professor of international health at Sweden's Karolinska Institute, and founder of Gapminder, a nonprofit that brings vital global data to life. (Recorded February 2006 in Monterey, CA.) TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes. TED stands for Technology, Entertainment, Design, and TEDTalks cover these topics as well as science, business, development and the arts. Closed captions and translated subtitles in a variety of languages are now available on TED.com, at http://www.ted.com/translate. Follow us on Twitter http://www.twitter.com/tednews Checkout our Facebook page for TED exclusives https://www.facebook.com/TED
Views: 2863787 TED
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: 1 wikipedia tts
Statistics | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/Statistics 00:03:43 1 Scope 00:04:45 1.1 Mathematical statistics 00:05:10 2 Overview 00:07:15 3 Data collection 00:07:24 3.1 Sampling 00:09:18 3.2 Experimental and observational studies 00:10:49 3.2.1 Experiments 00:13:06 3.2.2 Observational study 00:13:52 4 Types of data 00:16:35 5 Terminology and theory of inferential statistics 00:16:46 5.1 Statistics, estimators and pivotal quantities 00:18:55 5.2 Null hypothesis and alternative hypothesis 00:20:16 5.3 Error 00:22:45 5.4 Interval estimation 00:24:45 5.5 Significance 00:28:05 5.6 Examples 00:28:17 6 Misuse 00:31:16 6.1 Misinterpretation: correlation 00:32:14 7 History of statistical science 00:36:38 8 Applications 00:36:47 8.1 Applied statistics, theoretical statistics and mathematical statistics 00:37:31 8.2 Machine learning and data mining 00:37:49 8.3 Statistics in society 00:38:15 8.4 Statistical computing 00:39:32 8.5 Statistics applied to mathematics or the arts 00:41:22 9 Specialized disciplines 00:42:26 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 "There is only one good, knowledge, and one evil, ignorance." - Socrates SUMMARY ======= Statistics is a branch of mathematics dealing with data collection, organization, analysis, interpretation and presentation. In applying statistics to, for example, a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model process to be studied. Populations can be diverse topics such as "all people living in a country" or "every atom composing a crystal". Statistics deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments. See glossary of probability and statistics. When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can reasonably extend from the sample to the population as a whole. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation. Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation). Descriptive statistics are most often concerned with two sets of properties of a distribution (sample or population): central tendency (or location) seeks to characterize the distribution's central or typical value, while dispersion (or variability) characterizes the extent to which members of the distribution depart from its center and each other. Inferences on mathematical statistics are made under the framework of probability theory, which deals with the analysis of random phenomena. A standard statistical procedure involves the test of the relationship between two statistical data sets, or a data set and synthetic data drawn from an idealized model. A hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. Rejecting or disproving the null hypothesis is done using statistical tests that quantify the sense in which the null can be proven false, given the data that are used in the test. Working from a null hypothesis, two basic forms of error are recognized: Type I errors (null hypothesis is falsely rejected giving a "false positive") and Type II errors (null hypothesis fails to be rejected and an actual difference between populations is missed giving a "false ...
Views: 2 wikipedia tts
What is CLUSTER ANALYSIS? What does CLUSTER ANALYSIS mean? CLUSTER ANALYSIS meaning & explanation
 
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What is CLUSTER ANALYSIS? What does CLUSTER ANALYSIS mean? CLUSTER ANALYSIS meaning - CLUSTER ANALYSIS definition - CLUSTER ANALYSIS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics. Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It is often necessary to modify data preprocessing and model parameters until the result achieves the desired properties. Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek ß????? "grape") and typological analysis. The subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. This often leads to misunderstandings between researchers coming from the fields of data mining and machine learning, since they use the same terms and often the same algorithms, but have different goals. Cluster analysis was originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology by Zubin in 1938 and Robert Tryon in 1939 and famously used by Cattell beginning in 1943 for trait theory classification in personality psychology.
Views: 7119 The Audiopedia
What is GEOSPATIAL ANALYSIS? What does GEOSPATIAL ANALYSIS mean? GEOSPATIAL ANALYSIS meaning
 
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What is GEOSPATIAL ANALYSIS? What does GEOSPATIAL ANALYSIS mean? GEOSPATIAL ANALYSIS meaning - GEOSPATIAL ANALYSIS definition - GEOSPATIAL ANALYSIS 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 Geospatial analysis, or just spatial analysis, is an approach to applying statistical analysis and other analytic techniques to data which has a geographical or spatial aspect. Such analysis would typically employ software capable of rendering maps processing spatial data, and applying analytical methods to terrestrial or geographic datasets, including the use of geographic information systems and geomatics. Geographic information systems (GIS), which is a large domain that provides a variety of capabilities designed to capture, store, manipulate, analyze, manage, and present all types of geographical data, and utilizes geospatial analysis in a variety of contexts, operations and applications. Geospatial analysis, using GIS, was developed for problems in the environmental and life sciences, in particular ecology, geology and epidemiology. It has extended to almost all industries including defense, intelligence, utilities, Natural Resources (i.e. Oil and Gas, Forestry ... etc.), social sciences, medicine and Public Safety (i.e. emergency management and criminology), disaster risk reduction and management (DRRM), and climate change adaptation (CCA). Spatial statistics typically result primarily from observation rather than experimentation. Vector-based GIS is typically related to operations such as map overlay (combining two or more maps or map layers according to predefined rules), simple buffering (identifying regions of a map within a specified distance of one or more features, such as towns, roads or rivers) and similar basic operations. This reflects (and is reflected in) the use of the term spatial analysis within the Open Geospatial Consortium (OGC) “simple feature specifications”. For raster-based GIS, widely used in the environmental sciences and remote sensing, this typically means a range of actions applied to the grid cells of one or more maps (or images) often involving filtering and/or algebraic operations (map algebra). These techniques involve processing one or more raster layers according to simple rules resulting in a new map layer, for example replacing each cell value with some combination of its neighbours’ values, or computing the sum or difference of specific attribute values for each grid cell in two matching raster datasets. Descriptive statistics, such as cell counts, means, variances, maxima, minima, cumulative values, frequencies and a number of other measures and distance computations are also often included in this generic term spatial analysis. Spatial analysis includes a large variety of statistical techniques (descriptive, exploratory, and explanatory statistics) that apply to data that vary spatially and which can vary over time. Some more advanced statistical techniques include Getis-ord Gi* or Anselin Local Moran's I which are used to determine clustering patterns of spatially referenced data. Geospatial analysis goes beyond 2D and 3D mapping operations and spatial statistics. It includes: Surface analysis —in particular analysing the properties of physical surfaces, such as gradient, aspect and visibility, and analysing surface-like data “fields”; Network analysis — examining the properties of natural and man-made networks in order to understand the behaviour of flows within and around such networks; and locational analysis. GIS-based network analysis may be used to address a wide range of practical problems such as route selection and facility location (core topics in the field of operations research, and problems involving flows such as those found in hydrology and transportation research. In many instances location problems relate to networks and as such are addressed with tools designed for this purpose, but in others existing networks may have little or no relevance or may be impractical to incorporate within the modeling process....
Views: 2195 The Audiopedia
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: 265 CyberScrap youpul
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: 2898 The Audiopedia
SEO - Keyword discovery tool - Mozenda Data Mining - analyticip.com
 
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http://www.analyticip.com statistical data mining, statistical analysis and data mining, data mining statistics web analytics, web analytics 2.0, web analytics services, open source web analytics, web analytics consulting, , what is data mining, data mining algorithms, data mining concepts, define data mining, data visualization tools, data mining tools, data analysis tools, data collection tools, data analytics tools, data extraction tools, tools for data mining, data scraping tools, list of data mining tools, software data mining, best data mining software, data mining software, data mining softwares, software for data mining, web mining, web usage mining, web content mining, web data mining software, data mining web, data mining applications, applications of data mining, application data mining, open source data mining, open source data mining tools, data mining for business intelligence, business intelligence data mining, business intelligence and data mining, web data extraction, web data extraction software, easy web extract, web data extraction tool, extract web data
Views: 77 Data Analytics
Statistics | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: 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. You can find other Wikipedia audio articles too at: https://www.youtube.com/channel/UCuKfABj2eGyjH3ntPxp4YeQ You can upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts "The only true wisdom is in knowing you know nothing." - Socrates SUMMARY ======= Statistics is a branch of mathematics dealing with data collection, organization, analysis, interpretation and presentation. In applying statistics to, for example, a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model process to be studied. Populations can be diverse topics such as "all people living in a country" or "every atom composing a crystal". Statistics deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments. See glossary of probability and statistics. When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can reasonably extend from the sample to the population as a whole. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation. Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation). Descriptive statistics are most often concerned with two sets of properties of a distribution (sample or population): central tendency (or location) seeks to characterize the distribution's central or typical value, while dispersion (or variability) characterizes the extent to which members of the distribution depart from its center and each other. Inferences on mathematical statistics are made under the framework of probability theory, which deals with the analysis of random phenomena. A standard statistical procedure involves the test of the relationship between two statistical data sets, or a data set and synthetic data drawn from an idealized model. A hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. Rejecting or disproving the null hypothesis is done using statistical tests that quantify the sense in which the null can be proven false, given the data that are used in the test. Working from a null hypothesis, two basic forms of error are recognized: Type I errors (null hypothesis is falsely rejected giving a "false positive") and Type II errors (null hypothesis fails to be rejected and an actual difference between populations is missed giving a "false negative"). Multiple problems have come to be associated with this framework: ranging from obtaining a sufficient sample size to specifying an adequate null hypothesis.Measurement processes that generate statistical data are also subject to error. Many of these errors are classified as random (noise) or systematic (bias), but other types of errors (e.g., blunder, such as when an analyst reports incorrect units) can also be important. The presence of missing data or censoring may result in biased estimates and specific techniques have been developed to address these problems. Statistics can be said to have begun in ancient civilization, going back at least to the 5th century BC, but it was not until the 18th century that it started to draw more heavily from calculus and probability theory. In more recent years statistics has relied more on statistical software to produce tests such as descriptive analysis.
Views: 8 wikipedia tts
Wikipedia is a Fraud
 
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@ wheezerYT hey cheif why don't u look up the trade mark and copyright on that dumb fuck.....lol, oh and when u place 6th in the country, 4 spots out of the Olympics, and get over 100k in scholarships to college, u can try to claim some bullshit like 15 minutes of fame. I have been on more front pages, and repped by more media outlets, for my sub par accomplishments, than u ever will, for even ur brightest day on planet earth. Sad truth is, ur a low life faggot, and u couldn't even boast one hit on Google if u searched ur name. Get over it bro, I'm not hating on u personally, so its obvious u have some deep seeded psychological infatuation with me. And to the stupid fucking Ass clown talking about hard beer, and ultra being water.......ultra, 4.1% alcohol content, budlight, 4.2........read a article of factual information before u try to sound cool, and if u wanna play that fat hippie card, than go ahead. Drink, imported bullshit that makes u feel manly and bloated, I'm good with being like the other 92% of little pussies in America who consume, and prefer brand name light beer. With that said, I hope all u fat, balding, limp Dick days enjoyed looking at my gorgeous face, and YouTube page. Tell fag fuck Jay Luis, I apreciate the free traffic. And as always, I'm still the most, referenced, trafficked person ever on home boys website. Get Some WHOIS search results for: JASONSTACKHOUSE.COM (Registered) Is this your domain? Add hosting, email and more. Want to buy this domain? Get it with our Domain Buy service. What is a Premium Domain Name? Premium Domain names are now more valuable then typical domain names because they are based on common words and phrases. GoDaddy.com does not own these domain names, but is one of the few registrars offering them on behalf of their third-party owners. 2011-04-23 The data contained in GoDaddy.com, Inc.'s WHOIS database, while believed by the company to be reliable, is provided "as is" with no guarantee or warranties regarding its accuracy. This information is provided for the sole purpose of assisting you in obtaining information about domain name registration records. Any use of this data for any other purpose is expressly forbidden without the prior written permission of GoDaddy.com, Inc. By submitting an inquiry, you agree to these terms of usage and limitations of warranty. In particular, you agree not to use this data to allow, enable, or otherwise make possible, dissemination or collection of this data, in part or in its entirety, for any purpose, such as the transmission of unsolicited advertising and solicitations of any kind, including spam. You further agree not to use this data to enable high volume, automated or robotic electronic processes designed to collect or compile this data for any purpose, including mining this data for your own personal or commercial purposes. Please note: the registrant of the domain name is specified in the "registrant" field. In most cases, GoDaddy.com, Inc. is not the registrant of domain names listed in this database. Registrant: Jason Stackhouse 1462 Valley Green Dr. Tallahassee, Florida 32303 United States Registered through: GoDaddy.com, Inc. (http://www.godaddy.com) Domain Name: JASONSTACKHOUSE.COM Created on: 25-Apr-10 Expires on: 25-Apr-12 Last Updated on: 29-Mar-11 Administrative Contact: Stackhouse, Jason [email protected] 1462 Valley Green Dr. Tallahassee, Florida 32303 United States (610) 809-4377 Technical Contact: Stackhouse, Jason [email protected] 1462 Valley Green Dr. Tallahassee, Florida 32303 United States (610) 809-4377 Domain servers in listed order: NS01.DOMAINCONTROL.COM NS02.DOMAINCONTROL.COM http://who.godaddy.com/whois.aspx?k=L5QC6WvmiRveZZFa2AFrcYSBjqLXx8Ea&domain=jasonstackhouse.com&prog_id=GoDaddy
Views: 3424 Jason Stackhouse
Bootstrap aggregating bagging
 
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This video is part of the Udacity course "Machine Learning for Trading". Watch the full course at https://www.udacity.com/course/ud501
Views: 83010 Udacity
What is BAYESIAN STATISTICS? What does BAYESIAN STATISTICS mean? BAYESIAN STATISTICS meaning
 
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What is BAYESIAN STATISTICS? What does BAYESIAN STATISTICS mean? BAYESIAN STATISTICS meaning - BAYESIAN STATISTICS definition - BAYESIAN STATISTICS 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 Bayesian statistics, named for Thomas Bayes (1701–1761), is a theory in the field of statistics in which the evidence about the true state of the world is expressed in terms of degrees of belief known as Bayesian probabilities. Such an interpretation is only one of a number of interpretations of probability and there are other statistical techniques that are not based on 'degrees of belief'. One of the key ideas of Bayesian statistics is that "probability is orderly opinion, and that inference from data is nothing other than the revision of such opinion in the light of relevant new information." The general set of statistical techniques can be divided into a number of activities, many of which have special Bayesian versions. Bayesian inference is an approach to statistical inference that is distinct from frequentist inference. It is specifically based on the use of Bayesian probability to summarize evidence. The formulation of statistical models using Bayesian statistics has the identifying feature of requiring the specification of prior distributions for any unknown parameters. Indeed, parameters of prior distributions may themselves have prior distributions, leading to Bayesian hierarchical modeling, or may be interrelated, leading to Bayesian networks. The Bayesian design of experiments includes a concept called 'influence of prior beliefs'. This approach uses sequential analysis techniques to include the outcome of earlier experiments in the design of the next experiment. This is achieved by updating 'beliefs' through the use of prior and posterior distribution. This allows the design of experiments to make good use of resources of all types. An example of this is the multi-armed bandit problem. Statistical graphics includes methods for data exploration, for model validation, etc. The use of certain modern computational techniques for Bayesian inference, specifically the various types of Markov chain Monte Carlo techniques, have led to the need for checks, often made in graphical form, on the validity of such computations in expressing the required posterior distributions.
Views: 453 The Audiopedia
Data Mining & Business Intelligence | Tutorial #3 | Issues in Data Mining
 
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This video addresses the issues which are there involved in Data Mining system. Watch now! #RanjiRaj #DataMining #DMIssues Follow me on Instagram 👉 https://www.instagram.com/reng_army/ Visit my Profile 👉 https://www.linkedin.com/in/reng99/ Support my work on Patreon 👉 https://www.patreon.com/ranjiraj
Views: 3503 Ranji Raj
What is ANOMALY DETECTION? What does ANOMALY DETECTION mean? ANOMALY DETECTION meaning
 
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What is ANOMALY DETECTION? What does ANOMALY DETECTION mean? ANOMALY DETECTION meaning - ANOMALY DETECTION definition - ANOMALY DETECTION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. In data mining, anomaly detection (also outlier detection) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset.[1] Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.[2] In particular in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular unsupervised methods) will fail on such data, unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro clusters formed by these patterns.[3] Three broad categories of anomaly detection techniques exist.[1] Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then testing the likelihood of a test instance to be generated by the learnt model.
Views: 6207 The Audiopedia
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: 905 Chris Menard
สอน Data Mining ด้วย Excel: การทำ Cluster Analysis โดยใช้ข้อมูล Fisher's Iris Data Set
 
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สอนวิธีการใช้ Data mining add-in บน Excel เพื่อทำ Cluster Analysis/Detect Categories โดยใช้ข้อมูลดอก Iris ของ Fisher (https://en.wikipedia.org/wiki/Iris_flower_data_set) เป็นตัวอย่าง ==ดาวน์โหลดไฟล์ตัวอย่างได้ที่ https://goo.gl/TKBTBE
Views: 2457 prasertcbs
Bioinformatics part 3 Sequence alignment introduction
 
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This Bioinformatics lecture explains the details about the sequence alignment. The mechanism and protocols of sequence alignment is explained in this video lecture on Bioinformatics. For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences.[1] Aligned sequences of nucleotide or amino acid residues are typically represented as rows within a matrix. Gaps are inserted between the residues so that identical or similar characters are aligned in successive columns. Sequence alignments are also used for non-biological sequences, such as those present in natural language or in financial data. Very short or very similar sequences can be aligned by hand. However, most interesting problems require the alignment of lengthy, highly variable or extremely numerous sequences that cannot be aligned solely by human effort. Instead, human knowledge is applied in constructing algorithms to produce high-quality sequence alignments, and occasionally in adjusting the final results to reflect patterns that are difficult to represent algorithmically (especially in the case of nucleotide sequences). Computational approaches to sequence alignment generally fall into two categories: global alignments and local alignments. Calculating a global alignment is a form of global optimization that "forces" the alignment to span the entire length of all query sequences. By contrast, local alignments identify regions of similarity within long sequences that are often widely divergent overall. Local alignments are often preferable, but can be more difficult to calculate because of the additional challenge of identifying the regions of similarity. A variety of computational algorithms have been applied to the sequence alignment problem. These include slow but formally correct methods like dynamic programming. These also include efficient, heuristic algorithms or probabilistic methods designed for large-scale database search, that do not guarantee to find best matches. Global alignments, which attempt to align every residue in every sequence, are most useful when the sequences in the query set are similar and of roughly equal size. (This does not mean global alignments cannot end in gaps.) A general global alignment technique is the Needleman--Wunsch algorithm, which is based on dynamic programming. Local alignments are more useful for dissimilar sequences that are suspected to contain regions of similarity or similar sequence motifs within their larger sequence context. The Smith--Waterman algorithm is a general local alignment method also based on dynamic programming. 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: 165741 Shomu's Biology
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: 3840 The Audiopedia
Astrostatistics | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/Astrostatistics 00:00:34 Professional association 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: 0.9019242587676262 Voice name: en-US-Wavenet-F "I cannot teach anybody anything, I can only make them think." - Socrates SUMMARY ======= Astrostatistics is a discipline which spans astrophysics, statistical analysis and data mining. It is used to process the vast amount of data produced by automated scanning of the cosmos, to characterize complex datasets, and to link astronomical data to astrophysical theory. Many branches of statistics are involved in astronomical analysis including nonparametrics, multivariate regression and multivariate classification, time series analysis, and especially Bayesian inference.
Views: 7 wikipedia tts
Getting Wikipedia Tables into a JSON Format
 
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Can't find the data you need? Perhaps you're looking in the wrong place. Article from this video so you can follow along: http://en.wikipedia.org/wiki/List_of_U.S._state_abbreviations JSFiddle from the end of the video: http://jsfiddle.net/fE5Bw/
Views: 3678 aboutscript
Analysis of competing hypotheses | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/Analysis_of_competing_hypotheses 00:01:09 1 Process 00:04:10 2 Strengths 00:04:48 3 Weaknesses 00:07:51 3.1 Structured analysis of competing hypotheses 00:08:58 3.2 Other approaches to formalism 00:10:02 4 Automation 00:11:00 5 See also 00:11:09 6 Notes 00:11:18 7 External links 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: 0.8784042140836096 Voice name: en-GB-Wavenet-A "I cannot teach anybody anything, I can only make them think." - Socrates SUMMARY ======= The analysis of competing hypotheses (ACH) allegedly provides an unbiased methodology for evaluating multiple competing hypotheses for observed data. It was developed by Richards (Dick) J. Heuer, Jr., a 45-year veteran of the Central Intelligence Agency, in the 1970s for use by the Agency. ACH is used by analysts in various fields who make judgments that entail a high risk of error in reasoning. It helps an analyst overcome, or at least minimize, some of the cognitive limitations that make prescient intelligence analysis so difficult to achieve.ACH was a step forward in intelligence analysis methodology, but it was first described in relatively informal terms. Producing the best available information from uncertain data remains the goal of researchers, tool-builders, and analysts in industry, academia and government. Their domains include data mining, cognitive psychology and visualization, probability and statistics, etc. Abductive reasoning is an earlier concept with similarities to ACH.
Views: 8 wikipedia tts
Biometry | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/Biostatistics 00:00:35 1 History 00:00:44 1.1 Biostatistics and Genetics 00:05:41 1.2 Biostatistics and Medicine 00:08:03 2 Research planning 00:09:01 2.1 Research question 00:09:42 2.2 Hypothesis definition 00:11:44 2.3 Sampling 00:13:22 2.4 Experimental design 00:14:58 2.5 Data collection 00:16:34 3 Analysis and data interpretation 00:16:45 3.1 Descriptive Tools 00:24:01 3.2 Inferential Statistics 00:24:18 4 Statistical considerations 00:24:24 4.1 Power and statistical error 00:27:30 4.2 p-value 00:27:41 4.3 Multiple testing 00:28:30 4.4 Mis-specification and robustness checks 00:29:12 4.5 Model selection criteria 00:29:53 5 Developments and Big Data 00:30:47 5.1 Use in high-throughput data 00:30:59 5.2 Bioinformatics advances in databases, data mining, and biological interpretation 00:31:49 5.3 Use of computationally intensive methods 00:32:00 6 Applications 00:32:21 6.1 Public health 00:32:32 6.2 Quantitative genetics 00:33:04 6.3 Expression data 00:35:49 6.4 Other studies 00:39:09 7 Tools 00:40:05 8 Scope and training programs 00:40:15 9 Specialized journals 00:41:12 10 See also 00:44:54 11 References 00:46:41 12 External links 00:47:13 Tools 00:50:31 Scope and training programs 00:52:39 Specialized journals 00:53:43 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: 0.7873757339873362 Voice name: en-AU-Wavenet-A "I cannot teach anybody anything, I can only make them think." - Socrates SUMMARY ======= Biostatistics are the application of statistics to a wide range of topics in biology. It encompasses the design of biological experiments, especially in medicine, pharmacy, agriculture and fishery; the collection, summarization, and analysis of data from those experiments; and the interpretation of, and inference from, the results. A major branch is medical biostatistics, which is exclusively concerned with medicine and health.
Views: 0 wikipedia tts
How to Perform K-Means Clustering in R Statistical Computing
 
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In this video I go over how to perform k-means clustering using r statistical computing. Clustering analysis is performed and the results are interpreted. http://www.influxity.com
Views: 198587 Influxity
List of open-source machine learning software | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/Machine_learning 00:01:07 1 Overview of Machine Learning 00:02:17 1.1 Machine learning tasks 00:05:41 2 History and relationships to other fields 00:08:03 2.1 Relation to data mining 00:09:26 2.2 Relation to optimization 00:10:15 2.3 Relation to statistics 00:11:04 3 Theory 00:13:04 4 Approaches 00:13:13 4.1 Types of learning algorithms 00:13:33 4.1.1 Supervised and semi-supervised learning 00:15:18 4.1.2 Unsupervised learning 00:16:42 4.1.3 Reinforcement learning 00:17:45 4.2 Processes and techniques 00:18:03 4.2.1 Feature learning 00:20:44 4.2.2 Sparse dictionary learning 00:21:46 4.2.3 Anomaly detection 00:23:38 4.2.4 Decision trees 00:24:40 4.2.5 Association rules 00:29:10 4.3 Models 00:29:18 4.3.1 Artificial neural networks 00:31:57 4.3.2 Support vector machines 00:32:52 4.3.3 Bayesian networks 00:33:47 4.3.4 Genetic algorithms 00:34:24 5 Applications 00:35:42 6 Limitations 00:36:35 6.1 Bias 00:38:00 7 Model assessments 00:39:35 8 Ethics 00:41:13 9 Software 00:41:28 9.1 Free and open-source software 00:41:37 9.2 Proprietary software with free and open-source editions 00:41:48 9.3 Proprietary software 00:41:57 10 Journals 00:42:13 11 Conferences 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: 0.8261455844014172 Voice name: en-AU-Wavenet-D "I cannot teach anybody anything, I can only make them think." - Socrates SUMMARY ======= Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in the applications of email filtering, detection of network intruders, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.
Views: 2 Subhajit Sahu
Lecture 12 | Machine Learning (Stanford)
 
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Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses unsupervised learning in the context of clustering, Jensen's inequality, mixture of Gaussians, and expectation-maximization. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 CS 229 Course Website: http://www.stanford.edu/class/cs229/ Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford
Views: 119378 Stanford
Network science | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/Network_science 00:00:47 1 Background and history 00:04:15 1.1 Department of Defense initiatives 00:07:43 2 Network properties 00:08:07 2.1 Size 00:10:10 2.2 Density 00:12:04 2.3 Planar Network Density 00:13:16 2.4 Average degree 00:15:16 2.5 Average shortest path length (or characteristic path length) 00:16:50 2.6 Diameter of a network 00:17:25 2.7 Clustering coefficient 00:19:30 2.8 Connectedness 00:20:22 2.9 Node centrality 00:21:55 2.10 Node influence 00:22:31 3 Network models 00:22:55 3.1 Erdős–Rényi random graph model 00:26:01 3.2 Configuration model 00:31:39 3.3 Watts–Strogatz small world model 00:33:16 3.4 Barabási–Albert (BA) preferential attachment model 00:35:43 3.4.1 Mediation-driven attachment (MDA) model 00:39:28 3.5 Fitness model 00:43:32 4 Network analysis 00:43:41 4.1 Social network analysis 00:45:04 4.2 Dynamic network analysis 00:45:59 4.3 Biological network analysis 00:46:49 4.4 Link analysis 00:47:59 4.4.1 Network robustness 00:48:26 4.4.2 Pandemic analysis 00:48:43 4.4.2.1 Susceptible to infected 00:49:39 4.4.2.2 Infected to recovered 00:50:34 4.4.2.3 Infectious period 00:51:16 4.4.3 Web link analysis 00:51:54 4.4.3.1 PageRank 00:53:20 4.4.3.1.1 Random jumping 00:55:23 4.5 Centrality measures 00:57:27 5 Spread of content in networks 00:58:51 5.1 The SIR model 01:03:28 5.2 The master equation approach 01:09:09 6 Interdependent networks 01:09:52 7 Multilayer networks 01:10:31 8 Network optimization 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 ======= Network science is an academic field which studies complex networks such as telecommunication networks, computer networks, biological networks, cognitive and semantic networks, and social networks, considering distinct elements or actors represented by nodes (or vertices) and the connections between the elements or actors as links (or edges). The field draws on theories and methods including graph theory from mathematics, statistical mechanics from physics, data mining and information visualization from computer science, inferential modeling from statistics, and social structure from sociology. The United States National Research Council defines network science as "the study of network representations of physical, biological, and social phenomena leading to predictive models of these phenomena."
Views: 2 wikipedia tts
K-means clustering: how it works
 
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Full lecture: http://bit.ly/K-means The K-means algorithm starts by placing K points (centroids) at random locations in space. We then perform the following steps iteratively: (1) for each instance, we assign it to a cluster with the nearest centroid, and (2) we move each centroid to the mean of the instances assigned to it. The algorithm continues until no instances change cluster membership.
Views: 508077 Victor Lavrenko
Machine Learning in iOS - Live Tutorial Session - RWDevCon 2017
 
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Machine Learning. Convolutional Neural Networks. Deep Learning Neural Networks. What is all the hype about? What are these technologies, what are they good for, and can we use them for anything useful right now? This session requires no background in any of these areas, and will introduce you to machine learning on iOS with a worked example. Download course materials here: https://store.raywenderlich.com/downloads/812 Watch the full course here: https://store.raywenderlich.com/products/rwdevcon-2017-vault-bundle --- About www.raywenderlich.com: https://www.raywenderlich.com/384-reactive-programming-with-rxandroid-in-kotlin-an-introduction raywenderlich.com is a website focused on developing high quality programming tutorials. Our goal is to take the coolest and most challenging topics and make them easy for everyone to learn – so we can all make amazing apps. We are also focused on developing a strong community. Our goal is to help each other reach our dreams through friendship and cooperation. As you can see below, a bunch of us have joined forces to make this happen: authors, editors, subject matter experts, app reviewers, and most importantly our amazing readers! --- From Wikipedia: https://en.wikipedia.org/wiki/Machine_learning Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed. The name machine learning was coined in 1959 by Arthur Samuel. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders, and computer vision. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining,[5] where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data
Views: 452 raywenderlich.com
Web Scraping With Python - Wikipedia Words Frequency Analysis Using Matplotlib
 
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Web Scraping With Python - Wikipedia Words Frequency Analysis Using Matplotlib
Views: 1744 Martin M
Multilingual Text Mining: Lost in Translation, Found in Native Language Mining - Rohini Srihari
 
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There has been a meteoric rise in the amount of multilingual content on the web. This is primarily due to social media sites such as Facebook, and Twitter, as well as blogs, discussion forums, and reader responses to articles on traditional news sites. Language usage statistics indicate that Chinese is a very close second to English, and could overtake it to become the dominant language on the web. It is also interesting to see the explosive growth in languages such as Arabic. The availability of this content warrants a discussion on how such information can be effectively utilized. Such data can be mined for many purposes including business-related competitive insight, e-commerce, as well as citizen response to current issues. This talk will begin with motivations for multilingual text mining, including commercial and societal applications, digital humanities applications such as semi-automated curation of online discussion forums, and lastly, government applications, where the value proposition (benefits, costs and value) is different, but equally compelling. There are several issues to be touched upon, beginning with the need for processing native language, as opposed to using machine translated text. In tasks such as sentiment or behaviour analysis, it can certainly be argued that a lot is lost in translation, since these depend on subtle nuances in language usage. On the other hand, processing native language is challenging, since it requires a multitude of linguistic resources such as lexicons, grammars, translation dictionaries, and annotated data. This is especially true for "resourceMpoor languages" such as Urdu, and Somali, languages spoken in parts of the world where there is considerable focus nowadays. The availability of content such as multilingual Wikipedia provides an opportunity to automatically generate needed resources, and explore alternate techniques for language processing. The rise of multilingual social media also leads to interesting developments such as code mixing, and code switching giving birth to "new" languages such as Hinglish, Urdish and Spanglish! This phenomena exhibits both pros and cons, in addition to posing difficult challenges to automatic natural language processing. But there is also an opportunity to use crowd-sourcing to preserve languages and dialects that are gradually becoming extinct. It is worthwhile to explore frameworks for facilitating such efforts, which are currently very ad hoc. In summary, the availability of multilingual data provides new opportunities in a variety of applications, and effective mining could lead to better cross-cultural communication. Questions Addressed (i) Motivation for mining multilingual text. (ii) The need for processing native language (vs. machine translated text). (iii) Multilingual Social Media: challenges and opportunities, e.g., preserving languages and dialects.
Views: 1452 UA German Department
YaMA Tutorial - Exporting to Wiki and Comma Separated Values formats
 
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In this segment, let us look at how you can export information to inter-operate with some of the other applications that you use, like the Wiki and the applications using the Comma Separated Values format.
Views: 67 teamyama
What Is The Bayesian Model??
 
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Bayesian network wikipediabayesian wikipedia. The active contour model described in section gif is an example of a driven segmentation technique. Bayesian modeling definition of bayesian by the free a brief overview model averaging department statistics what's it all about? Statistical modeling, causal components models university california, santa structure world, uncertainty, behavior methodology for colorado state project euclid. Bayes' theorem is somewhat secondary to the concept of a prior Jun 20, 2016 an important part bayesian inference establishment parameters and models. Bayesian modeling, inference and prediction school of engineeringbayesian model. Bayesian models that have been used in bayesian model averaging provides a coherent approach for accounting methodology to perform specific classes is 1999, vol4, 382 417. They are not bayesian modeling. The interview is here at kdnuggets news. Statistical models based on the classical (or frequentist) paradigm treat parameters of model as fixed, unknown constants. The kalman part ii how to make a bayesian model from these data? (these questions are shared with other models) inference inverts this generative process. Bayesian modelling cambridge machine learning group. Bayes rule) allows us to infer unknown quantities, adapt our models, a bayesian model is statistical made of the pair prior x likelihood posterior marginal. Bayesian model averaging a tutorial dec 13, 2016 kevin gray sent me bunch of questions on bayesian statistics and i responded. Bayesian model averaging a tutorialhoeting, david madigan, adrian evolinsky. Q550 models in is based on a bayes engine belief goverened by the theorem way to update beliefs light of new bayesian modeling, inference and predictionapplied mathematics statistics. Abstract jul 28, 2017 author summary the intestinal epithelium is an important model system for studying dynamics and regulation of multicellular populations bayes factor can be combined with prior odds predictions in bayesian averaging (bma). For some reason video created by university of california, santa cruz for the course 'bayesian statistics techniques and models'. The formulation of statistical models using bayesian statistics has the identifying feature requiring specification prior a network, bayes belief bayes(ian) model or probabilistic directed acyclic graphical is (a type model) that represents set random variables and their conditional dependencies via graph (dag) forms uncertainty noise associated with our then inverse probability (i. Wikipedia wiki bayesian_statistics url? Q webcache. University of california, santa cruz jul 7, 1997 the bayesian model. Redirected from bayesian modeling) related to modeling approach, analysis, updating model selection, stat 882 au 2006, dec 6hoeting, david madigan, adrian evolinsky. Bayesian network wikipedia. Statistical modeling, bayesian models using bayes rule have been used to explain many results in perception, action, neural coding, and
Views: 265 Pan Pan 1
Rand Index in Statistics - A Worked Example - Cluster Analysis
 
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Hi there! This is an application of the Rand Index in Statistics. I hope that the chosen example makes it easy for you to understand the Rand Index. If you have any questions, post them and I will try to answer as good as I can. I have also uploaded the slides just in case you want them for yourself offline. http://ubuntuone.com/2hi9bTo2poBERDB5PGpYr3 If you have not yet looked at the Wikipedia article about the Rand Index, then do so: http://en.wikipedia.org/wiki/Rand_index It's not a must in order to follow the example but it gives a much wider definition of the Rand Index.
Views: 11762 BelVecchioUK
Combining R with Java for Data Analysis
 
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Java is a general-purpose language and is not particularly well suited for performing statistical analysis. Special languages and software environments have been created by and for statisticians to use. Statisticians think about programming and data analysis much different from Java programmers. These languages and tools make it easy to perform very sophisticated analyses on large data sets easily. Tools, such as R and SAS, contain a large toolbox of statistical tools that are well tested, documented and validated. For data analysis you want to use these tools. In this session we will provide an overview of how to leverage the power of R from Java. R is the leading open source statistical package/language/environment. The first part of the presentation will provide an overview of R focusing on the differences between R and Java at the language level. We’ll also look at some of the basic and more advanced tests to illustrate the power of R. The second half of the presentation will cover how to integrate R and Java using rJava. We’ll look at leverage R from the new Java EE Batching (JSR 352) to provide robust statistical analysis for enterprise applications. Authors: Ryan Cuprak undefined Elsa Cuprak Elsa was a statistician for the Cardiology/Heart Failure and Transplant Departments at Yale School of Medicine. She is an expert in statistics as well as SAS and Excel. Elsa has a masters degree in Actuary Science from the University of Iowa and bachelors in statistics from the University of California Berkley. She worked for several years as an actuary at both Met Life and the West Coast Life Insurance Company.
Views: 12074 Parleys
JMP (statistical software) | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/JMP_(statistical_software) 00:01:17 1 History 00:04:54 2 Software 00:06:43 3 JMP Scripting Language (JSL) 00:07:48 4 Notable applications 00:08:44 5 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: 0.9114366206643958 Voice name: en-GB-Wavenet-B "I cannot teach anybody anything, I can only make them think." - Socrates SUMMARY ======= JMP (pronounced "jump") is a suite of computer programs for statistical analysis developed by the JMP business unit of SAS Institute. It was launched in 1989 to take advantage of the graphical user interface introduced by the Macintosh. It has since been significantly rewritten and made available for the Windows operating system. JMP is used in applications such as Six Sigma, quality control, and engineering, design of experiments, as well as for research in science, engineering, and social sciences. The software can be purchased in any of five configurations: JMP, JMP Pro, JMP Clinical, JMP Genomics and the JMP Graph Builder App for the iPad. JMP can be automated with its proprietary scripting language, JSL. The software is focused on exploratory visual analytics, where users investigate and explore data. These explorations can also be verified by hypothesis testing, data mining, or other analytic methods. In addition, discoveries made through graphical exploration can lead to a designed experiment that can be both designed and analyzed with JMP.
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The Case for Small Data Management
 
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Abstract: Exabytes of data; several hundred thousand TPC-C transactions per second on a single computing core; scale-up to hundreds of cores and a dozen Terabytes of main memory; scale-out to thousands of nodes with close to Petabyte-sized main memories; and massively parallel query processing are a reality in data management. But, hold on a second: for how many users exactly? How many users do you know that really have to handle these kinds of massive datasets and extreme query workloads? On the other hand: how many users do you know that are fighting to handle relatively small datasets, say in the range of a few thousand to a few million rows per table? How come some of the most popular open source DBMS have hopelessly outdated optimizers producing inefficient query plans? How come people don’t care and love it anyway? Could it be that most of the world’s data management problems are actually quite small? How can we increase the impact of database research in areas when datasets are small? What are the typical problems? What does this mean for database research? We discuss research challenges, directions, and a concrete technical solution coined PDbF: Portable Database Files. This is an extended version of an abstract and Gong Show talk presented at CIDR 2015. This talk was held on March 6, 2015 at the German Database Conference BTW in Hamburg. http://www.btw-2015.de/?keynote_dittrich Short CV: Jens Dittrich is a Full Professor of Computer Science in the area of Databases, Data Management, and "Big Data" at Saarland University, Germany. Previous affiliations include U Marburg, SAP AG, and ETH Zurich. He is also associated to CISPA (Center for IT-Security, Privacy and Accountability). He received an Outrageous Ideas and Vision Paper Award at CIDR 2011, a BMBF VIP Grant, a best paper award at VLDB 2014, two CS teaching awards in 2011 and 2013, as well as several presentation awards including a qualification for the interdisciplinary German science slam finals in 2012 and three presentation awards at CIDR (2011, 2013, and 2015). His research focuses on fast access to big data including in particular: data analytics on large datasets, Hadoop MapReduce, main-memory databases, and database indexing. He has been a PC member and/or area chair of prestigious international database conferences such as PVLDB, SIGMOD, and ICDE. Since 2013 he has been teaching his classes on data management as flipped classrooms. See http://datenbankenlernen.de or http://youtube.com/jensdit for a list of freely available videos on database technology in German and English (about 80 videos in German and 80 in English so far). image credits: public domain http://commons.wikimedia.org/wiki/File:The_Blue_Marble.jpg CC, Laura Poitras / Praxis Films http://commons.wikimedia.org/wiki/File:Edward_Snowden-2.jpg http://creativecommons.org/licenses/by/3.0/legalcode istock, voyager624 http://www.istockphoto.com/stock-photo-20540898-blue-digital-tunnel.php?st=0d10b3d http://commons.wikimedia.org/wiki/Category:Egg_sandwich?uselang=de#mediaviewer/File:Sandwich_Huevo_-_Ventana.JPG http://creativecommons.org/licenses/by/3.0/legalcode زرشک CC BY-SA 3.0, http://creativecommons.org/licenses/by-sa/3.0/legalcode public domain, http://en.wikipedia.org/wiki/Tanker_%28ship%29#mediaviewer/File:Sirius_Star_2008b.jpg public domain, http://de.wikipedia.org/wiki/General_Dynamics_F-16#mediaviewer/File:General_Dynamic_F-16_USAF.jpg ©iStock.com: skynesher public domain, http://commons.wikimedia.org/wiki/File:Astronaut-EVA.jpg others: Jens Dittrich, http://datenbankenlernen.de
What is SOFTWARE ANALYTICS? What does SOFTWARE ANALYTICS mean? SOFTWARE ANALYTICS meaning
 
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What is SOFTWARE ANALYTICS? What does SOFTWARE ANALYTICS mean? SOFTWARE ANALYTICS meaning - SOFTWARE ANALYTICS definition - SOFTWARE ANALYTICS 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 Software analytics refers to analytics specific to software systems and related software development processes. It aims at describing, predicting, and improving development, maintenance, and management of complex software systems. Methods and techniques of software analytics typically rely on gathering, analyzing, and visualizing information found in the manifold data sources in the scope of software systems and their software development processes - software analytics "turns it into actionable insight to inform better decisions related to software". Software analytics represents a base component of software diagnosis that generally aims at generating findings, conclusions, and evaluations about software systems and their implementation, composition, behavior, and evolution. Software analytics frequently uses and combines approaches and techniques from statistics, prediction analysis, data mining, and scientific visualization. For example, software analytics can map data by means of software maps that allow for interactive exploration. Data under exploration and analysis by software analytics exists in software lifecycle, including source code, software requirement specifications, bug reports, test cases, execution traces/logs, and real-world user feedback, etc. Data plays a critical role in modern software development, because hidden in the data is the information and insight about the quality of software and services, the experience that software users receive, as well as the dynamics of software development. Insightful information obtained by software analytics is information that conveys meaningful and useful understanding or knowledge towards performing the target task. Typically insightful information cannot be easily obtained by direct investigation on the raw data without the aid of analytic technologies. Actionable information obtained by software analytics is information upon which software practitioners can come up with concrete solutions (better than existing solutions if any) towards completing the target task. Software analytics focuses on trinity of software systems, software users, and software development process: Software systems: Depending on scale and complexity, the spectrum of software systems can span from operating systems for devices to large networked systems that consist of thousands of servers. System quality such as reliability, performance and security, etc., is the key to success of modern software systems. As the system scale and complexity greatly increase, larger amount of data, e.g., run-time traces and logs, is generated; and data becomes a critical means to monitor, analyze, understand and improve system quality. Software users: Users are (almost) always right because ultimately they will use the software and services in various ways. Therefore, it is important to continuously provide the best experience to users. Usage data collected from the real world reveals how users interact with software and services. The data is incredibly valuable for software practitioners to better understand their customers and gain insights on how to improve user experience accordingly. Software development process: Software development has evolved from its traditional form to exhibiting different characteristics. The process is more agile and engineers are more collaborative than that in the past. Analytics on software development data provides a powerful mechanism that software practitioners can leverage to achieve higher development productivity. In general, the primary technologies employed by software analytics include analytical technologies such as machine learning, data mining and pattern recognition, information visualization, as well as large-scale data computing & processing.
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