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Rethinking Research Data | Kristin Briney | TEDxUWMilwaukee
 
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The United States spends billions of dollars every year to publicly support research that has resulted in critical innovations and new technologies. Unfortunately, the outcome of this work, published articles, only provides the story of the research and not the actual research itself. This often results in the publication of irreproducible studies or even falsified findings, and it requires significant resources to discern the good research from the bad. There is way to improve this process, however, and that is to publish both the article and the data supporting the research. Shared data helps researchers identify irreproducible results. Additionally, shared data can be reused in new ways to generate new innovations and technologies. We need researchers to “React Differently” with respect to their data to make the research process more efficient, transparent, and accountable to the public that funds them. Kristin Briney is a Data Services Librarian at the University of Wisconsin-Milwaukee. She has a PhD in physical chemistry, a Masters in library and information studies, and currently works to help researchers manage their data better. She is the author of “Data Management for Researchers” and regular blogs about data best practices at dataabinitio.com. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 7245 TEDx Talks
Sampling & its 8 Types: Research Methodology
 
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Dr. Manishika Jain in this lecture explains the meaning of Sampling & Types of Sampling Research Methodology Population & Sample Systematic Sampling Cluster Sampling Non Probability Sampling Convenience Sampling Purposeful Sampling Extreme, Typical, Critical, or Deviant Case: Rare Intensity: Depicts interest strongly Maximum Variation: range of nationality, profession Homogeneous: similar sampling groups Stratified Purposeful: Across subcategories Mixed: Multistage which combines different sampling Sampling Politically Important Cases Purposeful Sampling Purposeful Random: If sample is larger than what can be handled & help to reduce sample size Opportunistic Sampling: Take advantage of new opportunity Confirming (support) and Disconfirming (against) Cases Theory Based or Operational Construct: interaction b/w human & environment Criterion: All above 6 feet tall Purposive: subset of large population – high level business Snowball Sample (Chain-Referral): picks sample analogous to accumulating snow Advantages of Sampling Increases validity of research Ability to generalize results to larger population Cuts the cost of data collection Allows speedy work with less effort Better organization Greater brevity Allows comprehensive and accurate data collection Reduces non sampling error. Sampling error is however added. Population & Sample @2:25 Sampling @6:30 Systematic Sampling @9:25 Cluster Sampling @ 11:22 Non Probability Sampling @13:10 Convenience Sampling @15:02 Purposeful Sampling @16:16 Advantages of Sampling @22:34 #Politically #Purposeful #Methodology #Systematic #Convenience #Probability #Cluster #Population #Research #Manishika #Examrace For IAS Psychology postal Course refer - http://www.examrace.com/IAS/IAS-FlexiPrep-Program/Postal-Courses/Examrace-IAS-Psychology-Series.htm For NET Paper 1 postal course visit - https://www.examrace.com/CBSE-UGC-NET/CBSE-UGC-NET-FlexiPrep-Program/Postal-Courses/Examrace-CBSE-UGC-NET-Paper-I-Series.htm types of sampling types of sampling pdf probability sampling types of sampling in hindi random sampling cluster sampling non probability sampling systematic sampling
Views: 384242 Examrace
Analysis of Variance (ANOVA)
 
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A description of the concepts behind Analysis of Variance. There is an interactive visualization here: http://demonstrations.wolfram.com/VisualANOVA/ but I have not tried it, and this: http://rpsychologist.com/d3-one-way-anova has another visualization
Views: 557822 J David Eisenberg
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: 541550 Bozeman Science
Novel Data Mining Methods for Virtual Screening - PhD Defense
 
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The Defense of PhD degree in Computer Science in King Abdullah University of Science and Technology (KAUST). Abstract: Drug discovery is a process that takes many years and hundreds of millions of dollars to reveal a confident conclusion about a specific treatment. Part of this sophisticated process is based on preliminary investigations to suggest a set of chemical compounds as candidate drugs for the treatment. Computational resources have been playing a significant role in this part through a step known as virtual screening. From a data mining perspective, availability of rich data resources is key in training prediction models. Yet, the difficulties imposed by the big expansion in data and its dimensionality are inevitable. In this thesis, I address the main challenges that come when data mining techniques are used for virtual screening. In order to achieve an efficient virtual screening using data mining, I start by addressing the problem of feature selection and provide analysis of best ways to describe a chemical compound for an enhanced screening performance. High-throughput screening (HTS) assays data used for virtual screening are characterized by a great class imbalance. To handle this problem of class imbalance, I suggest using a novel algorithm called DRAMOTE to narrow down promising candidate chemicals aimed at interaction with specific molecular targets before they are experimentally evaluated. Existing works are mostly proposed for small-scale virtual screening based on making use of few thousands of interactions. Thus, I propose enabling large-scale (or big) virtual screening through learning millions of interaction while exploiting any relevant dependency for a better accuracy. A novel solution called DRABAL that incorporates structure learning of a Bayesian Network as a step to model dependency between the HTS assays, is showed to achieve significant improvements over existing state-of-the-art approaches.
Views: 482 Othman Soufan
How To... Calculate Pearson's Correlation Coefficient (r) by Hand
 
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Step-by-step instructions for calculating the correlation coefficient (r) for sample data, to determine in there is a relationship between two variables.
Views: 502183 Eugene O'Loughlin
Accuracy and Precision
 
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To see all my Chemistry videos, check out http://socratic.org/chemistry This is an easy to understand introduction to accuracy and precision. We'll play "guess my age," and look at bulls eyes representation of accuracy and precision.
Views: 224291 Tyler DeWitt
Saturday Science at Scripps Research: Ryan Shenvi - Strong Inference
 
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(Visit: http://www.uctv.tv/) The Scripps Research Institutes’ Ryan Shenvi, who searches for ways to synthesize new medicines from both synthetic and natural sources, explores the crucial roles of imagination and critical thinking in the practice of the scientific method. Series: "Saturday Science at The Scripps Research Instititute" [3/2015] [Science] [Show ID: 28683]
How To... Perform a Chi-Square Test (By Hand)
 
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Also known as a "Goodness of Fit" test, use this single sample Chi-Square test to determine if there is a significant difference between Observed and Expected values. This video shows a step-by-step method for calculating Chi-square.
Views: 433548 Eugene O'Loughlin
Research Methods - Introduction
 
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In this video, Dr Greg Martin provides an introduction to research methods, methedology and study design. Specifically he takes a look at qualitative and quantitative research methods including case control studies, cohort studies, observational research etc. Global health (and public health) is truly multidisciplinary and leans on epidemiology, health economics, health policy, statistics, ethics, demography.... the list goes on and on. This YouTube channel is here to provide you with some teaching and information on these topics. I've also posted some videos on how to find work in the global health space and how to raise money or get a grant for your projects. Please feel free to leave comments and questions - I'll respond to all of them (we'll, I'll try to at least). Feel free to make suggestions as to future content for the channel. SUPPORT: —————- This channel has a crowd-funding campaign (please support if you find these videos useful). Here is the link: http://bit.ly/GH_support OTHER USEFUL LINKS: ———————— Channel page: http://bit.ly/GH_channel Subscribe: http://bit.ly/GH_subscribe Google+: http://bit.ly/GH_Google Twitter: @drgregmartin Facebook: http://bit.ly/GH_facebook HERE ARE SOME PLAYLISTS ——————————————- Finding work in Global Health: http://bit.ly/GH_working Epidemiology: http://bit.ly/GH_epi Global Health Ethics: http://bit.ly/GH_ethics Global Health Facts: http://bit.ly/GH_facts WANT CAREER ADVICE? ———————————— You can book time with Dr Greg Martin via Google Helpouts to get advice about finding work in the global health space. Here is the link: http://bit.ly/GH_career -~-~~-~~~-~~-~- Please watch: "Know how interpret an epidemic curve?" https://www.youtube.com/watch?v=7SM4PN7Yg1s -~-~~-~~~-~~-~-
Healthcare Data Mining with Matrix Models (Part 1)
 
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Authors: Joel Dudley, Icahn School of Medicine at Mount Sinai Ping Zhang, IBM Thomas J. Watson Research Center Fei Wang, Department of Healthcare Policy and Research, Cornell University Abstract: In the last decade, advances in high-throughput technologies, growth of clinical data warehouses, and rapid accumulation of biomedical knowledge provided unprecedented opportunities and challenges to researchers in biomedical informatics. One distinct solution, to efficiently conduct big data analytics for biomedical problems, is the application of matrix computation and factorization methods such as non-negative matrix factorization, joint matrix factorization, tensor factorization. Compared to probabilistic and information theoretic approaches, matrix-based methods are fast, easy to understand and implement. In this tutorial, we provide a review of recent advances in algorithms and methods using matrix and their potential applications in biomedical informatics. We survey various related articles from data mining venues as well as from biomedical informatics venues to share with the audience key problems and trends in matrix computation research, with different novel applications such as drug repositioning, personalized medicine, and electronic phenotyping. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 731 KDD2016 video
Tentative steps towards mining PhD theses
 
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Sara Gould, Development Manager, British Library Sara will talk about the Library’s recent participation in a national project to mine chemical compounds from the pages of PhD theses, describe some of the challenges in accessing theses for Text and Data Mining, and invite participants to ‘have a go’ at mining theses for new research purposes.
Introduction to Optimization: What Is Optimization?
 
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A basic introduction to the ideas behind optimization, and some examples of where it might be useful. TRANSCRIPT: Hello, and welcome to Introduction to Optimization. This video provides a basic answer to the question, “What is optimization?” In simplest terms, optimization is choosing inputs that will result in the best possible outputs, or making things the best that they can be. This can mean a variety of things, from deciding on the most effective allocation of available resources, to producing a design with the best characteristics, to choosing control variables that will cause a system to behave as desired. Optimization problems often involve the words maximize or minimize. Optimization is also useful when there are limits (or constraints) on the resources involved, or boundaries restricting the possible solutions. Let’s take a look at a very simple example of an optimization problem: Given a parabola, chose x to get the largest y. We can try different x values to see the resulting y value. Eventually we can find the maximum y value by choosing x here. You may also have solved this type of problem in calculus class by taking the derivative of the parabola and setting it equal to zero. Now for this simple problem it is easy to see the correct solution. For more complicated problems, it can be difficult to immediately see the correct solution, guessing and checking can take much too long, and it can be difficult to find the values where the derivative is equal to zero. To find the answers to most optimization problems we need to use a special type of program called an optimization algorithm. We’ll learn more about optimization algorithms in upcoming videos. Optimization can be applied to a huge variety of situations and problems. For example: Warehouse placement Choosing the optimal location for a warehouse to minimize shipment times to potential customers. Bridge design Designing a bridge that can carry the maximum load possible for a given cost. Build order Choosing the optimal build order for units in a strategy game to amass the strongest possible army in a given time. Artificial Pancreas Controlling the insulin output from an artificial pancreas to minimize the difference between actual and desired blood sugar levels throughout the day. Wing design Design an airplane wing to minimize weight while maintaining strength. Stock portfolio Selecting the best set of stocks to invest in to maximize returns based on predicted performance. Temperature control of a chemical reaction Controlling the temperature of a chemical reaction throughout a process to maximize the purity of a desired product. As you can see, optimization is a powerful tool in many applications. This is just a small sampling of the many fields that make use of optimization techniques to improve the quality of their solutions. If something can be modeled mathematically, it can usually be optimized. To summarize: Optimization improves results by helping to choose the inputs that produce the best outputs Most optimization problems require an optimization algorithm to solve Optimization is applicable to many disciplines
Views: 50144 AlphaOpt
Deb Grubbe on Big Data in Chemical Engineering
 
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Chemical Engineer Deb Grubbe had an opportunity to chat with CEP senior editor Michelle Bryner about how Big Data could be used to improve safety in the chemical process industry (CPI), in turn improving the health of the industry by reducing incidents and downtime, and making companies more profitable. Using Big Data tools to analyze volumes of process data, laboratory data, data related to structures, or electrical systems can provide engineers a much deeper understanding of their processes, which they can then use to prevent problems before they happen.
Views: 1461 AIChE ChEnected
Qualitative and Quantitative research in hindi  | HMI series
 
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For full course:https://goo.gl/J9Fgo7 HMI notes form : https://goo.gl/forms/W81y9DtAJGModoZF3 Topic wise: HMI(human machine interaction):https://goo.gl/bdZVyu 3 level of processing:https://goo.gl/YDyj1K Fundamental principle of interaction:https://goo.gl/xCqzoL Norman Seven stages of action : https://goo.gl/vdrVFC Human Centric Design : https://goo.gl/Pfikhf Goal directed Design : https://goo.gl/yUtifk Qualitative and Quantitative research:https://goo.gl/a3izUE Interview Techniques for Qualitative Research :https://goo.gl/AYQHhF Gestalt Principles : https://goo.gl/Jto36p GUI ( Graphical user interface ) Full concept : https://goo.gl/2oWqgN Advantages and Disadvantages of Graphical System (GUI) : https://goo.gl/HxiSjR Design an KIOSK:https://goo.gl/Z1eizX Design mobile app and portal sum:https://goo.gl/6nF3UK whatsapp: 7038604912
Views: 100639 Last moment tuitions
How to calculate Standard Deviation and Variance
 
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Tutorial on calculating the standard deviation and variance for statistics class. The tutorial provides a step by step guide. Like us on: http://www.facebook.com/PartyMoreStudyLess Related Videos: How to Calculate Mean and Standard Deviation Using Excel http://www.youtube.com/watch?v=efdRmGqCYBk Why are degrees of freedom (n-1) used in Variance and Standard Deviation http://www.youtube.com/watch?v=92s7IVS6A34 Playlist of z scores http://www.youtube.com/course?list=EC6157D8E20C151497 David Longstreet Professor of the Universe Like us on: http://www.facebook.com/PartyMoreStudyLess Professor of the Universe: David Longstreet http://www.linkedin.com/in/davidlongstreet/ MyBookSucks.Com
Views: 1802788 statisticsfun
Mean; Median; Mode; Standard Deviation
 
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This clip show the calculation of each of these values for a small data set.
Views: 535406 John Quinn
Whats Eating Scientific Data?  21st Century Approaches to
 
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Google Tech Talk June 17, 2009 ABSTRACT Whats eating Scientific Data? 21st Century Approaches to Discovering (Chemical) Data Presented by Jim Downing and Nico Adams. The web of documents and unstructured information is slowly but inexorably evolving towards a web of data. The increasing data-centricity of the web is driven by the next generation of web-applications and the future evolution of search - the searching of structured data is the value proposition behind a recent spate of start-ups in the search space. Furthermore, the internet in general and the semantic web in particular are revolutionising the way in which science communicates, manages and exchanges data, impacting all areas of scientific endeavour from scholarly communication through to laboratory management and data analysis and mining. Chemistry is the central physical science and at the heart of modern research into new drugs, new materials and new personal care products. All of these products require the confluence of structured data from a number of different domains and often advances in science can be viewed as a data integration problem and therefore the availability as well as the discoverability of high-quality scientific/chemical data on the internet is of the utmost importance. In this talk we will discuss recent developments in the semantic toolstack for chemistry, starting with markup languages for chemical data, RDF vocabularies as well as ontologies (ChemAxiom) for chemicals and materials (data). It will illustrate how ontologies can be used for indexing, faceted search and retrieval of chemical information and for the "axiomatisation" of chemical entities and materials beyond simple notions of chemical structure. We will discuss the use of linked data to generate new chemical insights and will provide a brief discussion of the use of our entity extraction and natural language processing system OSCAR for the "semantification" of chemical information. We will demonstrate the use of authoring tools (Chem4Word) for the generation of structured "datuments" (data + documents) on the web as well as the Lensfield data processing and publication system. There will also be a brief discussion on how some of the principles developed for chemistry can be applied to other domains, such as biomedical research. Finally, we will review some of the challenges that are facing both chemical data and the adoption of semantic web technologies today. Biosketch Nico Adams: Nico Adams read chemistry the University of York and subsequently worked as a research chemist at DSM Research (The Netherlands) and Cambridge Combinatorial (now Millenium Pharmaceuticals, UK), on the combinatorial synthesis and screening of early transition metal olefin polymerisation catalysts. He subsequently became a member of the group of Prof P. Mountford at the Inorganic Chemistry Laboratory, University of Oxford to read towards his doctoral degree in organometallic chemistry. In 2003 he joined the Technische Universiteit Eindhoven as a post-doctoral research associate (group of Prof U. S. Schubert) and the Dutch Polymer Institute (DPI) as a project leader in polymer informatics. In 2006 he joined the University of Cambridge as a research associate, where he manages a research group in polymer informatics. His main research interests lie in the area of combinatorial and solid phase organometallic chemistry, materials and polymer informatics, the use of polymers for biomedical applications as well as ontological engineering and the semantic web. Biosketch Jim Downing: After completing a Masters in computational fluids and mechanics, Jim spent 4 years with a small software start-up in Cambridge working on information and knowledge systems in science and engineering research, and later in public sector information. He moved to the University of Cambridge in 2004 to work on the Open Source DSpace institutional repository software. Working with early adopters of the DSpace system at Cambridge (particularly Prof. Peter Murray-Rust) led to an interest in chemical information, and to Jim joining Prof. Murray-Rust's group to develop software architectures for chemical information, including a move towards semantic web technologies and RESTful web APIs. Jim is currently interested in the application of Linked Data in chemistry and the opportunities and challenges presented by functional programming languages in cheminformatics.
Views: 4177 GoogleTechTalks
Skoltech Colloquium: Numerical Algorithms for High-Dimensional Problems, 30.01.2014
 
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January 30, 2014 Numerical Algorithms for High-Dimensional Problems Speaker: Ivan Oseledets, Associate Professor at the Skolkovo Institute of Science of Technology (Skoltech). He previously was a Senior Researcher in the Institute of Numerical Mathematics of Russian Academy of Sciences (INM RAS) in Moscow, Russia (till 2013, from 2013-part time). Ivan has received the PhD degree in Numerical Mathematics from the INM RAS in 2007 and the degree of Doctor of Sciences (second Russian degree) in 2012. Ivan was an invited Professor in the Haussdorf Institute of Mathematics in Bonn in 2011, and also has a part-time position in the Max-Planck Institute for Mathematics in the Sciences (Leipzig) from 2009. Dr. Oseledets research interests include numerical analysis, linear algebra, tensor methods and their applications in high-dimensional problems: solution of PDEs, quantum chemistry and computational material design, stochastic partial differential equations, wavelets, data mining and compression. Dr. Oseledets has received the medal of Russian academy of Sciences for the best student work in Mathematics in 2005; the medal of Russian academy of Sciences for the best work among young mathematicians in 2009. He is the winner of the Dynasty Foundation contest among young mathematicians in Russia in 2012. Abstract: Multidimensional problems are notoriously difficult due to the curse of dimensionality. However, high-dimensional problems are usually the most interesting ones and moreover, if the problem is of a considerable practical interest, often there is a method that solves it. The most vivid example is the Schrodinger equation in quantum chemistry, where efficient solution methods have been proposed. However, such methods are usually problem-specific, require a lot of efforts to implement and difficult to be applied in other areas. In the recent years, active development of mathematical foundations for the algorithms for the solution of high-dimensional problem has begun. Novel tensor formats (Hierarchical Tucker, Tensor Train) as well as surprinsing connections with other research areas (MPS, PEPS, tensor networks, graphical models) form a new research area with new fascinating theoretical and algorithmic problems and new applications in chemistry, biology and data-mining and global optimization.
Views: 964 Skoltech
Simple Explanation of Chi-Squared
 
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An explanation of how to compute the chi-squared statistic for independent measures of nominal data. For an explanation of significance testing in general, see http://evc-cit.info/psych018/hyptest/index.html There is also a chi-squared calculator at http://evc-cit.info/psych018/chisquared/index.html
Views: 1000980 J David Eisenberg
Sampling Techniques [Hindi]
 
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The main types of probability sampling methods are simple random sampling, stratified sampling, cluster sampling, multistage sampling, and systematic random sampling. The key benefit of probability sampling methods is that they guarantee that the sample chosen is representative of the population
Views: 149969 Manager Sahab
Carles Bo (ICIQ) - Taming the Big Data in Computational Chemistry (4 Feb 2015)
 
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The massive use of simulation techniques in chemical research generates huge amounts of information, which starts to become recognized as the BigData problem. The main obstacle for managing big information volumes is its storage in such a way that facilitates data mining as a strategy to optimize the processes that enable scientists to face the challenges of the new sustainable society based on the knowledge and the rational use of existent resources. The present project aims at creating a platform of services in the cloud to manage computational chemistry. As other related projects, the concepts underlying our platform rely on well defined standards and it implements treatment, hierarchical storage and data recovery tools to facilitate data mining of the Theoretical and Computational Chemistry's BigData. Its main goal is the creation of new methodological strategies that promote an optimal reuse of results and accumulated knowledge and enhances daily researchers’ productivity. This proposal automatizes relevant data extracting processes and transforms numerical data into labelled data in a database. This platform provides tools for the researcher in order to validate, enrich, publish and share information, and tools in the cloud to access and visualize data. Other tools permit creation of reaction energy profile plots by combining data of a set of molecular entities, or automatic creation of Supporting Information files, for instance. The final goal is to build a new reference tool in computational chemistry research, bibliography management and services to third parties. Potential users include computational chemistry research groups worldwide, university libraries and related services, and high performance supercomputer centers.
Views: 118 Info HPCNow!
Range, variance and standard deviation as measures of dispersion | Khan Academy
 
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Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/probability/descriptive-statistics/variance_std_deviation/e/variance?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Watch the next lesson: https://www.khanacademy.org/math/probability/descriptive-statistics/variance_std_deviation/v/variance-of-a-population?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/descriptive-statistics/box-and-whisker-plots/v/range-and-mid-range?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it! About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 1320661 Khan Academy
Finding mean, median, and mode | Descriptive statistics | Probability and Statistics | Khan Academy
 
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Here we give you a set of numbers and then ask you to find the mean, median, and mode. It's your first opportunity to practice with us! Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/probability/descriptive-statistics/central_tendency/e/mean_median_and_mode?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Watch the next lesson: https://www.khanacademy.org/math/probability/descriptive-statistics/central_tendency/v/exploring-mean-and-median-module?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/descriptive-statistics/central_tendency/v/statistics-intro-mean-median-and-mode?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it! About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 2086347 Khan Academy
Mean median mode and range ll statistics ll central tendency easy way class 9 cbse
 
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Mean median mode and range statistics Statistics - Mean, Median, Mode how to make paper bag from newspaper https://youtu.be/JoTqwqjdjPs Statistics for Ungrouped Data- How to find Mean Median Mode Finding mean, median, and mode CALCULATE MEAN MEDIAN AND MODE FOR GROUPED DATA Mean; Median; Mode; Standard Deviation Statistics intro: Mean, median, and mode | Data and statistics Central Tendency - Mean Median Mode Range Mean, Median, and Mode - CBSE NCERT Class 9, chapter 14, statistics. class 8, class 7, class 6, class 10. Mode, Mean, and Median - VERY EASY way to learn, Statistics intro: Mean, median, and mode | Data and statistics | 6th grade Introduction to descriptive statistics and central tendency. Ways to measure the average of a set: median, mean, mode. Mean, Median, Mode, and Range Made Easy! Different types of quadrilaterals and their properties class 9 cbse https://www.youtube.com/watch?v=xahcJZu1u9c If you like our videos, subscribe to our channel https://www.youtube.com/channel/UCEVG-1G2sP_CCvRUp3i_fyg Feel free to connect with us at https://www.facebook.com/galaxycoachingclasses/?ref=bookmarks or https://www.facebook.com/galaxymathstricks/ Please Like Our Facebook Page. https://www.facebook.com/galaxycoachingclasses/ Please Follow Me On Instagram https://www.instagram.com/chetanptl12/ Please Follow me on Twitter. https://twitter.com/chetan21385 Have fun, while you learn. Thanks for watching
Views: 800752 galaxy coaching classes
Statistics intro: Mean, median, and mode | Data and statistics | 6th grade | Khan Academy
 
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This is a fantastic intro to the basics of statistics. Our focus here is to help you understand the core concepts of arithmetic mean, median, and mode. Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/mean-and-median/e/calculating-the-mean?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Watch the next lesson: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/mean-and-median/v/mean-median-and-mode?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Missed the previous lesson? https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/histograms/v/interpreting-histograms?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Grade 6th on Khan Academy: By the 6th grade, you're becoming a sophisticated mathemagician. You'll be able to add, subtract, multiply, and divide any non-negative numbers (including decimals and fractions) that any grumpy ogre throws at you. Mind-blowing ideas like exponents (you saw these briefly in the 5th grade), ratios, percents, negative numbers, and variable expressions will start being in your comfort zone. Most importantly, the algebraic side of mathematics is a whole new kind of fun! And if that is not enough, we are going to continue with our understanding of ideas like the coordinate plane (from 5th grade) and area while beginning to derive meaning from data! (Content was selected for this grade level based on a typical curriculum in the United States.) About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to Khan Academy‰Ûªs 6th grade channel: https://www.youtube.com/channel/UCnif494Ay2S-PuYlDVrOwYQ?sub_confirmation=1 Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 1925707 Khan Academy
Understanding Confidence Intervals: Statistics Help
 
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This short video gives an explanation of the concept of confidence intervals, with helpful diagrams and examples. Find out more on Statistics Learning Centre: http://statslc.com or to see more of our videos: https://wp.me/p24HeL-u6
Views: 781794 Dr Nic's Maths and Stats
Fact vs. Theory vs. Hypothesis vs. Law… EXPLAINED!
 
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Viewers like you help make PBS (Thank you 😃) . Support your local PBS Member Station here: https://to.pbs.org/PBSDSDonate Think you know the difference? Don’t miss our next video! SUBSCRIBE! ►► http://bit.ly/iotbs_sub ↓ More info and sources below ↓ Some people try to attack things like evolution by natural selection and man-made climate change by saying “Oh, that’s just a THEORY!” Yes, they are both theories. Stop saying it like it’s a bad thing! It’s time we learn the difference between a fact, a theory, a hypothesis, and a scientific law. Have an idea for an episode or an amazing science question you want answered? Leave a comment or check us out at the links below! Follow on Twitter: http://twitter.com/okaytobesmart http://twitter.com/jtotheizzoe Follow on Tumblr: http://www.itsokaytobesmart.com Follow on Instagram: http://instagram.com/jtotheizzoe Follow on Snapchat: YoDrJoe ----------------- It’s Okay To Be Smart is written and hosted by Joe Hanson, Ph.D. Follow me on Twitter: @jtotheizzoe Email me: itsokaytobesmart AT gmail DOT com Facebook: http://www.facebook.com/itsokaytobesmart For more awesome science, check out: http://www.itsokaytobesmart.com Produced by PBS Digital Studios: http://www.youtube.com/user/pbsdigitalstudios Joe Hanson - Creator/Host/Writer Joe Nicolosi - Director Amanda Fox - Producer, Spotzen Inc. Kate Eads - Producer Andrew Matthews - Editing/Motion Graphics/Animation Katie Graham - Camera John Knudsen - Gaffer Theme music: “Ouroboros” by Kevin MacLeod Other music via APM Stock images from Shutterstock, stock footage from Videoblocks (unless otherwise noted)
Views: 720356 It's Okay To Be Smart
TDM Symposium - Text & Data Mining: The Changing Nature of Research with Kiera McNiece
 
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PLEASE NOTE: Due to technical difficulties, audio and visuals of the speaker for this talk are missing until 18:24. Please follow this link to view the video from this timecode: https://youtu.be/6ucNiWiyzyg?t=1104 Plenary address from Cambridge Office of Scholarly Communication's Text & Data Mining Symposium, head at the Engineering Department of Cambridge University on Wednesday 12 July 2017. You can find all speaker presentations on the Apollo repository here: https://www.repository.cam.ac.uk/handle/1810/266221
Toyota AI Battery Life Cycle Prediction
 
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Wouldn’t it be nice if battery manufacturers could tell which of their batteries will last at least two years and sell those to mobile phone makers, and which will last for ten years or more and sell those to electric vehicle manufacturers? New collaborative research published today in Nature Energy shows how they could start doing that. Scientists at the Massachusetts Institute of Technology (MIT), Stanford University and the Toyota Research Institute (TRI) discovered that combining comprehensive experimental data and artificial intelligence revealed the key for accurately predicting the useful life of lithium-ion batteries before their capacities started to wane. After the researchers trained their machine learning model with a few hundred million data points, the algorithm predicted how many more cycles each battery would last, based on voltage declines and a few other factors among the early cycles. The predictions were within 9 percent of the actual cycle life. Separately, the algorithm categorized batteries as either long or short life expectancy based on just the first five charge/discharge cycles. Here, the predictions were correct 95 percent of the time. This machine learning method could accelerate the research and development of new battery designs, and reduce the time and cost of production, among other applications. The researchers have made the data—the largest of its kind—publicly available. “The standard way to test new battery designs is to charge and discharge the cells until they die. Since batteries have a long lifetime, this process can take many months and even years,” said co-lead author Peter Attia, Stanford doctoral candidate in Materials Science and Engineering. “It’s an expensive bottleneck in battery research.” The work was carried out at the Center for Data-Driven Design of Batteries, an academic-industrial collaboration that integrates theory, experiments and data science. The Stanford researchers, led by William Chueh, assistant professor in Materials Science & Engineering, conducted the battery experiments. MIT’s team, led by Richard Braatz, professor in Chemical Engineering, performed the machine learning work. Kristen Severson is co-lead author of the research. She completed her Ph.D. in chemical engineering at MIT last spring. One of the critical tasks in data-driven, multi-institute research projects is ensuring that the large streams of data produced at experimental facilities are managed and transferred between different research groups efficiently. Study co-authors Muratahan Aykol and Patrick Herring brought TRI’s experience with big data to the project and their own expertise on battery development to enable effective management and seamless flow of battery data, which was essential for this collaboration to create accurate machine-learning models for the early-prediction of battery failure.
Views: 410 Motorward
Statistics - How to make a histogram
 
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This example shows how to make a histogram. Remember that the horizontal axis represents the values of the variables. The vertical axis gives us the frequency of those variables. For more videos visit http://www.mysecretmathtutor.com
Views: 711664 MySecretMathTutor
Normal Distribution - Explained Simply (part 1)
 
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*** IMPROVED VERSION of this video here: https://youtu.be/tDLcBrLzBos I describe the standard normal distribution and its properties with respect to the percentage of observations within each standard deviation. I also make reference to two key statistical demarcation points (i.e., 1.96 and 2.58) and their relationship to the normal distribution. Finally, I mention two tests that can be used to test normal distributions for statistical significance. normal distribution, normal probability distribution, standard normal distribution, normal distribution curve, bell shaped curve
Views: 1130424 how2stats
Fellow Short Talks: Dr Scott Hale, Oxford University
 
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Bio Dr Scott A. Hale is a Faculty Fellow with expertise in both the social sciences and computer science. His research focuses on knowledge discovery, data mining, and the visualization of human behaviour in three substantive areas: multilingualism and user experience, mobilization/collective action, and human mobility. Research New technologies generate unprecedented quantities of digital trace data about human behaviour and provide opportunities to study complex social systems in frameworks similar to those of the natural sciences. His work concentrates on empirical observation of patterns in large-scale data and experiments. These approaches form part of a new field — computational social science or social data science — and can generate theory-informed predictive models and change the way we understand and solve social problems. The vast majority of the data about human behaviour, however, is unstructured, and my research therefore seeks to develop new tools to take full advantage of these data. #TuringShortTalks
Bayes theorem trick (solve in less than 30 sec )
 
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𝗧𝗼𝗽𝗶𝗰: Bayes theorem trick 𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Engineering Mathematics .................................... 𝗧𝗼 𝗕𝗨𝗬 𝗻𝗼𝘁𝗲𝘀 𝗼𝗳 𝗦𝗵𝗿𝗲𝗻𝗶𝗸 𝗝𝗮𝗶𝗻: (Engineering Mathematics ) 1) Mail id: [email protected] 2) Whatsapp: +91 8097220743 ......................... 𝗦𝗼𝗰𝗶𝗮𝗹 𝘀𝗶𝘁𝗲𝘀 𝗼𝗳 𝗦𝗵𝗿𝗲𝗻𝗶𝗸 𝗝𝗮𝗶𝗻: 1) Facebook- https://www.facebook.com/shrenikjn10 2) Instagram- https://www.instagram.com/shrenik_jain_10/ 3) Quora- https://www.quora.com/profile/Shrenik-Jain-51
Views: 394489 Shrenik Jain
Mining Engineering Career Opportunities Field Salary Colleges by BrainChecker
 
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http://www.brainchecker.in Mining Engineering Career by BrainChecker Stay tuned for regular updates from BrainChecker Channel. We provide excellent education related tips and excellent career guidance. Contact: https://goo.gl/forms/cmB1rRC4v5qF2rf73 Fill the form above and we would get in touch with you Hey there and welcome to the Brainchecker's YouTube channel, India’s largest Career Counseling Company!! Our entire video would be divided into 5 sections: - Introduction. - Nature of work. - Eligibility and Professional Courses available. - Best Colleges - Career prospects and Salary Students are requested to perform their own due research before choosing a career. You can check the description for additional details and assistance from Brain Checker. Introduction Mining engineering is an engineering discipline that applies science and technology to the extraction of minerals from the earth. Mining engineering is associated with many other disciplines, such as geology, mineral processing and metallurgy, geotechnical engineering and surveying. A mining engineer may manage any phase of mining operations – from exploration and discovery of the mineral resource, through feasibility study, mine design, development of plans, production and operations to mine closure. Nature of Work Mining Engineers design mines and will use engineering principles, technology and scientific theory for the safe and effective extraction of natural resources from these mines. Mining Engineers plan, design and operate the mining processes, both underground and above ground. Mining Engineers will be responsible for the overseeing of both mining operations and miners, and are employed by many mining-related organizations Duties The duties of a mining engineer include: • Involved in the design of both open-pit and underground mines • Overseeing of the operations of the mine • Training and supervision of personnel • Looking into methods for transporting minerals to the various processing plants • Oversee the design and construction of mine shafts and tunnels in underground mines • To ensure that mines are operated safely and in the most environmentally effective ways possible • Oversee the production rates to assess the effectiveness of the mine operation • To be involved in finding solutions for problematic areas such as water and air pollution, land reclamation etc. • Design and develop mines and determine the best way to extract metal or minerals to get the most out of deposits, and to extract as much out of the mine whilst maintaining strict safety and environmental issues at hand, for the least amount of money. • Involved in trying to limit the amount of water used in the operation, and keep pollution to a bare minimum. • Prepare technical reports for miners, engineers, and managers Now let’s go to, Eligibility and Professional Courses • 10+2 Science with Physics, Chemistry, Mathematics is mandatory with at least 60% marks. • Entrance examinations are conducted for admission to B.E./B.Tech programs in marine engineering. Some colleges could consider the marks obtained in 10+2 qualifying examination as well, for selection into these programs. • M.E./M.Tech programs can be pursued if the individual has completed B.E./B.Tech in the same discipline. Moreover, they need to qualify for GATE as well. We at Brain Checker help students in choosing their career. To know if this career suits your talents of skillsets, you can consult a Brain Checker Career Specialist. Check link in the description for more details. Now we are going to look at few good colleges offering this qualification : 1 IIT Kharagpur, West Bengal 2 IIT - BHU, Uttar Pradesh 3 Indian School of Mines, Jharkhand 4 NIT Surathkal, Karnataka 5 Visvesvaraya National Institute of Technology Nagpur, Maharashtra Moving on to the next part of the video........ Career Prospects There are a lot of opportunities for Mining Engineering graduates in the following fields,. Arab countries like Saudi Arabia, Kuwait, Qatar and UK provides profitable career opportunities to the eligible candidates. Job Profiles include: • Mining Engineer – Granite • Mining Engineering Technicians • Research Engineers- Data Mining • Assistant Mining Engineer • Mining Engineer • Mine Planner • Technical Consultant amongst others An Mining Engineering graduate gets an average salary between Rs.40,000 and Rs 50,000 per month at the entry level. It also depends on the university you graduate from. Top universities will fetch their graduates higher salary packages. After a couple of years of experience an individual can earn up to Rs 3,00,000 per month or more depending on the skill set, experience and performance. Thank you for watching, if you loved this Brain Checker Video please like, share and subscribe to us. Bye!!
Saturday Science at Scripps Research: Markers Magnets and Microbiomes: Mining Biology with Chemistry
 
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(Visit: http://www.uctv.tv/) The Scripps Research Institute’s Dennis Wolan takes you on a fascinating exploration of the human body’s ecosystem and the myriad symbiotic relations found there that sustain and affect everything from immunity to behavior, and how his lab “mines” this microbiome for potential therapies. Series: "Saturday Science at The Scripps Research Instititute" [4/2016] [Science] [Show ID: 30497]
Webinar: Machine Learning, AI, and Data Driven Materials Development and Design (Part 1 of 3)
 
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Materials are an important contributor to technological progress, and yet the process of materials discovery and development has historically been inefficient. In general, the current innovation workflow is human-centered, where researchers design, conduct, analyze and interpret results obtained through experiments, simulations or literature review. Such results are often high-dimensional, large in number and heterogeneous in nature, which hinders a researcher’s ability to draw insight from this data manually. This webinar explores the synthesis of machine learning with materials research, highlighting a broad spectrum of topics in which machine learning, artificial intelligence, or statistics play a significant role in addressing problems in experimental and theoretical materials science. It also generated discussion on the fundamental connection between machine learning and material science, and its future application and impact. (This is part one of three. Parts two and three will be posted on October 10 and October 17, 2018.)
Gold Live! - Gold Price, Silver Price, Base Metals, Mining, Charts & Data, Market News, Crypto
 
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Download for iOS: https://kitco.com/ios Download for Android: https://kitco.com/android Kitco’s new and improved Gold Live!, gives you access to the best gold and market price quotes, charts, latest precious metals news and expert opinions in a familiar but improved and exciting user experience. All the news and information you love in a better, faster and more intuitive package of our existing app used by a million users with an average of a 4.5 star user rating. Updated and improved news, commentaries and press release sections and a new widget, breaking news and market alert features. The all-new Gold Live! Keeps its familiar feel that’s loved by millions while vastly improving and speeding up the access to precious metals quotes and charts. In addition to following markets, you can watch Kitco News video interviews on our improved mobile friendly video platform. With comprehensive coverage and full control, it brings live spot prices for gold, silver, and other metals, along with critical market information to your fingertips. Live quotes, charts, video news, commentaries and much more! Download the official Gold Live! App and get all the latest updates so you’re always on top of the latest precious metals, finance, stocks and mining news. YOUR FAVORITE PRECIOUS METALS AND WHAT MOVED THEM TOPICS AT A GLANCE - Uncover trading information and the latest news from Kitco New, Kitco Commentaries and other leading sources. BREAKING NEWS AND ALERTS. Never miss a story - Get real-time notifications sent directly to your phone for the biggest breaking business news. KEEP UP WITH THE MARKETS - Find market data on all the precious metals, base metals, mining equities, cryptocurrencies, FX and more -- always one tap away. KITCO VIDEO NEWS – Watch the best industry videos and stories from Kitco News, delivered right to your phone. And this is just the start. Our editorial and development teams are working hard to roll out new features regularly. KITCO GOLD INDEX (KGX) - Shows how US dollar fluctuations impact the value of gold. The Kitco Gold Index has one purpose that is to determine whether the value of gold is actual, a reflection of changes in the US Dollar value, or a combination of both. Key Features (Overview) - Precious and base metals quotes in multiple currencies - NEW: Mining news and stocks - Currencies exchange rates (FX) - Cryptocurrency prices and news - Kitco Gold Index (KGX) - Charts and data - Video news - Customizable market alerts - Breaking news from the finance world - Gold price - Silver price - Spot gold - Spot silver - Gold news - Silver news - Gold charts - Silver charts Live spot and historical precious metals prices and charts - Gold, silver, platinum, palladium, rhodium - Daily and historical London Fix - Charts and data - Kitco Gold Index (KGX) The latest market news at your fingertips - Commentaries - Analysis - Press releases - Stocks - FOREX - Futures - Investment trends Video news - Interviews - Exclusive reports - Event coverage Mining - Mining news - Mining stocks - Video news - Event coverage Indices - DJIA, NASDAQ, S&P 500, USD Index, NYSE, TSX, NIKKEI, XAU, HUI, JSE Gold, TSX Gold, and GFMS Currency rates (FOREX) - Canadian dollar (CAD), Euro (EUR), British pound (GBP), Australian dollar (AUD), Japanese Yen (JPY), Chinese yuan (CNY), Brazilian real (BRL), Swiss franc (CHF), Mexican peso (MXN), Russian ruble (RUB), Hong Kong dollar (HKD), South African rand (ZAR), and Indian rupee (INR) Live and historical base metals quotes - Copper, nickel, aluminum, zinc, lead, and uranium Cryptocurrencies live prices and charts - Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Monero (XMR), Ripple (XRP), Dash (DASH), Zcash (ZEC) Other features - Oil price Would you like to request a new feature? Have any questions, technical issues or feedback? Please don't hesitate to contact us at [email protected]
Views: 2181 Kitco NEWS
CAREERS IN CHEMISTRY– Degree,Healthcare Scientist,Engineering firms,Laboratory Jobs
 
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CAREERS IN CHEMISTRY.Go through the career opportunities of CHEMISTRY, Govt jobs and Employment News channel from Freshersworld.com – The No.1 job portal for freshers in India. Visit http://www.freshersworld.com?src=Youtube for detailed Career information,Job Opportunities,Education details of CHEMISTRY. Chemistry as a subject provides insight about topics of concern that deal with the composition, properties, structure and most importantly, change of matter. When you first listen to the word chemistry, all you can think of is molecules and matters and structures. Chemistry as a subject paves way for much more than any of these mentioned. There is a major assumption that 'All chemists wear white coats'. In fact, it is the other way round! Here is a list of jobs that a student can attain to after having his/ her chemistry degree in hand: • Healthcare scientist, clinical biochemistry • Forensic scientist • Pharmacologist • Analytical chemist • Research scientist (physical sciences) • Toxicologist • Chemical engineer Also, there are a few options wherein this degree could be used in a more specific way: • Science writer • Higher education lecturer • Patent attorney • Chartered certified accountant • Environmental consultant • Secondary school teacher The skills which are absolutely required for a student having a chemistry degree in hand is to whilst bagging a job in need are: • analysis and problem-solving; • research skills • technical skills • quantitative skills • organizational skills • time management and organization; • written and oral communication; • monitoring/maintaining records and data; • teamwork; • IT and technology. Here is a list to as in who all employs chemistry graduates that would rather provide an insight to the career as such: • Consulting firms • Pharmaceutical companies • Museums • Engineering firms • Industrial inspection firms • Magazines and newspapers Cosmetics and fragrance production companies • Computers and telecommunications Government agencies • Fine and heavy chemical manufacturing companies • Food and beverage production companies • Mining and metallurgy companies • Law Firms • Oil and gas companies • Plastic manufacturing companies • Universities, colleges and schools • Hospitals & other medical organizations • Pulp and paper companies • Environment and pollution control firms Here is a list of colleges mastering in chemistry: Osmania PG College, Kurnool M S R S Siddardha Degree College Gayatri College of Science & Management Government Degree College (Men) Ramakrishna Mission Vivekananda College J & J College of Science Sheth L H Science College M G Science Institute Bhavan's R A College of Science Sir C R Reddy Autonomous College NIT Silchar NIT Surathkal NIT Rourkela NIT Jalandhar NIT Durgapur NIT Agartala Meenakshi College for Women Ethiraj College for Women Sri Venkateswara College St. Stephen’s College Hans Raj College Ramjas College Miranda House College Gargi College BITS Pilani MNIT Jaipur IIT Kharagpur IIT Bombay IIT Delhi IIT Guwahati IIT Kanpur IIT Madras NIT Warangal NIT Trichy KMC Delhi Acharya Narendra Dev College ISM Dhanbad IIT Roorkee There are thousands of ways in getting into a chemistry job. All you need to do is have patience and focus on the right direction. Patience and perseverance are the two roads to success, especially in chemistry. For more jobs & career information and daily job alerts, subscribe to our channel and support us. You can also install our Mobile app for govt jobs for getting regular notifications on your mobile. Freshersworld.com is the No.1 job portal for freshers jobs in India. Check Out website for more Jobs & Careers. http://www.freshersworld.com?src=Youtube - - ***Disclaimer: This is just a career guidance video for fresher candidates. The name, logo and properties mentioned in the video are proprietary property of the respective companies. The career and job information mentioned are an indicative generalised information. In no way Freshersworld.com, indulges into direct or indirect recruitment process of the respective companies.
Statistics - How to find outliers
 
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This video covers how to find outliers in your data. Remember that an outlier is an extremely high, or extremely low value. We determine extreme by being 1.5 times the interquartile range above Q3 or below Q1. For more videos visit http://www.mysecretmathtutor.com
Views: 450500 MySecretMathTutor
Webinar: Machine Learning, AI, and Data Driven Materials Development and Design (Part 3 of 3)
 
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Materials are an important contributor to technological progress, and yet the process of materials discovery and development has historically been inefficient. In general, the current innovation workflow is human-centered, where researchers design, conduct, analyze and interpret results obtained through experiments, simulations or literature review. Such results are often high-dimensional, large in number and heterogeneous in nature, which hinders a researcher’s ability to draw insight from this data manually. This webinar explores the synthesis of machine learning with materials research, highlighting a broad spectrum of topics in which machine learning, artificial intelligence, or statistics play a significant role in addressing problems in experimental and theoretical materials science. It also generated discussion on the fundamental connection between machine learning and material science, and its future application and impact. (This is part three of three.)
George Luxbacher: Applying Mining Engineering Knowledge to an Environmental and Economic Problem
 
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bit.ly/OHC-global-mining for complete oral history transcript for “George Luxbacher: Mine Planning, Consolidation, Remediation, and Adaptation to Regulatory Change in the Coal and Chemical Industries," interviewed by Paul Burnett for the Global Mining and Materials Research Project. George Luxbacher is Principal at MELM Consulting, LLC, providing liability management services to the mining, oil & gas, chemical industries related to environmental issues and discontinued operations. After graduating from Penn State, he began his career in the early 1970s as a mining engineer for Pittsburgh Coal Company (a then Consolidation Coal Company subsidiary), leaving to return to Penn State for his MS and PhD degrees. After graduation in 1980 he joined Occidental Research Corporation, remaining employed by various Occidental Petroleum Corporation subsidiaries, including Island Creek Coal Company and Glenn Springs Holdings, Inc. (Occidental’s environmental remediation/reclamation subsidiary), until he retired in 2015 as Senior VP at GSH. After retirement he formed MELM to return to his mining roots. He served as President of the Society for Mining, Metallurgy, and Exploration (SME) in 2008 and the American Institute for Mining, Metallurgical, and Petroleum Engineers in 2012. These interviews were funded with support from the American Institute of Mining Engineers, Metallurgists, and Petroleum Engineers (AIME), the Society for Mining, Metallurgy, and Exploration (SME), the Association for Iron & Steel Technology (AIST), The Minerals, Metals, & Materials Society (TMS), and the Society of Petroleum Engineers (SPE). Copyright © 2016 The Regents of the University of California Oral History Center, The Bancroft Library, UC Berkeley
Final Year Project Tips
 
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How to select final year project? Python Tutorial for Beginners : https://goo.gl/cLKYQP Android Tutorial : https://goo.gl/c6hsnf Servlet JSP tutorial playlist : https://goo.gl/y1kvwc Hibernate Tutorial : https://goo.gl/ett8K8 Spring Boot Tutorials : https://goo.gl/7894NE Final Year MCA, BScIT, BCA Which topic to Select for Project? Which language to use for the Project? Where to Host Website? Editing Monitors : https://amzn.to/2RfKWgL https://amzn.to/2Q665JW https://amzn.to/2OUP21a. Editing Laptop : ASUS ROG Strix - (new version) https://amzn.to/2RhumwO Camera : https://amzn.to/2OR56AV lens : https://amzn.to/2JihtQo Mics https://amzn.to/2RlIe9F https://amzn.to/2yDkx5F Check out our website: http://www.telusko.com Follow Telusko on Twitter: https://twitter.com/navinreddy20 Follow on Facebook: Telusko : https://www.facebook.com/teluskolearnings Navin Reddy : https://www.facebook.com/navintelusko Follow Navin Reddy on Instagram: https://www.instagram.com/navinreddy20 Subscribe to our other channel: Navin Reddy : https://www.youtube.com/channel/UCxmkk8bMSOF-UBF43z-pdGQ?sub_confirmation=1 Telusko Hindi : https://www.youtube.com/channel/UCitzw4ROeTVGRRLnCPws-cw?sub_confirmation=1
Views: 150833 Telusko
ChemAxon - a chemical and biological software development company
 
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ChemAxon is a cheminformatics and bioinformatics company providing software solutions for life sciences and other industries relying on chemical and biological research. This short introduction emphasizes the key strengths of the company: 1. We understand scientific and IT issues 2. Our software has an outstanding knowledge in chemistry 3. Open and freely accessible documentations and sources 4. Platform independent and cloud based solutions 5. Quick and prompt support and consultancy
Views: 533 ChemAxon
Network mining and analysis for social applications (KDD 2014)
 
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Network mining and analysis for social applications KDD 2014 Feida Zhu Huan Sun Xifeng Yan The recent blossom of social network and communication services in both public and corporate settings have generated a staggering amount of network data of all kinds. Unlike the bio-networks and the chemical compound graph data often used in traditional network mining and analysis, the new network data grown out of the social applications are characterized by their rich attributes, high heterogeneity, enormous sizes and complex patterns of various semantic meanings, all of which have posed significant research challenges to the graph/network mining community. In this tutorial, we aim to examine some recent advances in network mining and analysis for social applications, covering a diverse collection of methodologies and applications from the perspectives of event, relationship, collaboration, and network pattern. We would present the problem settings, the challenges, the recent research advances and some future directions for each perspective. Topics include but are not limited to correlation mining, iceberg finding, anomaly detection, relationship discovery, information flow, task routing, and pattern mining.
What is Interpolation and Extrapolation?
 
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Learn the difference between interpolation and extrapolation in this free math video tutorial by Mario's Math Tutoring. Learn Algebra 1 lesson by lesson in my "Learn Algebra 1" Video Course for Sale here: https://mariosmathtutoring.teachable.com/p/learn-algebra-1-video-course * Organized List of My Video Lessons to Help You Raise Your Scores & Pass Your Class. Videos Arranged by Math Subject as well as by Chapter/Topic. (Bookmark the Link Below) http://www.mariosmathtutoring.com/free-math-videos.html
Views: 95004 Mario's Math Tutoring
Mod-01 Lec-02 Data Mining, Data assimilation and prediction
 
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Dynamic Data Assimilation: an introduction by Prof S. Lakshmivarahan,School of Computer Science,University of Oklahoma.For more details on NPTEL visit http://nptel.ac.in
Views: 1935 nptelhrd
Research Methodology Meaning Types Objectives [Hindi]
 
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Methodology is the systematic, theoretical analysis of the methods applied to a field of study. A research method is a systematic plan for conducting research. Sociologists draw on a variety of both qualitative and quantitative research methods, including experiments, survey research, participant observation, and secondary data.
Views: 160771 Manager Sahab
Jure Leskovec, "The Web as a Laboratory for Studying Humanity", by  Jure Leskovec 2011-10-24:
 
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Speaker: Jure Leskovec Event Details http://www.sfbayacm.org/event/dmsig-1024-jure-leskovec-web-laboratory-studying-humanity With an increasing amount of social interaction taking place in on-line settings, we are accumulating massive amounts of data about phenomena that were once essentially invisible to us: the collective behavior and social interactions of hundreds of millions of people. Analyzing this massive data computationally offers enormous potential both to address long-standing scientific questions, and also to harness and inform the design of future social computing applications: What are emerging ideas and trends? How is information being created, how it flows and mutates as it is passed from a node to node like an epidemic? How will a community or a social network evolve in the future? We discuss how computational perspective can be applied to questions involving structure of online networks and the dynamics of information flows through such networks, including analysis of massive data as well as mathematical models that seek to abstract some of the underlying phenomena. Speaker Bio Jure Leskovec (http://cs.stanford.edu/~jure) is an assistant professor of Computer Science at Stanford University where he is a member of the Info Lab and the AI Lab. His research focuses on mining and modeling large social and information networks, their evolution, and diffusion of information and influence over them. Problems he investigates are motivated by large scale data, the Web and on-line media. He received six best paper awards, a ACM KDD dissertation award, Microsoft Research Faculty Fellowship and appeared on IEEE Intelligent Systems magazine "AI's 10 to Watch". Jure also holds three patents. Before joining Stanford Jure spent a year as a postdoctoral researcher at Cornell University. He completed his Ph.D. in computer science at Carnegie Mellon University in 2008. Jure has authored the Stanford Network Analysis Platform (SNAP), a general purpose network analysis and graph mining library that easily scales to massive networks with hundreds of millions of nodes, and billions of edges.
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