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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: 495 Othman Soufan
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: 55295 AlphaOpt
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 -~-~~-~~~-~~-~-
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: 572508 Bozeman Science
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
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: 547507 John Quinn
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: 413229 Examrace
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: 7593 TEDx Talks
How to choose your research topic
 
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Choosing a research topic and writing your research proposal can be difficult when you're faced with a lot of choice. Current Griffith PhD candidates and supervisors give some advice to help you create a winning research proposal. Find out more at https://www.griffith.edu.au/research-study
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]
Data Mining - Three Big Problems in Fundraising | Lectures On-Demand
 
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Ashutosh R. Nandeshwar, Associate Director of Analytics, University of Michigan The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
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: 1354166 Khan Academy
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: 117519 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: 1898751 statisticsfun
An Introduction to Linear Regression Analysis
 
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Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 831222 statisticsfun
RESEARCH PAPER FORMAT (in Hindi)
 
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find relevant notes-https://viden.io/search/knowledge?query=computer+science also search PDFs notes-https://viden.io More videos like this: https://www.youtube.com/playlist?list=PL9P1J9q3_9fNmTX2ZkUnboMBp8yU_GHYj
Views: 107450 LearnEveryone
Henrietta Lacks, the Tuskegee Experiment, & Ethical Data Collection: Crash Course Statistics #12
 
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Today we’re going to talk about ethical data collection. From the Tuskegee syphilis experiments and Henrietta Lacks’ HeLa cells to the horrifying experiments performed at Nazi concentration camps, many strides have been made from Institutional Review Boards (or IRBs) to the Nuremberg Code to guarantee voluntariness, informed consent, and beneficence in modern statistical gathering. But as we’ll discuss, with the complexities of research in the digital age many new ethical questions arise. Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Mark Brouwer, Glenn Elliott, Justin Zingsheim, Jessica Wode, Eric Prestemon, Kathrin Benoit, Tom Trval, Jason Saslow, Nathan Taylor, Divonne Holmes à Court, Brian Thomas Gossett, Khaled El Shalakany, Indika Siriwardena, Robert Kunz, SR Foxley, Sam Ferguson, Yasenia Cruz, Eric Koslow, Caleb Weeks, Tim Curwick, Evren Türkmenoğlu, Alexander Tamas, D.A. Noe, Shawn Arnold, mark austin, Ruth Perez, Malcolm Callis, Ken Penttinen, Advait Shinde, Cody Carpenter, Annamaria Herrera, William McGraw, Bader AlGhamdi, Vaso, Melissa Briski, Joey Quek, Andrei Krishkevich, Rachel Bright, Alex S, Mayumi Maeda, Kathy & Tim Philip, Montather, Jirat, Eric Kitchen, Moritz Schmidt, Ian Dundore, Chris Peters, Sandra Aft, Steve Marshall -- Want to find Crash Course elsewhere on the internet? Facebook - http://www.facebook.com/YouTubeCrashCourse Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 72057 CrashCourse
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: 4179 GoogleTechTalks
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.
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: 869123 galaxy coaching classes
Some statistics tests, t-test, z-test, f-test and chi square test- A theoritical aspect
 
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( Correction - Pen was assumed name instead of auther) T-test, Z-test, F-yest, Chi square test. For different competitive exams Keep watching chanakya group of economics.
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: 119 Info HPCNow!
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: 158278 Manager Sahab
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: 1523 AIChE ChEnected
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: 463236 MySecretMathTutor
Chi-squared Test
 
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Paul Andersen shows you how to calculate the ch-squared value to test your null hypothesis. He explains the importance of the critical value and defines the degrees of freedom. He also leaves you with a problem related to the animal behavior lab. This analysis is required in the AP Biology classroom. Intro Music Atribution Title: I4dsong_loop_main.wav Artist: CosmicD Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/ Creative Commons Atribution License
Views: 1487957 Bozeman Science
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.
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
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: 229567 Tyler DeWitt
Verification Vs Validation (Hindi) .
 
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Learn the difference between Verification and Validation , explained in Hindi with example. Understand the definition given by ISO 9000. Watch other videos from ‘Quality HUB India’- https://www.youtube.com/channel/UCdDEcmELwWVr_77GpqldKmg/videos • Subscribe to my channel ‘Quality HUB India’ for getting notification. • Like, comment & Share the video with your colleague and friends Link to buy My books 1. Mistake-Proofing Simplified: An Indian Perspective: https://www.amazon.in/gp/product/8174890165/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=8174890165&linkCode=as2&tag=qhi-21 2. Management Thoughts on Quality for Every Manager: https://www.amazon.in/gp/product/B0075MCLTO/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B0075MCLTO&linkCode=as2&tag=qhi-21 Gadgets I use and Link to buy 1. OnePlus 5 - Mobile https://www.amazon.in/gp/product/B01MXZW51M/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B01MXZW51M&linkCode=as2&tag=qhi-21 2. HP 14-AM122TU 14-inch Laptop https://www.amazon.in/gp/product/B06ZYLLT8G/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B06ZYLLT8G&linkCode=as2&tag=qhi-21 3. Canon EOS 700D 18MP Digital SLR Camera https://www.amazon.in/gp/product/B00VT61IKA/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B00VT61IKA&linkCode=as2&tag=qhi-21 4. Sonia 9 Feet Light Stand LS-250 https://www.amazon.in/gp/product/B01K7SW2OQ/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B01K7SW2OQ&linkCode=as2&tag=qhi-21 5. Sony MDR-XB450 On-Ear EXTRA BASS Headphones https://www.amazon.in/gp/product/B00NFJGUPW/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B00NFJGUPW&linkCode=as2&tag=qhi-21 6. QHM 602 USB MINI SPEAKER https://www.amazon.in/gp/product/B00L393EXC/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B00L393EXC&linkCode=as2&tag=qhi-21 7. Photron Tripod Stedy 400 with 4.5 Feet Pan Head https://www.amazon.in/gp/product/B00UBUMCNW/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B00UBUMCNW&linkCode=as2&tag=qhi-21 8. Tie Clip Collar mic Lapel https://www.amazon.in/gp/product/B00ITOD6NM/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B00ITOD6NM&linkCode=as2&tag=qhi-21 9. Hanumex Generic Green BackDrop Background 8x12 Ft for Studio Backdrop https://www.amazon.in/gp/product/B06W53TMDR/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B06W53TMDR&linkCode=as2&tag=qhi-21 10. J 228 Mini Tripod Mount + Action Camera Holder Clip Desktop Self-Tripod For Camera https://www.amazon.in/gp/product/B072JXX9DB/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B072JXX9DB&linkCode=as2&tag=qhi-21 11. Seagate Backup Plus Slim 1TB Portable External Hard Drive https://www.amazon.in/gp/product/B00GASLJK6/ref=as_li_tl?ie=UTF8&camp=3638&creative=24630&creativeASIN=B00GASLJK6&linkCode=as2&tag=qhi-21 Watch other Videos from ‘Quality HUB India’ 1. Process Capability Study (Cp,Cpk, Pp & Ppk) - https://www.youtube.com/watch?v=5hBRE0uji5w 2. What is Six Sigma ?Learn Six Sigma in 30 minutes- https://www.youtube.com/watch?v=1oiKYydbrSw 3. Failure Mode and Effects Analysis (FMEA) - https://www.youtube.com/watch?v=UxSBUHgb1V0&t=25s 4. Statistical Process Control (SPC) in Hindi – https://www.youtube.com/watch?v=WiVjjoeIrmc&t=115s 5. Measurement System Analysis (MSA) (Part 1) - https://www.youtube.com/watch?v=GGwaZeMmZS8&t=25s 6. Advanced Product Quality Planning(APQP) - https://www.youtube.com/watch?v=FaawYoPsUYE&t=35s 7. ‘Quality Circles' - https://www.youtube.com/watch?v=kRp9OIANgG8&t=25s 8. What is 'Cost of Quality' and 'Cost of Poor Quality' - https://www.youtube.com/watch?v=IsCRylbHni0&t=25s 9. How to perfectly define a problem ? 5W and 1H approach - https://www.youtube.com/watch?v=JXecodDxBfs&t=55s 10. What is 'Lean Six Sigma' ? Learn the methodology with benefits. - https://www.youtube.com/watch?v=86XJqf1IhQM&t=41s 11. What is KAIZEN ? 7 deadly Waste (MUDA) and benefit of KAIZEN - https://www.youtube.com/watch?v=TEcE-cKk1qI&t=115s 12. What is '5S' Methodology? (Hindi)- https://www.youtube.com/watch?v=dW8faNOX91M&t=25s 13. 7 Quality Control Tools - (Part 1) Hindi - https://www.youtube.com/watch?v=bQ9t3zoM0NQ&t=88s 14. "KAIZEN" in HINDI- https://www.youtube.com/watch?v=xJpbHTc3wmo&t=25s 15. 'PDCA' or 'Deming Cycle'. Plan-DO-Check-Act cycle - https://www.youtube.com/watch?v=Kf-ax6qIPVc 16. Overall Equipment Effectiveness (OEE) - https://www.youtube.com/watch?v=5OM5-3WVtd0&feature=youtu.be 17. Why-Why Analysis? - Root Cause Analysis Tool - https://www.youtube.com/watch?v=Uxn6N6OJvwA
Views: 81239 Quality HUB India
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.)
What the Heck Does “Data Science” Really Mean? The Dr. Data Show
 
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In this episode of The Dr. Data Show, Eric Siegel answers the question, "What the heck do 'data science' and 'big data' really mean?" Sign up for future episodes and more info: http://www.TheDoctorDataShow.com Attend Predictive Analytics World: http://www.pawcon.com Read Dr. Data's book: http://www.thepredictionbook.com Welcome to "The Dr. Data Show"! I'm Eric Siegel. “Data science.” “Big data.” What the hell do these buzzwords really, specifically mean? Are they just cockamamie -- intentionally vague jargon that overhypes and overpromises? Or are these terms actually helpful -- do they somehow designate, like, the most profound impact of the Information Age? Well, I’ll start with the vague and overhyping side and then circle back to why these buzzwords may matter after all. It’s time for the Dr. Data buzzword smackdown. There are a lotta problems with these words. First, "data scientist" is redundant. It's like calling a librarian a "book librarian." If you're doing science, it involves data. Duh! Furthermore, don't tell anyone I said this, but real sciences like physics and chemistry don't have "science" in their name. Your science is trying too hard if it has to call itself a science: Social science, political science, data science, and I gotta say -- even though I have three degrees in it and was a professor of it -- computer science is an arbitrarily defined field. It's just the amalgam of everything to do with computers -- as a concept and as an appliance -- from the engineering of how to build them and the deep mathematics about their theoretical limitations to how to make them more user friendly, and even business strategies for managing a team of programmers... Universities might as well also have a "toaster science" department, which covers the engineering of better toasters as well as the culinary arts on how to best cook with them. But I digress. Ok, next buzzword: “Big data.” First of all, it's just grammatically incorrect. It’s like looking at the Pacific Ocean and saying “big water.” It should be “a lotta data” or “plenty of data.” But the real problem with "big data" is that it emphasizes the size. 'Cause what’s exciting about data isn't how much of it there is per se -- it's about how quickly it's growing -- which is amazing by the way. There’s always so much more data today than there was yesterday. So we're gonna run out of adjectives really quickly: “big data,” “bigger data,” “even bigger data,” “the biggest data.” Actually, there’s been a long-running conference called the International Conference on Very Large Databases since 1975. I’m not joking. That's before the first Star Wars movie came out! Now, in some cases, people use the terms data science and big data just to refer to machine learning, i.e., when computers learn from the experience encoded in data. That's the topic of most episodes of this program, The Dr. Data Show. It’s a show about machine learning -- which is a well-defined field and by the way is also often called predictive analytics, especially when you're talking about its deployment in the private or public sector. I would urge folks to use the well-defined terms machine learning or predictive analytics if in fact that's what you’re specifically talking about. But as for data science and big data, in their general usage they suffer from a terrible case of vagueness. The have a wide range of subjective definitions, which compete and conflict. Basically, they're often used to mean nothing more specific than "some clever use of data." The terms don't necessarily refer to any particular technology, method, or value proposition. They're just plain subjective -- you can use them to mean whichever technology you'd like: machine learning, data visualization, or even just basic reporting. But much worse than that, this vagueness often serves to mislead and misrepresent by alluding to capabilities that don't exist. For example, the popular press -- as well certain analytics vendors -- sometimes use "data science" to denote some whole collection of methods that includes machine learning as well as some other advanced methods. The problem is, those other advanced methods are implied but often actually just don't really exist. They're vaporware. This confusion is sometimes inadvertent -- such as when journalists aren’t fully knowledgeable of the topic yet want it to sound as powerful as possible -- but, either way, the end result is souped-up hype that overpromises and circulates misinformation. All these issues, by the way, also apply to the older-school term "data mining," also totally subjective. Besides, calling it "data mining" is like instead of "gold mining," saying “dirt mining.” Malfunction, failed analogy... 'Cause we aren't searching for data, we're searching within data... For the complete transcript and more: http://www.TheDoctorDataShow.com
Views: 959 Eric Siegel
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: 1949893 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: 2111538 Khan Academy
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: 735087 It's Okay To Be Smart
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!!
Graph Sparsification I: Sparsification via Effective Resistances
 
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Nikhil Srivastava, Microsoft Research India Algorithmic Spectral Graph Theory Boot Camp http://simons.berkeley.edu/talks/nikhil-srivastava-2014-08-26a
Views: 3472 Simons Institute
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: 172293 Manager Sahab
Mining Tellurium (Te) and selenium (Se) for solar panels - University of Leicester
 
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www.le.ac.uk https://www2.le.ac.uk/departments/geology/research/vtmrg/tease A shift from fossil fuels to low-CO2 technologies will lead to greater consumption of certain essential raw materials. Tellurium (Te) and selenium (Se) are 'E-tech' elements essential in photovoltaic (PV) solar panels. They are rare and mined only in small quantities; their location within the Earth is poorly known; recovering them is technically and economically challenging; and their recovery and recycling has significant environmental impacts. Yet demand is expected to surge and PV film production will consume most Se mined and outstrip Te supply by 2020. Presently, these elements are available only as by-products of Cu and Ni refining and their recovery from these ores is decreasing, leading to a supply risk that could hamper the roll-out of PV. Meeting future demand requires new approaches, including a change from by-production to targeted processing of Se and Te-rich ores. Our research aims to tackle the security of supply by understanding the processes that govern how and where these elements are concentrated in the Earth's crust; and by enabling their recovery with minimal environmental and economic cost. This will involve 20 industrial partners from explorers, producers, processors, end-users and academia, contributing over £0.5M. Focussed objectives across 6 environments will target key knowledge gaps: The magmatic environment: Develop methods for accurately measuring Se and Te in minerals and rocks - they typically occur in very low concentrations and research is hampered by the lack of reliable data. Experimentally determine how Te and Se distribute between sulfide liquids and magmas - needed to predict where they occur - and ground-truth these data using well-understood magmatic systems. Assess the recognised, but poorly understood, role of "alkaline" magmas in hydrothermal Te mineralisation. The hydrothermal environment: Measure preferences of Te and Se for different minerals to predict mineral hosts and design ore process strategies. Model water-rock reaction in "alkaline" magma-related hydrothermal systems to test whether the known association is controlled by water chemistry. The critical zone environment: Determine the chemical forms and distributions of Te and Se in the weathering environment to understand solubility, mobility and bioavailability. This in turn controls the geochemical halo for exploration and provides a natural analogue for microbiological extraction. The sedimentary environment: Identify the geological and microbiological controls on the occurrence, mobility and concentration of Se and Te in coal - a possible major repository of Se. Identify the geological and microbiological mechanisms of Se and Te concentration in oxidised and reduced sediments - and evaluate these mechanisms as potential industrial separation processes. Microbiological processing: Identify efficient Se- and Te-precipitating micro-organisms and optimise conditions for recovery from solution. Assess the potential to bio-recover Se and Te from ores and leachates and design a bioreactor. Ionic liquid processing: Assess the ability of ionic solvents to dissolve Se and Te ore minerals as a recovery method. Optimise ionic liquid processing and give a pilot-plant demonstration. This is the first holistic study of the Te and Se cycle through the Earth's crust, integrated with groundbreaking oreprocessing research. Our results will be used by industry to: efficiently explore for new Te and Se deposits; adapt processing techniques to recover Te and Se from existing deposits; use new low-energy, low-environmental impact recovery technologies. Our results will be used by national agencies to improve estimates of future Te and Se supplies to end-users, who will benefit from increased confidence in security of supply, and to international government for planning future energy strategies. The public will benefit through unhindered development of sustainable environmental technologies to support a low-CO2 society. This film was produced by External Relations, University of Leicester in 2017. Filmed & Edited by Hayley Evans Produced by Ellen Rudge and Dan Smith
Sašo Džeroski - Mining Big and Complex Data
 
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Full title Sašo Džeroski - Mining Big and Complex Data Abstract Increasingly often, data mining has to learn predictive models from big data, which may have many examples or many input/output dimensions and may be streaming at very high rates. Contemporary predictive modeling problems may also be complex in a number of other ways: they may involve (a) structured data, both as input and output of the prediction process, (b) incompletely/partially labelled data, and (c) data placed in a spatio-temporal or network context. Data mining methods that can handle such problems are being developed within the EU funded MAESTRA project (http://maestra-project.eu/). The talk will first give an introduction to the different tasks encountered when learning from big and complex data. It will then present some methods for solving such tasks. It will focus on structured-output prediction, semi-supervised learning (from incompletely annotated data), and learning from data streams. Some illustrative applications of these methods will also be described. About the lecturer Sašo Džeroski is a scientific councillor at the Jozef Stefan Institute and the Centre of Excellence for Integrated Approaches in Chemistry and Biology of Proteins, both in Ljubljana, Slovenia. He is also a full professor at the Jozef Stefan International Postgraduate School and the Faculty of Computer and Information Science, University of Ljubljana. His research group investigates machine learning and data mining (including structured output prediction and automated modeling of dynamic systems) and their applications (in environmental sciences, incl. ecology, and life sciences, incl. systems biology). He has co-authored/co-edited more than ten books, including “Inductive Logic Programming”, “Relational Data Mining”, “Learning Language in Logic”, “Computational Discovery of Scientific Knowledge” and “Inductive Databases and Constraint-Based Data Mining”. He has participated in many international research projects and coordinated two of them in the past. He currently leads the FET XTrack project MAESTRA (Learning from Massive, Incompletely annotated, and Structured Data) and is one of the principal investigators in the FET Flagship Human Brain Project. Saso Džeroski received his Ph.D. degree in computer science from the University of Ljubljana in 1995. He was awarded a Jožef Stefan Golden Emblem Prize for his outstanding doctoral dissertation. Immediately thereafter, he received a fellowship from ERCIM, The European Research Consortium for Informatics and Mathematics, awarded to 5% of applicants. In 2008, he was awarded the title ECCAI fellow by the European Association for Artificial Intelligence (at that time called European Coordinating Committee on Artificial Intelligence) for “Pioneering Work in the field of AI and Outstanding Service for the European AI community”. In 2015, he became a foreign member of the Macedonian Academy of Sciences and Arts. Homepage http://www-ai.ijs.si/SasoDzeroski/
Techniques for random sampling and avoiding bias | Study design | AP Statistics | Khan Academy
 
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Techniques for random sampling and avoiding bias. View more lessons or practice this subject at http://www.khanacademy.org/math/ap-statistics/gathering-data-ap/sampling-methods/v/techniques-for-random-sampling-and-avoiding-bias?utm_source=youtube&utm_medium=desc&utm_campaign=apstatistics AP Statistics on Khan Academy: Meet one of our writers for AP¨_ Statistics, Jeff. A former high school teacher for 10 years in Kalamazoo, Michigan, Jeff taught Algebra 1, Geometry, Algebra 2, Introductory Statistics, and AP¨_ Statistics. Today he's hard at work creating new exercises and articles for AP¨_ Statistics. Khan Academy is a nonprofit organization with the mission of providing a free, world-class education for anyone, anywhere. We offer quizzes, questions, instructional videos, and articles on a range of academic subjects, including math, biology, chemistry, physics, history, economics, finance, grammar, preschool learning, and more. We provide teachers with tools and data so they can help their students develop the skills, habits, and mindsets for success in school and beyond. Khan Academy has been translated into dozens of languages, and 15 million people around the globe learn on Khan Academy every month. As a 501(c)(3) nonprofit organization, we would love your help! Donate or volunteer today! Donate here: https://www.khanacademy.org/donate?utm_source=youtube&utm_medium=desc Volunteer here: https://www.khanacademy.org/contribute?utm_source=youtube&utm_medium=desc
Views: 108580 Khan Academy
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: 559 ChemAxon
Lecture 91 — Hubs and Authorities (Advanced) | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Rescuing rare earths from coal mine waste — Speaking of Chemistry
 
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Acidic mine water is contaminating many streams in West Virginia’s coal country. Researchers are trying to extract valuable rare-earth elements from that waste to help recover some of the costs of treating it. https://cen.acs.org/materials/inorganic-chemistry/coal-new-source-rare-earths/96/i28?utm_source=YouTube&utm_medium=Social&utm_campaign=CEN ↓↓More info and references below↓↓ This video was corrected on July 12, 2018. An earlier version of the video displayed the incorrect formula for manganese hydroxide, showing Mg2(OH)3 instead of Mn(OH)2. We regret the error. Read more: A whole new world for rare earths | C&EN https://cen.acs.org/articles/95/i34/whole-new-world-rare-earths.html Managing a dearth of rare earths | C&EN https://cen.acs.org/articles/90/i14/Managing-Dearth-Rare-Earths.html Securing the supply of rare earths | C&EN https://cen.acs.org/articles/88/i35/Securing-Supply-Rare-Earths.html Speaking of Chemistry is a production of Chemical & Engineering News (C&EN), the weekly news magazine of the American Chemical Society. Contact us at [email protected]!
Views: 1203 CEN Online
Being a Data Scientist at Lubrizol
 
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The Data Scientist role at Lubrizol is extremely varied. Our team works in all areas of the business on many topics including problem solving, statistics, analysis, and programming. Their work is global, and their results are essential to the business of Lubrizol. © 2019 The Lubrizol Corporation. All rights reserved. Reproduction in whole or in part, in any form or medium without express written permission is prohibitedhttp://www.lubrizol.com
Yisong Yue - CS+Data - Alumni College 2016
 
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"Automatically Improving Automation Using Big Data" Yisong Yue, Assistant Professor of Computing and Mathematical Sciences, is director of Decision, Optimization, and Learning at the California Institute of Technology (DOLCIT), which brings together experts in machine learning, optimization, applied math, statistics, control, robotics, and human-computer interaction. Its goal is to work on creating a world where intelligent systems seamlessly integrate learning and planning, as well as automatically balance computational and statistical tradeoffs in the underlying optimization problems. Yue was previously a research scientist at Disney Research and before that, he was a postdoctoral researcher in the Machine Learning Department and the iLab at Carnegie Mellon University. The Caltech Alumni Association held a day-long event to explore the ways in which computational thinking at Caltech is disrupting science and engineering, creating entirely new disciplines with "CS+X". From developing new paradigms for computation—quantum computing and DNA computing—to pushing the boundaries of machine learning and statistics in ways that transform fields like astronomy, chemistry, neuroscience, and biology, Caltech faculty are pioneering new disciplines at the interface of computer science, and science and engineering. Learn more about the event - http://alumni.caltech.edu/alumni-college Produced in association with Caltech Academic Media Technologies. ©2016 California Institute of Technology
Views: 3274 caltech
Perry Samson - Mining My Students Notes to Create Study Guides | Lectures On-Demand
 
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Professor Perry Samson, Atm, Oceanic & Space Sci. - CoE, University of Michigan The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
Gated Graph Sequence Neural Networks
 
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Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases. In this work, we study feature learning techniques for graph-structured inputs. Our starting point is previous work on Graph Neural Networks (Scarselli et al., 2009), which we modify to use gated recurrent units and modern optimization techniques and then extend to output sequences. The result is a flexible and broadly useful class of neural network models that has favorable inductive biases relative to purely sequence-based models (e.g., LSTMs) when the problem is graph-structured. We demonstrate the capabilities on some simple AI (bAbI) and graph algorithm learning tasks. We then show it achieves state-of-the-art performance on a problem from program verification, in which subgraphs need to be matched to abstract data structures. Paper: http://arxiv.org/abs/1511.05493
Views: 5294 Microsoft Research
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.)
How to Start an AI Startup
 
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How are you supposed to get in on the AI hype? Deep learning has enabled a whole new breed of applications, and there are still so many different opportunities to apply it in fields that are completely untapped. I'll go through the steps you need to take to start your own AI startup using a combination of my own experiences and best practices from the industry as a guide. From data collection to model training to picking a problem, we'll try to understand this challenging task. Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Sources: https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A/playlists https://www.deeplearning.ai/ http://www.fast.ai/ http://www.deeplearningbook.org/ https://www.kaggle.com/datasets https://github.com/awesomedata/awesome-public-datasets https://archive.ics.uci.edu/ml/datasets.html More learning resources: https://www.youtube.com/watch?v=CBYhVcO4WgI https://www.youtube.com/watch?v=bNpx7gpSqbY https://www.youtube.com/watch?v=JqxzLUE6pP8 https://www.youtube.com/watch?v=ii1jcLg-eIQ https://www.youtube.com/watch?v=ia8arCDoxZ8 https://www.youtube.com/watch?v=677ZtSMr4-4 Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 277394 Siraj Raval