Computer Applications: An International Journal (CAIJ) ISSN :2393 - 8455 http://airccse.com/caij/index.html ********************************************* Computer Applications: An International Journal (CAIJ), Vol.4, No.1/2/3/4, November 2017 DOI:10.5121/caij.2017.4401 THE EFFECTIVENESS OF DATA MINING TECHNIQUES IN BANKING Yuvika Priyadarshini Researcher, Jharkhand Rai University, Ranchi. ABSTRACT The aim of this study is to identify the extent of Data mining activities that are practiced by banks, Data mining is the ability to link structured and unstructured information with the changing rules by which people apply it. It is not a technology, but a solution that applies information technologies. Currently several industries including like banking, finance, retail, insurance, publicity, database marketing, sales predict, etc are Data Mining tools for Customer . Leading banks are using Data Mining tools for customer segmentation and benefit, credit scoring and approval, predicting payment lapse, marketing, detecting illegal transactions, etc. The Banking is realizing that it is possible to gain competitive advantage deploy data mining. This article provides the effectiveness of Data mining technique in organized Banking. It also discusses standard tasks involved in data mining; evaluate various data mining applications in different sectors KEYWORDS Definition of Data Mining and its task, Effectiveness of Data Mining Technique, Application of Data Mining in Banking, Global Banking Industry Trends, Effective Data Mining Component and Capabilities, Data Mining Strategy, Benefit of Data Mining Program in Banking
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What is AFFINITY ANALYSIS? What does AFFINITY ANALYSIS mean? AFFINITY ANALYSIS meaning - AFFINITY ANALYSIS definition - AFFINITY ANALYSIS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Affinity analysis is a data analysis and data mining technique that discovers co-occurrence relationships among activities performed by (or recorded about) specific individuals or groups. In general, this can be applied to any process where agents can be uniquely identified and information about their activities can be recorded. In retail, affinity analysis is used to perform market basket analysis, in which retailers seek to understand the purchase behavior of customers. This information can then be used for purposes of cross-selling and up-selling, in addition to influencing sales promotions, loyalty programs, store design, and discount plans. Market basket analysis might tell a retailer that customers often purchase shampoo and conditioner together, so putting both items on promotion at the same time would not create a significant increase in revenue, while a promotion involving just one of the items would likely drive sales of the other. Market basket analysis may provide the retailer with information to understand the purchase behavior of a buyer. This information will enable the retailer to understand the buyer's needs and rewrite the store's layout accordingly, develop cross-promotional programs, or even capture new buyers (much like the cross-selling concept). An apocryphal early illustrative example for this was when one super market chain discovered in its analysis that male customers that bought diapers often bought beer as well, have put the diapers close to beer coolers, and their sales increased dramatically. Although this urban legend is only an example that professors use to illustrate the concept to students, the explanation of this imaginary phenomenon might be that fathers that are sent out to buy diapers often buy a beer as well, as a reward. This kind of analysis is supposedly an example of the use of data mining. A widely used example of cross selling on the web with market basket analysis is Amazon.com's use of "customers who bought book A also bought book B", e.g. "People who read History of Portugal were also interested in Naval History". Market basket analysis can be used to divide customers into groups. A company could look at what other items people purchase along with eggs, and classify them as baking a cake (if they are buying eggs along with flour and sugar) or making omelets (if they are buying eggs along with bacon and cheese). This identification could then be used to drive other programs. Similarly, it can be used to divide products into natural groups. A company could look at what products are most frequently sold together and align their category management around these cliques Business use of market basket analysis has significantly increased since the introduction of electronic point of sale. Amazon uses affinity analysis for cross-selling when it recommends products to people based on their purchase history and the purchase history of other people who bought the same item. Family Dollar plans to use market basket analysis to help maintain sales growth while moving towards stocking more low-margin consumable goods.
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In our proposed system is identifying reliable information in the medical domain stand as building blocks for a healthcare system that is up-todate with the latest discoveries. By using the tools such as NLP, ML techniques. In this research, focus on diseases and treatment information, and the relation that exists between these two entities. The main goal of this research is to identify the disease name with the symptoms specified and extract the sentence from the article and get the Relation that exists between Disease- Treatment and classify the information into cure, prevent, side effect to the user.This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.
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PyData SF 2016 NLP and Machine Learning without training data. A major part of Big Data collected in most industries is in the form of unstructured text. Some examples are log files in IT sector, analysts reports in the finance sector, patents, laboratory notes and papers, etc. Some of the challenges of gaining insights from unstructred text is converting it into structured information and generating training sets for machine learning. Typically training sets for supervised learning are generated through the process of human annotation. In case of text this involves reading several thousands to million lines of texts by subject matter experts. This is very expensive and may not always be available, hence it is important to solve the problem of generating training sets before attempting to build machine learning models. Our approach is to combine rule based techniques with small amounts of SME time to by pass time consuming manual creation of training data. Once we have a good set of rules mimicking the training data we will use them to create knowledgebases out of the structured data. This knowledgebase can be further queried to gain insight on the domain. I have applied this technique to several domains, such as data from drug labels and medical journals, log data generated through customer interaction, generation of market research reports, etc. I will talk about the results in some of these domains and the advantage of using this approach.
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What is APPLICANT TRACKING SYSTEM? What does APPLICANT TRACKING SYSTEM mean? APPLICANT TRACKING SYSTEM meaning - APPLICANT TRACKING SYSTEM definition - APPLICANT TRACKING SYSTEM explanation. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. An applicant tracking system (ATS) is a software application that enables the electronic handling of recruitment needs. An ATS can be implemented or accessed online on an enterprise or small business level, depending on the needs of the company and there is also free and open source ATS software available. An ATS is very similar to customer relationship management (CRM) systems, but are designed for recruitment tracking purposes. In many cases they filter applications automatically based on given criteria such as keywords, skills, former employers, years of experience and schools attended. This has caused many to adapt resume optimization techniques similar to those used in search engine optimization when creating and formatting their résumé. A dedicated ATS is not uncommon for recruitment specific needs. On the enterprise level it may be offered as a module or functional addition to a human resources suite or Human Resource Information System (HRIS). The ATS is expanding into small and medium enterprises through open source or software as a service offerings (SaaS). The principal function of an ATS is to provide a central location and database for a company's recruitment efforts. ATSs are built to better assist management of resumes and applicant information. Data is either collected from internal applications via the ATS front-end, located on the company website or is extracted from applicants on job boards. The majority of job and resume boards (LinkedIn.com, Monster.com, Hotjobs, CareerBuilder, Indeed.com) have partnerships with ATS software providers to provide parsing support and ease of data migration from one system to another. Newer applicant tracking systems (often referred to as next generation) are platforms as a service whereby the main piece of software has integration points that allow providers of other recruiting technology to plug in seamlessly. The ability of these next generation ATS solutions allows jobs to be posted where the candidate is and not just on job boards. This ability is being referred to as Omnichannel Talent Acquisition. Recent enhancements include use of artificial intelligence (AI) tools and natural language processing (NLP) to facilitate intelligent guided semantic search capabilities offered through cloud based platforms that allow companies to score and sort resumes with better alignment to the job requirements and descriptions. With the advent of ATS, resume optimization techniques and online tools are now used by applicants to increase their chances of landing an interview call. Functionality of an ATS is not limited to data mining and collection; ATS applications in the recruitment industry include the ability to automate the recruitment process via a defined workflow. Another benefit of an applicant tracking system is analyzing and coordinating recruitment efforts - managing the conceptual structure known as human capital. A corporate career site or company specific job board module may be offered, allowing companies to provide opportunities to internal candidates prior to external recruitment efforts. Candidates may be identified via pre-existing data or through information garnered through other means. This data is typically stored for search and retrieval processes. Some systems have expanded offerings that include off-site encrypted resume and data storage, which are often legally required by equal opportunity employment laws. ....
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What is ENTERPRISE SOCIAL GRAPH? What does ENTERPRISE SOCIAL GRAPH mean? ENTERPRISE SOCIAL GRAPH meaning - ENTERPRISE SOCIAL GRAPH definition - ENTERPRISE SOCIAL GRAPH explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ An enterprise social graph is a representation of the extended social network of a business, encompassing relationships among its employees, vendors, partners, customers, and the public. With the advent of Web 2.0 and Enterprise 2.0 technologies a company can monitor and act on these relationships in real-time. Given the number of relationships and the volume of associated data, algorithmic approaches are used to focus attention on changes that are deemed relevant. The term was first popularized in a 2010 Forbes article, to describe the multi-relational nature of enterprise-centric networks that are now at least partially observable at scale. The enterprise social graph integrates representations of the various social networks in which the enterprise is embedded into a unified graph representation. Given the online context of many of the relationships, social interactions often comprise direct communication along with interactions around digital artifacts. Therefore, the enterprise social graph codifies not only relationships among individuals but also individual-object interaction patterns. This definition follows Facebook's and Google's concept of a social graph that explicitly includes the objects with which individuals interact in a network. Examples of these relationship patterns can include authorship, sharing or sending information, management or other social hierarchy, bookmarking, and other gestural signals that describe a relationship between two or more nodes. Additional representational challenges arise with the need to capture interaction dynamics and their changing social context over time, and as such, representational choices vary based ultimately on the analytic questions that are of interest. Besides being a specialized type of social graph, the enterprise social graph is related to network science and graph theory. Changes in how people connect, share, accomplish tasks through online social networks, combined with the growth of ambient public information relevant to an enterprise, contribute to the dynamism and increasing complexity of enterprise social graphs. Whereas meetings, phone calls, or email have been the traditional media for these exchanges, increasingly collaboration and conversation occurs via online social media. As Kogut and Zander point out, the more tacit knowledge is, the more difficult and expensive it is to transmit, since the costs of codifying and teaching will rise as tacitness increases. The consumerization of social business software enables simpler and more cost-effective ways making relationships and tacit knowledge both observable and actionable. From an internal enterprise perspective, understanding the enterprise social graph can provide greater awareness of internal dynamics, organizational and information flow inefficiencies, information seeking and expert identification, or exposing opportunities for new valued connections. From an external perspective, it can provide deeper insights into marketplace conditions and customer demand, customer issues and concerns, product development and co-creation, supply-side operational awareness or external causal relationships. Recent developments in big data analysis, combined with graph mining techniques, make it possible to analyze petabytes of structured and unstructured information and feed user-facing applications. In making use of the enterprise social graph, such applications excel at search, routing, and matching operations, particularly where these include personalization, statistical analysis and machine learning. Examples of applications that combine big data mining techniques over the enterprise social graph include business intelligence, personalized activity streams and intelligent filtering, social search, recommendation engines, automated question or message routing, expertise identification, and information context discovery.
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What is PRICE OPTIMIZATION? What does PRICE OPTIMIZATION mean? PRICE OPTIMIZATION meaning - PRICE OPTIMIZATION definition - PRICE OPTIMIZATION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Price optimization is the use of mathematical analysis by a company to determine how customers will respond to different prices for its products and services through different channels. It is also used to determine the prices that the company determines will best meet its objectives such as maximizing operating profit. The data used in price optimization includes operating costs, inventories and historic prices and sales. Price optimization practice has been implemented in industries including retail, banking, airlines, casinos, hotels, car rental, cruise lines and insurance industries. Price optimization utilizes analysis of big data to predict the behavior of potential buyers to different prices. Companies use price optimization models to determine pricing structures for initial pricing, promotional pricing and discount pricing. Price optimization uses calculations to visualize how demand varies at different price points and combines that data with cost and inventory levels to develop a profitable price point for that product or service. This model is also used to evaluate pricing for different customer segments by simulating how targeted customers will respond to price changes with data-driven scenarios. Price optimization starts with a segmentation of customers. A seller then estimates how customers in different segments will respond to different prices offered through different channels. Given this information, determining the prices that best meet corporate goals can be formulated and solved as a constrained optimization process. The form of the optimization is determined by the underlying structure of the pricing problem. If capacity is constrained and perishable and customer willingness-to-pay increases over time, then the underlying problem is classified as a yield management or revenue management problem. If capacity is constrained and perishable and customer willingness-to-pay decreases over time, then the underlying problem is one of markdown management. If capacity is not constrained and prices cannot be tailored to the characteristics of a particular customer, then the problem is one of list-pricing. If prices can be tailored to the characteristics of an arriving customer then the underlying problem is sometimes called customized pricing. Pricing and Revenue Optimization written by Dr. Robert L. Phillips discusses the economics behind pricing optimization, how it is used as a corporate process, its relationship to supply constraints and how it is perceived by the consumer. In the book, pricing optimization is recognized as an important application for quantitative analysis and there is increased interest in learning its techniques among different industries. Manfred Krafft and Murali K. Mantrala discuss the use of price optimization software in the retail industry and the paradigm shift from price optimization to pricing process improvement in their book Retailing in the 21st Century: Current and Future Trends. The book mentions that the research conducted on price optimization by its traditional definition is not applicable to the retail industry, thus they recommend retailers adopt a process view of pricing. In 2009, the NAW Institute for Distribution Excellence and Texas A&M University’s Industrial Distribution Program conducted a research study titled Pricing Optimization: Striking the Right Balance for Margin Advantage which investigated price optimization and best practices in wholesale distribution. The study recommended wholesalers practice complexity management to provide structure and consistency with regards to pricing in order to improve margins.
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What is DATA QUALITY FIREWALL? What does DATA QUALITY FIREWALL mean? DATA QUALITY FIREWALL meaning - DATA QUALITY FIREWALL definition - DATA QUALITY FIREWALL explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ A data quality firewall is the use of software to protect a computer system from the entry of erroneous, duplicated or poor quality data. Gartner estimates that poor quality data causes failure in up to 50% of customer relationship management systems. Older technology required the tight integration of data quality software, whereas this can now be accomplished by loosely coupling technology in a service-oriented architecture. A data quality firewall guarantees database accuracy and consistency. This application ensures that only valid and high quality data enter the system, which means that it obliquely protects the database from damage; this is extremely important since database integrity and security are absolutely essential. A data quality firewall provides real time feedback information about the quality of the data submitted to the system. The main goal of a data quality process consists in capturing erroneous and invalid data, processing them and eliminating duplicates and, lastly, exporting valid data to the user without failing to store a back-up copy into the database. A data quality firewall acts similarly to a network security firewall. It enables packets to pass through specified ports by filtering out data that present quality issues and allowing the remaining, valid data to be stored in the database. In other words, the firewall sits between the data source and the database and works throughout the extraction, processing and loading of data. It is necessary that data streams be subject to accurate validity checks before they can be considered as being correct or trustworthy. Such checks are of a temporal, formal, logic and forecasting kind.
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Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amount of data, not the extraction of data itself. It also is a buzzword, and is frequently also applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The popular book "Data mining: Practical machine learning tools and techniques with Java" (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis", or "analytics" -- or when referring to actual methods, artificial intelligence and machine learning -- are more appropriate. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
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What is REAL-TIME MARKETING? What does REAL-TIME MARKETING mean? REAL-TIME MARKETING meaning - REAL-TIME MARKETING definition - REAL-TIME MARKETING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Real-time marketing is marketing performed "on-the-fly" to determine an appropriate or optimal approach to a particular customer at a particular time and place. It is a form of market research inbound marketing that seeks the most appropriate offer for a given customer sales opportunity, reversing the traditional outbound marketing (or interruption marketing) which aims to acquire appropriate customers for a given 'pre-defined' offer. The dynamic 'just-in-time' decision making behind a real-time offer aims to exploit a given customer interaction defined by web-site clicks or verbal contact centre conversation. Real-time marketing techniques developed during the mid-1990s following the initial deployment of customer relationship management (CRM) solutions in major retail banking, investment banking and telecommunications companies. The intrinsic and prevailing 'heavyweight' nature of the key CRM vendors at this time, who were generally focused on major back-front office system integration projects, provided an opportunity for niche players within the campaign management application arena. The implementation of real-time marketing solutions through the late 1990s would typically involve a 10-14 week delivery project with 1-2 FTE expert consultants and often would follow an earlier outbound marketing solution implementation. This relatively lightweight delivery model had obvious attractions within the vendor sales cycle and customer procurement context but was ultimately to prove a disincentive for major systems integration services providers to partner with real-time marketing vendors. Real-time marketing solution implementation classically involves the server-side installation of a multithreaded core decisioning application server / interaction transactional-biased schema and supporting client components such as a 'fat-client' desktop campaign studio / rules editor, browser-based marketing user reporting interface and enterprise application APIs such as web services / Java components. Vendors typically will also provide legacy interfaces for COM, sockets and HTTP integration. Vendor solution approaches to real-time learning naturally vary but commonly, the underlying models utilize a naive Bayesian probability classifier, recognising that despite their apparently oversimplified assumptions, these classifiers have worked well in many complex real-world situations. To help gain acceptance with in-house specialist data mining stakeholders, the real-time solutions also support external model scores and execution within offer decision making. The dotcom 'bust' of 2000 inhibited the further development and implementation of item-based collaborative filtering techniques which, having been incorporated within real-time marketing solutions through the 1990s, should have been immediately attractive to online retailers managing hundreds of thousands (or millions) of products as opposed to a retail bank with a hundred propositions across savings, credit card and mortgage product lines. Over time, it became apparent to solution vendors and maturing customers alike, that 'traditional' outbound and emergent inbound marketing initiatives should be consolidated within a coherent and coordinated enterprise marketing strategy. To this end, a class of marketing application known as marketing resource management (MRM) which 'sits above' real-time marketing, began to emerge during the early 21st Century, albeit in a fairly bespoke and implementation-specific guise. The essence of this abstraction layer is that the MRM application orchestrates strategy, stakeholder sign-off, budgeting, program planning, campaign execution and effectiveness reporting across inbound real-time and outbound marketing disciplines. The term "real-time marketing" has the potential weakness of self-limiting the underlying decisioning server capability to cross/up-selling despite the observation that this particular function is generally the most compelling aspect of the application class. Vendors therefore found themselves re-branding real-time marketing products to suggest a more holistic appreciation of enterprise interaction decision management. In some respects, these early real-time marketing customer implementations were ahead of their time despite acknowledged revenue realization within the early adopters.
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Attribute কি? এট্রিবিউটের কি কাজ? এট্রিবিউট ও Entity এর সম্পর্ক স্থাপন । »See Full #Data_Mining Video Series Here: https://www.youtube.com/watch?v=t8lSMGW5eT0&list=PL9qn9k4eqGKRRn1uBmEhlmEd58ATOziA1 In This Video You are gonna learn Data Mining #Bangla_Tutorial Data mining is an important process to discover knowledge about your customer behavior towards your business offerings. » My #Linkedin_Profile: https://www.linkedin.com/in/rafayet13 » Read My Full Article on Data Mining Career Opportunity & So On » Link: https://medium.com/@rafayet13 Learn Data Mining In An Easy Way Data Mining Essential Course Data Mining Course For Beginner #Business_Analysis #Data_Scientist #Data_Analyst Data Mining Bangla Tutorial Data Mining Attributes Types and It's Examples || Relation Between Entity & Attributes || What is Attribute? ডেটা মাইনিং বাংলা টিউরিয়াল ভিডিও সিরিজ ডেটা মাইনিং ও ডেটা সাইন্স আধুনিক বিশ্বে একটি বহুল আলোচিত বিষয়। ডেটা সাইন্টিস্ট, ডেটা ইঞ্জিয়ার এবং ডেটা অ্যানালিস্ট পেশা বর্তমান ও ফিউচার ক্যারিয়্যার হিসেবে তুমুল জনপ্রিয়। সম্পুর্ন বাংলা ভাষায় ডেটা মাইনিং শিখার জন্য বুকবিডির পক্ষ থেকে একটি নিয়মিত ভিডিও টিউটোরিয়াল সিরিজের আয়োজন করা হয়েছে।
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What is PREDICTIVE ANALYTICS? What does PREDICTIVE ANALYSIS mean? PREDICTIVE ANALYSIS meaning - PREDICTIVE ANALYTICS definition - PREDICTIVE ANALYTICS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement. Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, healthcare, child protection, pharmaceuticals, capacity planning and other fields. One of the best-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer's credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time. Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavior patterns. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future. For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions. Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting. For example, "Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions." In future industrial systems, the value of predictive analytics will be to predict and prevent potential issues to achieve near-zero break-down and further be integrated into prescriptive analytics for decision optimization. Furthermore, the converted data can be used for closed-loop product life cycle improvement which is the vision of the Industrial Internet Consortium.
Views: 1648 The Audiopedia
This screen cast demonstrates the use of Microsoft Excel to organize information for qualitative research.
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Knowledge graphs generation is outpacing the ability to intelligently use the information that they contain. Octavian's work is pioneering Graph Artificial Intelligence to provide the brains to make knowledge graphs useful. Our neural networks can take questions and knowledge graphs and return answers. Imagine: a google assistant that reads your own knowledge graph (and actually works) a BI tool reads your business' knowledge graph a legal assistant that reads the graph of your case Taking a neural network approach is important because neural networks deal better with the noise in data and variety in schema. Using neural networks allows people to ask questions of the knowledge graph in their own words, not via code or query languages. Octavian's approach is to develop neural networks that can learn to manipulate graph knowledge into answers. This approach is radically different to using networks to generate graph embeddings. We believe this approach could transform how we interact with databases. Prior knowledge of Neural Networks is not required and the talk will include a simple demonstration of how a Neural Network can use graph data. ----------------------------- ABOUT THE SPEAKER ----------------------------- Andy believes that graphs have the potential to provide both a representation of the world and a technical interface that allows us to develop better AI and to turn it rapidly into useful products. Andy combines expertise in machine learning with experience building and operating distributed software systems and an understanding of the scientific process. Before he worked as a software engineer, Andy was a chemist, and he enjoys using the tensor algebra that he learned in quantum chemistry when working on neural networks. ----------------------------- ONLINE DISCUSSIONS ----------------------------- We'll be taking questions live during the session, but if you have any before or after be sure to post them in the project's thread in the Neo4j Community Site (https://community.neo4j.com/t/online-meetup-deep-learning-with-knowledge-graphs/2963). ---------------------------------------------------------------------------------------- WANT TO BE FEATURED IN OUR NEXT NEO4J ONLINE MEETUP? ---------------------------------------------------------------------------------------- We select talks from our Neo4j Community site! https://community.neo4j.com/ To submit your talk, post in in the #projects (if including a link to github or website) or #content (if linking to a blog post, slideshow, video, or article) categories. ------------------------------------------------------------------------- VOTE FOR THE PRESENTATIONS YOU'D LIKE TO SEE! ------------------------------------------------------------------------- 'VOTE' for the projects and content you'd like to see! Browse the the projects and content categories in our community site and 'heart' the ones you're interested in seeing! community.neo4j.com
Views: 3572 Neo4j
How to enter and analyze questionnaire (survey) data in SPSS is illustrated in this video. Lots more Questionnaire/Survey & SPSS Videos here: https://www.udemy.com/survey-data/?couponCode=SurveyLikertVideosYT Check out our next text, 'SPSS Cheat Sheet,' here: http://goo.gl/b8sRHa. Prime and ‘Unlimited’ members, get our text for free. (Only 4.99 otherwise, but likely to increase soon.) Survey data Survey data entry Questionnaire data entry Channel Description: https://www.youtube.com/user/statisticsinstructor For step by step help with statistics, with a focus on SPSS. Both descriptive and inferential statistics covered. For descriptive statistics, topics covered include: mean, median, and mode in spss, standard deviation and variance in spss, bar charts in spss, histograms in spss, bivariate scatterplots in spss, stem and leaf plots in spss, frequency distribution tables in spss, creating labels in spss, sorting variables in spss, inserting variables in spss, inserting rows in spss, and modifying default options in spss. For inferential statistics, topics covered include: t tests in spss, anova in spss, correlation in spss, regression in spss, chi square in spss, and MANOVA in spss. New videos regularly posted. Subscribe today! YouTube Channel: https://www.youtube.com/user/statisticsinstructor Video Transcript: In this video we'll take a look at how to enter questionnaire or survey data into SPSS and this is something that a lot of people have questions with so it's important to make sure when you're working with SPSS in particular when you're entering data from a survey that you know how to do. Let's go ahead and take a few moments to look at that. And here you see on the right-hand side of your screen I have a questionnaire, a very short sample questionnaire that I want to enter into SPSS so we're going to create a data file and in this questionnaire here I've made a few modifications. I've underlined some variable names here and I'll talk about that more in a minute and I also put numbers in parentheses to the right of these different names and I'll also explain that as well. Now normally when someone sees this survey we wouldn't have gender underlined for example nor would we have these numbers to the right of male and female. So that's just for us, to help better understand how to enter these data. So let's go ahead and get started here. In SPSS the first thing we need to do is every time we have a possible answer such as male or female we need to create a variable in SPSS that will hold those different answers. So our first variable needs to be gender and that's why that's underlined there just to assist us as we're doing this. So we want to make sure we're in the Variable View tab and then in the first row here under Name we want to type gender and then press ENTER and that creates the variable gender. Now notice here I have two options: male and female. So when people respond or circle or check here that they're male, I need to enter into SPSS some number to indicate that. So we always want to enter numbers whenever possible into SPSS because SPSS for the vast majority of analyses performs statistical analyses on numbers not on words. So I wouldn't want and enter male, female, and so forth. I want to enter one's, two's and so on. So notice here I just arbitrarily decided males get a 1 and females get a 2. It could have been the other way around but since male was the first name listed I went and gave that 1 and then for females I gave a 2. So what we want to do in our data file here is go head and go to Values, this column, click on the None cell, notice these three dots appear they're called an ellipsis, click on that and then our first value notice here 1 is male so Value of 1 and then type Label Male and then click Add. And then our second value of 2 is for females so go ahead and enter 2 for Value and then Female, click Add and then we're done with that you want to see both of them down here and that looks good so click OK. Now those labels are in here and I'll show you how that works when we enter some numbers in a minute. OK next we have ethnicity so I'm going to call this variable ethnicity. So go ahead and type that in press ENTER and then we're going to the same thing we're going to create value labels here so 1 is African-American, 2 is Asian-American, and so on. And I'll just do that very quickly so going to Values column, click on the ellipsis. For 1 we have African American, for 2 Asian American, 3 is Caucasian, and just so you can see that here 3 is Caucasian, 4 is Hispanic, and other is 5, so let's go ahead and finish that. Four is Hispanic, 5 is other, so let's go to do that 5 is other. OK and that's it for that variable. Now we do have it says please state I'll talk about that next that's important when they can enter text we have to handle that differently.
Views: 638270 Quantitative Specialists
Retailers have sophisticated demand forecast models and agile supply chains to ensure that the just the right amount of products are placed on the store shelves to meet demand. And yet, for some products, they get this amount wrong leading to empty shelves and overstocks, lost revenue opportunities, waste and profit erosion through discounting. Often it is the outside forces at play which explain these misses. A news article, government comment, scientific publication, the weather, the local rock concert are all examples of external forces which can influence local demand. Furthermore, the real time, public and conversational characteristics of Twitter data provide the barometer as to how people are reacting to such stories and thus help quantify the strength of the relationship of the external force to the impact on demand forecasts. By using advanced analytics and real time social media postings, the large retailer was able to reduce misses in its demand forecasting and store replenishment operations. Even small improvements to the model, when amplified at the scale of the retailer's supply chain, translated into significant financial benefits. Learn more about IBM and Twitter ibm.co/ibmandtwitter Subscribe to the IBM Analytics Channel: https://www.youtube.com/subscription_center?add_user=ibmbigdata The world is becoming smarter every day, join the conversation on the IBM Big Data & Analytics Hub: www.ibmbigdatahub.com www.facebook.com/IBManalytics www.twitter.com/IBMAnalytics www.linkedin.com/company/ibm-big-data-&-analytics www.slideshare.net/IBMBDA
Views: 2719 IBM Analytics
business 101: everything you need to know about business and startup basics. Learn the foundation concepts underlying all businesses, small to large. a video tutorial that covers all the basics, explaining concepts such as business goals, stakeholders, profits, and various types of businesses. Outlines what you need to think about if you were to start your own business, such as determining what your product or service will be, making and delivering your product or service, and funding your business.
Table of Contents Q&A 1:14:29 Should healthcare be more digitized? Absolutely. But if we go about it the wrong way... or the naïve way... we will take two steps forward and three steps back. Join Health Catalyst's President of Technology, Dale Sanders, for a 90-minute webinar in which he will describe the right way to go about the technical digitization of healthcare so that it increases the sense of humanity during the journey. The topics Dale covers include: • The human, empathetic components of healthcare’s digitization strategy • The AI-enabled healthcare encounter in the near future • Why the current digital approach to patient engagement will never be effective • The dramatic near-term potential of bio-integrated sensors • Role of the “Digitician” and patient data profiles • The technology and architecture of a modern digital platform • The role of AI vs. the role of traditional data analysis in healthcare • Reasons that home grown digital platforms will not scale, economically Most of the data that’s generated in healthcare is about administrative overhead of healthcare, not about the current state of patients’ well-being. On average, healthcare collects data about patients three times per year from which providers are expected to optimize diagnoses, treatments, predict health risks and cultivate long-term care plans. Where’s the data about patients’ health from the other 362 days per year? McKinsey ranks industries based on their Digital Quotient (DQ), which is derived from a cross product of three areas: Data Assets x Data Skills x Data Utilization. Healthcare ranks lower than all industries except mining. It’s time for healthcare to raise its Digital Quotient, however, it’s a delicate balance. The current “data-driven” strategy in healthcare is a train wreck, sucking the life out of clinicians’ sense of mastery, autonomy, and purpose. Healthcare’s digital strategy has largely ignored the digitization of patients’ state of health, but that’s changing, and the change will be revolutionary. Driven by bio-integrated sensors and affordable genomics, in the next five years, many patients will possess more data and AI-driven insights about their diagnosis and treatment options than healthcare systems, turning the existing dialogue with care providers on its head. It’s going to happen. Let’s make it happen the right way.
Views: 361 Health Catalyst
How To Analyze People On Sight | GreatestAudioBooks 🎅 Give the gift of audiobooks! 🎄 Click here: http://affiliates.audiobooks.com/tracking/scripts/click.php?a_aid=5b8c26085f4b8&a_bid=ec49a209 🌟SPECIAL OFFERS: ► Free 30 day Audible Trial & Get 2 Free Audiobooks: https://amzn.to/2Iu08SE ...OR: 🌟 try Audiobooks.com 🎧for FREE! : http://affiliates.audiobooks.com/tracking/scripts/click.php?a_aid=5b8c26085f4b8 ► Shop for books & gifts: https://www.amazon.com/shop/GreatestAudioBooks How To Analyze People On Sight | GreatestAudioBooks by Elsie Lincoln Benedict & Ralph Pain Benedict - Human Analysis, Psychology, Body Language - In this popular American book from the 1920s, "self-help" author Elsie Lincoln Benedict makes pseudo-scientific claims of Human Analysis, proposing that all humans fit into specific five sub-types. Supposedly based on evolutionary theory, it is claimed that distinctive traits can be foretold through analysis of outward appearance. While not considered to be a serious work by the scientific community, "How To Analyze People On Sight" makes for an entertaining read. . ► Follow Us On TWITTER: https://www.twitter.com/GAudioBooks ► Friend Us On FACEBOOK: http://www.Facebook.com/GreatestAudioBooks ► For FREE SPECIAL AUDIOBOOK OFFERS & MORE: http://www.GreatestAudioBooks.com ► SUBSCRIBE to Greatest Audio Books: http://www.youtube.com/GreatestAudioBooks ► BUY T-SHIRTS & MORE: http://bit.ly/1akteBP ► Visit our WEBSITE: http://www.GreatestAudioBooks.com READ along by clicking (CC) for Caption Transcript LISTEN to the entire book for free! Chapter and Chapter & START TIMES: 01 - Front matter -- - 00:00 02 - Human Analysis - 04:24 03 - Chapter 1, part 1 The Alimentive Type - 46:00 04 - Chapter 1, part 2 The Alimentive Type - 1:08:20 05 - Chapter 2, part 1 The Thoracic Type - 1:38:44 06 - Chapter 2, part 2 The Thoracic Type - 2:10:52 07 - Chapter 3, part 1 The Muscular type - 2:39:24 08 - Chapter 3, part 2 The Muscular type - 3:00:01 09 - Chapter 4, part 1 The Osseous Type - 3:22:01 10 - Chapter 4, part 2 The Osseous Type - 3:43:50 11 - Chapter 5, part 1 The Cerebral Type - 4:06:11 12 - Chapter 5, part 2 The Cerebral Type - 4:27:09 13 - Chapter 6, part 1 Types That Should and Should Not Marry Each Other - 4:53:15 14 - Chapter 6, part 2 Types That Should and Should Not Marry Each Other - 5:17:29 15 - Chapter 7, part 1 Vocations For Each Type - 5:48:43 16 - Chapter 7, part 2 Vocations For Each Type - 6:15:29 #audiobook #audiobooks #freeaudiobooks #greatestaudiobooks #book #books #free #top #best #psychology This video: Copyright 2012. Greatest Audio Books. All Rights Reserved. Audio content is a Librivox recording. All Librivox recordings are in the public domain. For more information or to volunteer visit librivox.org. Disclaimer: As an Amazon Associate we earn from qualifying purchases. Your purchases through Amazon affiliate links generate revenue for this channel. Thank you for your support.
Views: 2118764 Greatest AudioBooks
Database marketing is a form of direct marketing using databases of customers or potential customers to generate personalized communications in order to promote a product or service for marketing purposes. The method of communication can be any addressable medium, as in direct marketing. The distinction between direct and database marketing stems primarily from the attention paid to the analysis of data. Database marketing emphasizes the use of statistical techniques to develop models of customer behavior, which are then used to select customers for communications. As a consequence, database marketers also tend to be heavy users of data warehouses, because having a greater amount of data about customers increases the likelihood that a more accurate model can be built. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 359 Audiopedia
Data science training 75% OFF coupon: http://bit.ly/2TOqJ7A DOWNLOAD THE RESOURCES: http://bit.ly/2TKO228 What it takes to become a data scientist -- starting in the right place. In this webinar two of our instructors, Iliya and Simona, talk about the 3 things they needed to learn before all the books and trainings started to finally click. They discuss the most confusing data science terms, how they fit together, and where in the data processing timeline the data science processes happen. MORE INFORMATION ABOUT THE TRAINING: http://bit.ly/2TOqJ7A Follow us on YouTube: https://www.youtube.com/c/365DataScience Connect with us on our social media platforms: Website: https://bit.ly/2TrLiXb Facebook: https://www.facebook.com/365datascience Instagram: https://www.instagram.com/365datascience Twitter: https://twitter.com/365datascience LinkedIn: https://www.linkedin.com/company/365d... Get in touch about the training at: [email protected] Comment, like, share, and subscribe! We will be happy to hear from you and will get back to you!
Views: 7918 365 Data Science
Kalina Silverman wanted to see what could happen if she approached strangers and skipped the small talk to have more meaningful conversations with them instead. She made a video documenting the experience. The stories she heard and the connections she made proved that there's power in taking the time to stop and ask people to reflect on the questions that truly matter in life. Since then, she has continued to work on expanding Big Talk into a movement that inspires and enables people to connect with one another on a deeper level. Learn more about it at www.makebigtalk.com and visit Kalina at www.kalinasilverman.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: 5530224 TEDx Talks
What is customer retention? Definition and metrics ngdatawhat meaning 20 retention strategies that work help scout. What is customer retention? does retention medallia. 10 customer retention strategies to implement today groove hq. High customer retention means customers of the product or business tend to return to, continue buy in some other way not defect another business, non use entirely definition an assessment service can be prevented, by showing just a little more attention your customers, appreciation day never hurts either. Customer retention is a set of steps taken or processes implemented to reduce retaining customers means that lifetime value both in terms their the customer journey typical lifecycle you can identify mar 24, 2015 if your saas pay $50 per month, and average below are 10 strategies start using right apr 27, 2017 what does mean? desired result marketing keeping happy, satisfied browse enough web sites, will see increasing by just 2. May 5, 2014 discover 9 customer retention strategies you can implement today cultivating shared values means taking an interest in your clients and their apr 9, 2013 many companies realize that is extremely important, but few understand what losing a single mean to business. What do you stand customer retention is a simple concept happy customers who feel important and are regularly communicated with in the right way will keep coming back. Customer retention strategies 9 tactics for companies. What auto maintenance facility do you think sandra went to? . Googleusercontent search. Here are a few different ideas you can try as well jun 18, 2017jun 21, 2016. Aug 5, 2017 customer retention refers to the activities and actions companies organizations take reduce number of defections. Customer retention strategies marketing wizdom. Feb 2, 2017 what do you mean by customer retention? Lets explain the benefits of retention to an organisation. Marketing wizdom can introduce you to a number of simple customer retention strstegies that will cost little or nothing implement. Customer retention accenture strategy. Customer retention unlock 5 strategies to increase growth & profits. Definition what is customer retention? Tallyfy. A returning customer is cheaper to the business, as they will need spend less on advertising or inducements such price cutting and you may also be interested in oct 30, 2013 how do calculate your retention rate so that means 85. You also might like jun 16, 2016 good to know, but exactly does retention mean? If you want loyal customers, need create real connections with them. Behind each technique dec 30, 2016 this article points out 10 different steps businesses can use for increasing customer retention. It is a customer retention the ability of business to retain customers. Customer retention rate explained for dummies. Customer retention encyclopedia business terms customer definition what is shopify. What is customer retention? Definition and metrics ngdata. Customer retention
Views: 8 Your Question I
What is CRYPTO CLOUD COMPUTING? What does CRYPTO CLOUD COMPUTING mean? CRYPTO CLOUD COMPUTING meaning - CRYPTO CLOUD COMPUTING definition - CRYPTO CLOUD COMPUTING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Cloud computing is a combination of IaaS, PaaS, SaaS. To construct a secure cloud computing system, security at infrastructure, service platforms and application software levels have to be studied for a secure cloud computing system. Information encryption is one of effective means to achieve cloud computing information security. Traditionally, information encryption focuses on specified stages and operations, such as data encryption. For cloud computing, a system level design has to be implemented. Crypto cloud computing is a new secure cloud computing architecture. It can provide protection of information security at the system level, and allows users access to shared services conveniently and accurately. Crypto cloud computing protects individual’s connections with the outside world. It can protect the personal privacy without any delay of information exchange. Crypto cloud computing is based on the Quantum Direct Key system. Quantum Direct Key (QDK) is a set of advanced asymmetric offline key mechanism. In this mechanism, all entities get public and private key pair according to their ID. Each entity only holds its own private key, but has a public key generator to generate any public key. In this system, an entity can produce the public key of any other entities offline, no any third-party agency (such as CA) is necessary. Crypto cloud computing based on QDK can avoid network traffic congestion, and other drawbacks using current encryption system. In the crypto cloud computing system, each entity encrypts data using his/her own private key. All elements in the system such as cloud computing infrastructure units, platform, virtualization tools and all involved entities have their own keys. While fulfilling their own functions of information exchange and processing, all these elements will use the public key and private key to perform authentication first. What’s more, events occur in the cloud computing are also assigned a unique key. In this way, crypto cloud system guarantees the security and credibility of information exchange. Current cloud computing structure is developed for data and computing sharing. Security is not priority of system. On the contrary, encryption and security are inherently integrated in the crypto cloud computing based on the QDK. QDK authorized function units are bricks of crypto cloud computing. Besides primary function of data en/decryption, crypto cloud computing also provides many security related functions. For example, all channels sign transmit data using with their own keys, and the receiving terminals can avoid hijacking by verifying signature. What’s more, the exact position of security leakage can be identified determined by analyzing digital signatures of forged data. Based on such capabilities, crypto-related functions can be provided as services in cloud, which is named as ‘Crypto as a service (CAAS)’. Crypto cloud computing is not only the advances in information technology, but also innovation of logical relationship. In crypto cloud computing system, non-system data is not allowed to store and transmit. Private Key and offline public key, play a role of identification and certification in the process of information exchange. In this way, the cloud establishes a relationship of trust with a customer. Data identification depends on the logical relationship of mutual trust or need, and the logical relationship depends on the cloud customer. Crypto cloud computing is a new framework for cyber resource sharing. It protects data security and privacy. Well, in cloud environment, crypto cloud computing guarantees the information security and integrity during whole procedure. Security management of cloud computing can also be performed by authorizing the signatures of every element involved. What’s more, a user can retrieve all related resources using his QDK key. There is no personal privacy under the current cloud framework, as pointed out by Mark Zuckerberg, 'the Age of Privacy Is Over '.However, with the development of crypto cloud computing, we can resolve the conflict between services data sharing and privacy security. It opens up new prospects for the development of information sharing technology.
Views: 140 The Audiopedia
Subscribe: http://bit.ly/1IO8i6J Hi My name is Dr Wan Mohd Hirwani Wan Hussain. SUBSCRIBE http://bit.ly/1IO8i6J for step by step tutorials for E-learning, Entrepreneurship, Internet Marketing and Social Media. Youtube Channel: https://www.youtube.com/channel/UC8ZbOkjwUOnOwnIF0_GBxcg Plus: https://www.google.com/+DrWanMohdHirwaniWanHussain Facebook: https://www.facebook.com/profile.php?id=100006056059092 Twitter: https://twitter.com/wanmohdhirwani Instagram: https://instagram.com/wanhirwani Pinterest: https://www.pinterest.com/WanMohdHirwani/ More videos by Dr Wan Mohd Hirwani Wan Hussain: 10 Advice and Tips For PhD Students https://goo.gl/V9BDf2 Problems In Commercialization University Research https://goo.gl/RxJOuc Research Tools That Graduate Students Must Know https://goo.gl/Rnlppo Qualitative Research Tools For Graduate Students https://goo.gl/HTAw2I Web 2 0 in Research and Teaching https://goo.gl/oz6O6x Quantitative Research Tools For Graduate Students https://goo.gl/WhkLPW MAIL: Graduate School Of Business, Universiti Kebangsaan Malaysia, Selangor Malaysia Business enquiries only: wmhwh [at] ukm.edu.my
Views: 876 Dr Wan Mohd Hirwani Wan Hussain
Подпишитесь и включите звонок! https://on.fless.pro/subscribe За полтора месяца получил много вопросов о карьере в бизнес-аналитике, консалтинге, data science. Сегодня наконец-то на них отвечу. После этого - живой ask me anything. Подключайтесь, будет интересно =)
Views: 2613 Fless
Dr. Chaitan Baru and Dr. Elena Zheleva from the National Science Foundation presents a lecture on "Databases & Data Warehouses, Data: Structures, Types, Integrations" Lecture Abstract This talk will provide an overview of the evolution of database and data warehouse approaches and technologies. Where did we begin, and where have we come to? In the process, we will provide a review of concepts like structured and semistructured databases; schema on-write versus schema on-read; SQL/noSQL database; data integration; and data integrity constraints. The talk will be motivated by example of Data Science use cases. View slides from this lecture https://drive.google.com/open?id=0B4IAKVDZz_JUck8tQlJ0N2VTSlU About the Speakers: Chaitan Baru is Senior Advisor for Data Science in the Computer and Information Science and Engineering (CISE) Directorate at the National Science Foundation, where he coordinates the cross-Foundation BIGDATA research program, advises the NSF Big Data Hubs and Spokes program, assists in CISE strategic planning in Data Science, and participates in interdisciplinary and inter-agency Data Science-related activities. He co-chairs the Big Data Inter-agency Working Group, and is co-author of the US Federal Big Data R&D Strategic Plan (http://bit.ly/1Ughpjt), released May 2016 by the Networking and Information Technology R&D (NITRD) group of the National Coordination Office, White House Office of Science and Technology Policy. Elena Zheleva is a computer scientist with a background in machine learning, social network analysis and online privacy. Elena has presented her research at top-tier conferences, and she is the co-author of the book "Privacy in Social Networks." After completing her Ph.D. in Computer Science from the University of Maryland College Park in 2011, Elena spent five years in industry as a data scientist, focusing on recommender systems and incentivized social sharing. She was also fortunate to intern at Microsoft Research, AOL and The Institute for Genomic Research. Currently, Elena is an AAAS Science and Technology Policy Fellow at the National Science Foundation where she contributes to big data and data science initiatives. Please join our weekly meetings from your computer, tablet or smartphone. Visit our website to learn how to join! http://www.bigdatau.org/data-science-seminars
Views: 3160 BD2K Guide to the Fundamentals of Data Science
#TwitchPrime – 1 Month free #RuneScape membership & exclusive loot: https://rs.game/TwitchPrimeYT Play RuneScape free now: http://bit.ly/PlayRuneScapeNOW The RuneScape Documentary is here! Find out how one of the most successful MMORPGs of all time came to being – from humble beginnings in the Gower Brothers’ family home to reaching our 250 millionth account in 2016, and all the thrills and spills in between. We give you ’15 Years of Adventure’ – a History of RuneScape. Featuring interviews with some of the most famous RuneScape players of all time (including the legendary Zezima), as well as Jagex staff past and present, it’s essential viewing for gaming fans the world over. We hope you enjoy! Remember to subscribe and share this RuneScape movie using #15YearsOfAdventure! For more RuneScape history videos, you can follow this great channel: https://www.youtube.com/channel/UC40WkzHQLHf6uLMw1If6T-A/videos Join our other communities! Join us on: Twitch - http://www.twitch.tv/runescape Reddit - http://www.reddit.com/r/runescape Twitter - http://twitter.com/runescape Facebook - http://www.facebook.com/runescape www.runescape.com #RuneScape
Views: 1821535 RuneScape
What is PROCESS ARCHITECTURE? What does PROCESS ARCHITECTURE mean? PROCESS ARCHITECTURE meaning - PROCESS ARCHITECTURE definition - PROCESS ARCHITECTURE explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Process architecture is the structural design of general process systems and applies to fields such as computers (software, hardware, networks, etc.), business processes (enterprise architecture, policy and procedures, logistics, project management, etc.), and any other process system of varying degrees of complexity. Processes are defined as having inputs, outputs and the energy required to transform inputs to outputs. Use of energy during transformation also implies a passage of time: a process takes real time to perform its associated action. A process also requires space for input/output objects and transforming objects to exist: a process uses real space. A process system is a specialized system of processes. Processes are composed of processes. Complex processes are made up of several processes that are in turn made up of several processes. This results in an overall structural hierarchy of abstraction. If the process system is studied hierarchically, it is easier to understand and manage; therefore, process architecture requires the ability to consider process systems hierarchically. Graphical modeling of process architectures is considered by Dualistic Petri nets. Mathematical consideration of process architectures may be found in CCS and the ?-calculus. The structure of a process system, or its architecture, can be viewed as a dualistic relationship of its infrastructure and suprastructure. The infrastructure describes a process system's component parts and their interactions. The suprastructure considers the super system of which the process system is a part. (Suprastructure should not be confused with superstructure, which is actually part of the infrastructure built for (external) support.) As one traverses the process architecture from one level of abstraction to the next, infrastructure becomes the basis for suprastructure and vice versa as one looks within a system or without. Requirements for a process system are derived at every hierarchical level. Black-box requirements for a system come from its suprastructure. Customer requirements are black-box requirements near, if not at, the top of a process architecture's hierarchy. White-box requirements, such as engineering rules, programming syntax, etc., come from the process system's infrastructure. Process systems are a dualistic phenomenon of change/no-change or form/transform and as such, are well-suited to being modeled by the bipartite Petri Nets modeling system and in particular, process-class Dualistic Petri nets where processes can be simulated in real time and space and studied hierarchically.
Views: 595 The Audiopedia
Original video here: https://www.youtube.com/watch?v=jweQNDCe218 Link 1: NVLink on NVIDIA GeForce RTX 2080 & 2080 Ti in Windows 10 http://puget.systems/go/s_nvlink_win10 Link 2: NVLINK on RTX 2080 TensorFlow and Peer-to-Peer Performance with Linux http://puget.systems/go/s_nvlink_linux Link 3: "Optimizing Storage for Premiere Pro" video https://www.youtube.com/watch?v=r7zI7MYSY_0 Link 4: All Puget Systems publications http://puget.systems/go/s_publications Link 5: Puget Systems Oil Immersion Cooling http://puget.systems/go/s_immersion Link 6: Thermal Paste Application Techniques http://puget.systems/go/s_thermal_paste Link 7: Estimating CPU Performance using Amdahls Law http://puget.systems/go/s_amdhal Click here if you're interested in subscribing: http://bit.ly/Subscribe2SED ⇊ Click below for more links! ⇊ HOW TO BUILD A COMPUTER 1. DON'T BUY THE MOST EXPENSIVE MACHINE. 2. RESEARCH ACTUAL BENCHMARK DATA 3. BUY HARDWARE BASED ON YOUR SOFTWARE APPLICATION 4. More cores doesn't mean it's better for you! Side note: The fast rendering capability of this new machine actually let me eat dinner with my family on the first night I used it. This is incredibly important to me. A special thank you to Puget Systems for allowing me to visit and for helping me ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ GET SMARTER SECTION I asked Jon to put together the specs on the computer I spec'd out https://www.pugetsystems.com/go/smarter Amdahl's Law https://en.wikipedia.org/wiki/Amdahl%... Moore's Law https://en.wikipedia.org/wiki/Moore%2... ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Tweet Ideas to me at: http://twitter.com/smartereveryday I'm "ilikerockets" on Snapchat. Snap Code: http://i.imgur.com/7DGfEpR.png Smarter Every Day on Facebook https://www.facebook.com/SmarterEveryDay Smarter Every Day on Patreon http://www.patreon.com/smartereveryday Smarter Every Day On Instagram http://www.instagram.com/smartereveryday Smarter Every Day SubReddit http://www.reddit.com/r/smartereveryday Ambiance and musicy things by: Gordon McGladdery did the outro music the video. http://ashellinthepit.bandcamp.com/ The thought is it my efforts making videos will help educate the world as a whole, and one day generate enough revenue to pay for my kids college education. Until then if you appreciate what you've learned in this video and the effort that went in to it, please SHARE THE VIDEO! If you REALLY liked it, feel free to pitch a few dollars Smarter Every Day by becoming a Patron. http://www.patreon.com/smartereveryday Warm Regards, Destin
Views: 304234 Smarter Every Day 2
Move Beyond Trade-Off Thinking. When it comes to our hardest choices, it can seem as though making trade-offs is inevitable. But what about those crucial times when accepting the obvious trade-off just isn't good enough? What do we do when the choices in front of us don't get us what we need? In those cases, rather than choosing the least worst option, we can use the models in front of us to create a new and superior answer. This is integrative thinking. First introduced by world-renowned strategic thinker Roger Martin in "The Opposable Mind," integrative thinking is an approach to problem solving that uses opposing ideas as the basis for innovation. Now, in "Creating Great Choices," Martin and his longtime thinking partner Jennifer Riel vividly illustrate how integrative thinking works, and how to do it. The book includes fresh stories of successful integrative thinkers that will demystify the process of creative problem solving, as well as practical tools and exercises to help readers engage with the ideas. And it lays out the authors' four-step methodology for creating great choices, which can be applied in virtually any context. The result is a replicable, thoughtful approach to finding a "third and better way" to make important choices in the face of unacceptable trade‐offs. Insightful and instructive, "Creating Great Choices" blends storytelling, theory, and hands-on advice to help any leader or manager facing a tough choice. Get the book here: https://goo.gl/FJb1HR Moderated by David Barry.
Views: 7378 Talks at Google
#BladeAndSoul #BNS #Русификатор #Обновление Стрим в 10:00 Очередной утренний стрим =) Как всегда занимаемся фармом ачивки на СА, думаю сегодня стоит доделать. Кстати ДПС тестил на копье и вышло 790к. Кстати какую тему для видео можно придумать ? Новые оружия и триграммы 14.05 на русском сервере https://youtu.be/fCoCtiGnQ9w Вся информация о конкурсе есть тут https://vk.com/holygameindustry?w=wall-156796493_3421 Установка русификатора и корейского клиента https://youtu.be/6XqN1Q84SKk Уровень аккаунта - полный разбор https://youtu.be/HQDaBkteOKs Огромное спасибо группе https://vk.com/bnscnclub за помощь в данном деле. Ребята реально молодцы =) 💰 💲Хотите поддержать развитие канала или русификатора ? вам однозначно сюда https://donatepay.ru/don/94542 💲💰 👾 Дискорд 🔫https://discord.gg/t89KS6t 🔫👾 👾 Самый лучший VPN 🔫http://nopi.ng/SLIMONA 🔫👾 👾Ютуб канал🔫https://www.youtube.com/c/HolyGameIndustry .;;;;;;;; 👾 👾Твич канал🔫 https://www.twitch.tv/holygameindustry 🔫;;;;;;; 👾 👾Группа ВК🔫 https://vk.com/holygameindustry ; 👾 Support the stream: https://streamlabs.com/holygameindustry Multistreaming with https://restream.io/
Views: 273 Holy Game Industry
Facebook CEO Mark Zuckerberg faces a second a day of testimony in front of the House energy and commerce committee amid concerns over privacy on the social media site. It was revealed Facebook shared the information of 87 million users with data giant Cambridge Analytica. To read more: http://cbc.ca/ »»» Subscribe to CBC News to watch more videos: http://bit.ly/1RreYWS Connect with CBC News Online: For breaking news, video, audio and in-depth coverage: http://bit.ly/1Z0m6iX Find CBC News on Facebook: http://bit.ly/1WjG36m Follow CBC News on Twitter: http://bit.ly/1sA5P9H For breaking news on Twitter: http://bit.ly/1WjDyks Follow CBC News on Instagram: http://bit.ly/1Z0iE7O Download the CBC News app for iOS: http://apple.co/25mpsUz Download the CBC News app for Android: http://bit.ly/1XxuozZ »»»»»»»»»»»»»»»»»» For more than 75 years, CBC News has been the source Canadians turn to, to keep them informed about their communities, their country and their world. Through regional and national programming on multiple platforms, including CBC Television, CBC News Network, CBC Radio, CBCNews.ca, mobile and on-demand, CBC News and its internationally recognized team of award-winning journalists deliver the breaking stories, the issues, the analyses and the personalities that matter to Canadians.
Views: 18210 CBC News
The iconic Margaret Cho joins Dom for this week's episode of Dom Irrera Live From The Laugh Factory! Want to see more stand up comedy? Subscribe to the Laugh Factory's channel: http://youtube.com/subscription_center?add_user=thelaughfactory Tell us what you thought in the comments. Please click thumbs up and add this video to your favorites if you LOL'd. For exclusive ad free content and full length stand up videos, try out Laugh Factory VIP for only 99¢ per month: http://youtube.com/user/laughfactoryVIP http://twitter.com/thelaughfactory http://facebook.com/laughfactoryofc http://instagram.com/laughfactoryhw http://laughfactory.com LIVE SHOW TICKETS: http://www.laughfactory.com/clubs
Views: 4863 Laugh Factory
PANEL 2: BIG DATA The Internet, social media, and data mining have changed language and our ability to analyze usage, and increased sensitivities to the power of the words we use. This panel will explore how these new forms of discourse and analysis expand our understanding of the interplay of gender, personal narrative, and language, as well as data scraping that enables a statistical study of language usage by demographics. Ben Hookway (7:43), Chief Executive Officer, Relative Insight Lyle Ungar (20:53), Professor and Graduate Group Chair, Computer and Information Science, University of Pennsylvania Alice E. Marwick (36:19), Assistant Professor, Department of Communication and Media Studies, and Director, McGannon Center for Communication Research, Fordham University Moderator: Rebecca Lemov, Associate Professor of the History of Science, Harvard University Q&A (52:02)
Views: 1127 Harvard University
Moderator: Zulfikar Ramzan, Chief Technology Officer, RSA Ron Rivest, Institute Professor, MIT Adi Shamir, Professor, Computer Science Department, Weizmann Institute of Science, Israel Whitfield Diffie, Cryptographer and Security Expert, Cryptomathic Paul Kocher, Independent Researcher Moxie Marlinspike, Founder, Signal Despite how sophisticated information security has become, it is still a relatively young discipline. The founders of our field continue to be actively engaged in research and innovation. Join us to hear these luminaries engage in an enlightening discussion on the past, present and future of our industry. https://www.rsaconference.com/events/us18/agenda/sessions/11490-The-Cryptographers%E2%80%99-Panel
Views: 6567 RSA Conference
View full lesson: http://ed.ted.com/lessons/the-science-of-milk-jonathan-j-o-sullivan The milk industry produces in excess of 840 million tons of products each year. Why do humans drink so much milk? And given that all mammals lactate, why do we favor certain types of milk over others? Jonathan J. O’Sullivan describes how milk is made. Lesson by Jonathan J. O'Sullivan, animation by TED-Ed.
Views: 866522 TED-Ed
JUST YOUR AVERAGE DAY OF HUSTLING AND A QUICK STOP IN BOSTON. watch all of my journey as an entrepreneur HERE: https://www.youtube.com/playlist?list=PLfA33-E9P7FA-A72QKBw3noWuQbaVXqSD music featured in this DAILYVEE: ♫"Shallow Souls Master" By Maddy Raven - https://soundcloud.com/maddyraven/shallowsouls 💿 DailyVee Selects: https://soundcloud.com/garyvee/sets/dailyvee-selects -- Thank you for watching this video. I hope that you keep up with the daily videos I post on the channel, subscribe, and share your learnings with those that need to hear it. Your comments are my oxygen, so please take a second and say ‘Hey’ ;). -- ► Subscribe to My Channel Here http://www.youtube.com/subscription_center?add_user=GaryVaynerchuk -- Gary Vaynerchuk is a serial entrepreneur and the CEO and founder of VaynerMedia, a full-service digital agency servicing Fortune 500 clients across the company’s 5 locations. Gary is also a prolific public speaker, venture capitalist, 4-time New York Times Bestselling Author, and has been named to both Crain’s and Fortune’s 40 Under 40 lists. Gary is the host of the #AskGaryVee Show, a business and marketing focused Q&A video show and podcast, as well as DailyVee, a docu-series highlighting what it’s like to be a CEO, investor, speaker, and public figure in today’s digital age. Make sure to stay tuned for Gary’s latest project Planet of the Apps, Apple’s very first video series, where Gary will be a judge alongside Will.I.Am, Jessica Alba, and Gwyneth Paltrow. ---- Follow Me Online Here: Instagram: http://instagram.com/garyvee Facebook: http://facebook.com/gary Snapchat: https://www.snapchat.com/add/garyvee Website: http://garyvaynerchuk.com Soundcloud | https://soundcloud.com/garyvee/ Twitter: http://twitter.com/garyvee Medium: http://medium.com/@garyvee Planet of the Apps | http://planetoftheapps.com Podcast : http://garyvaynerchuk.com/podcast Wine Library : http://winelibrary.com Subscribe to my VIP Newsletter for exclusive content and weekly giveaways here: http://garyvee.com/GARYVIP
Views: 117769 GaryVee
Deep learning has generally been associated with unstructured data such as images, language, and audio. However it turns out that the structured data found in the columns of a database table or spreadsheet, where the columns can each represent different types of information in different ways (e.g. sales in dollars, area as zip code, product id, etc), can also be used very effectively by a neural network. This is equally true if the data can be represented as a time series (i.e. the rows represent different times or time periods). In particular, what we learnt in part 1 about embeddings can be used not just for collaborative filtering and word encodings, but also for arbitrary categorical variables representing products, places, channels, and so forth. This has been highlighted by the results of two Kaggle competitions that were won by teams using this approach. We will study both of these datasets and competition winning strategies in this lesson. Finally, we’ll look at how the Densenet architecture we studied in the last lesson can be used for image segmentation - that is, exactly specifying the location of every object in an image. This is another type of generative model, as we learnt in lesson 9, so many of the basic ideas from there will be equally applicable here. We hope you enjoyed your deep learning journey with us! Now that you’ve finished, be sure to drop by the forums to tell us how you’re using deep learning in your life or work, or what projects you’re considering working on now.
Views: 12539 Jeremy Howard
Data literacy isn't just about reading charts and graphs that other people make, it's also about having the capability and confidence to dive into the data ourselves to explore it and see what it's telling us. In this webinar, we'll cover the W-I-S-D-O-M Data-Working Flow that takes us from a key question through data exploration and discovery, all the way to an effective presentation or breakthrough decision.
Views: 164 Data Literacy
Now you can just copy a sentence or phrase within any app and get instant translation in your chosen language. You can copy to translate in any app. Google Play’s Best Self-Improvement App & Best App of 2016! U-Dictionary works great Offline too. You can download offline packs for 38 International languages, Collins Advanced Dictionary, WordNet Dictionary and also English Sample Sentences. You can use U-Dictionary without internet. Also, the installation package is only 5Mb. U-Dictionary is not only the largest English Offline Dictionary but is also the best app for language reference, English learning and vocabulary building. U-Dictionary is the best companion for all your English language needs! U-Dictionary Features: Copy to Translate: Copy any word or sentence while browsing, messaging, or reading news, to get meaning instantaneously. Quick Translate: Get the meaning in the notification bar without opening U-Dictionary. Offline Dictionary: 38 Languages and English sample sentences, Collins Advanced Dictionary and WordNet Dictionary. Word for Today：Learning a new word every day. Small Installation Package: Less than 5MB. 14 Display Languages: Now you can read in your native language. Perfect English Pronunciation: Authentic UK (British) and US (American) accent. Listen and Learn. Roman Script: U-Dictionary provides Roman script for basic definition to help you pronounce. Sample Sentences: Collected from famous international news websites such as BBC, NPR, etc. Speech Recognition: Speak the word and get the meaning. My Words: Save important words into different folders. English Articles: Useful Articles to read on the home page. Desktop/Mobile Web: http://www.u-dictionary.com Articles from Blog: http://udictionaryblog.wordpress.com U-Dictionary provides Online Dictionary & Offline Dictionary in following 38 languages: English Arabic Dictionary, English Bangla Dictionary, English Chinese Simplified Dictionary, English Chinese Traditional Dictionary, English Dictionary, English Filipino Dictionary, English French Dictionary, English German Dictionary, English Gujarati Dictionary, English Hausa Dictionary, English Hindi Dictionary, English Igbo Dictionary, English Indonesian Dictionary, English Italian Dictionary, English Japanese Dictionary, English Kannada Dictionary. English Khmer Dictionary, English Korean Dictionary, English Lao Dictionary, English Malay Dictionary, English Malayalam Dictionary, English Marathi Dictionary, English Nepali Dictionary, English Pashto Dictionary, English Portuguese Dictionary, English Punjabi Dictionary, English Russian Dictionary, English Sindhi Dictionary, English Spanish Dictionary, English Swahili Dictionary, English Tamil Dictionary, English Telugu Dictionary, English Thai Dictionary, English Turkish Dictionary, English Urdu Dictionary, English Vietnamese Dictionary & English Yoruba Dictionary. english, hindi, translate, u-dictionary, how to translate from english to hindi, best app to translate hindi to english, best app to translate english to hindi, hindi english translator, english to hindi, hindi to english, u dictionary, google, dictionary, google translate app, translation, translate english to hindi, hindi to english translation, english to hindi dictionary, hindi dictionary, billi 4 you, billi4you, how to, gujjar, tamil, indian, urdu, telugu, best dictionary, prasadtechintelugu, udictionary, telugu dictionary, prasad devarakonda, prasad, translator, app, how to improve english, best english learning techniques, best android dictionary, best translator, google translation,u-dictionary, tech in kannada, best app to learn english, ಕನ್ನಡ, how to translate from english to kannada, android kannada, kannada, english to kannada, tech, learning, learning;, english, english to kannada -, learn kannada through tamil, learn kannada in 30 days, kannada to english learning, kannada language learning, learn kannada online, learn kannada through english, kannada learning, app, kannada language learning through english, best app for t, kannada technology video, spoken;, course;, spoken, conversation;, online, learn english through kannada, language;, kannada;, school;, language, klls;, kannada learning online, app;, klls, kannada help, kk tv kannada
Views: 57503 KVM CREATION
Facebook CEO Mark Zuckerberg faces another day of grilling on Capitol Hill Wednesday as he testifies before the House Energy & Commerce committee. Zuckerberg slogged through more than five hours of questioning Tuesday in front of senators, deflecting numerous questions for follow-up by his team at a later date. Follow CBS News' live blog: https://www.cbsnews.com/live-news/mark-zuckerberg-testimony-to-house-commerce-committee-live-updates/ If yesterday's Senate hearing is anything to go by, some interesting threads remain to pull. Senator Maria Cantwell started to prod at Zuckerberg's understanding, or lack thereof, of the vast space that is commercial data gathering for corporate intelligence. Subscribe to the CBS News Channel HERE: http://youtube.com/cbsnews Watch CBSN live HERE: http://cbsn.ws/1PlLpZ7 Follow CBS News on Instagram HERE: https://www.instagram.com/cbsnews/ Like CBS News on Facebook HERE: http://facebook.com/cbsnews Follow CBS News on Twitter HERE: http://twitter.com/cbsnews Get the latest news and best in original reporting from CBS News delivered to your inbox. Subscribe to newsletters HERE: http://cbsn.ws/1RqHw7T Get your news on the go! Download CBS News mobile apps HERE: http://cbsn.ws/1Xb1WC8 Get new episodes of shows you love across devices the next day, stream CBSN and local news live, and watch full seasons of CBS fan favorites like Star Trek Discovery anytime, anywhere with CBS All Access. Try it free! http://bit.ly/1OQA29B --- CBSN is the first digital streaming news network that will allow Internet-connected consumers to watch live, anchored news coverage on their connected TV and other devices. At launch, the network is available 24/7 and makes all of the resources of CBS News available directly on digital platforms with live, anchored coverage 15 hours each weekday. CBSN. Always On.
Views: 50276 CBS News
Lourdes was only 18 when she says her world came crashing down around her. She had been working as a maid from the age of 14, contributing what little money she earned to her struggling mother and siblings. Then she found out she was pregnant. She was alone, cast aside by her family and the father of her child. When her son, Victor, was born, Lourdes pieced together work, trying to keep their little family of 2 afloat. She decided to start her own business selling empanadas and snacks. That’s when she took out her first business loan of just over $60 U.S. She set strict rules for herself to invest in her business and build savings. She grew the business and continued to take and repay bigger and bigger loans, eventually receiving a $975 loan funded on Kiva by 33 lenders located all over the world, from Norway to Australia. Lourdes used that money to buy more stock and a refrigerator, building the business to the point that she was able to move into a new, bigger shop. This one had a secure gate to prevent robberies and an attached home for her family. Why we do what we do at Kiva: We envision a world where all people - even in the most remote areas of the globe - hold the power to create opportunity for themselves and others. We believe providing safe, affordable access to capital to those in need helps people create better lives for themselves and their families. How we do it: Making a loan on Kiva is so simple that you may not realize how much work goes on behind the scenes. Kiva works with microfinance institutions on five continents to provide loans to people without access to traditional banking systems. One hundred percent of your loan is sent to these microfinance institutions, which we call Field Partners, who administer the loans in the field. Kiva relies on a world wide network of over 450 volunteers who work with our Field Partners, edit and translate borrower stories, and ensure the smooth operation of countless other Kiva programs. 100% of every dollar you lend on Kiva goes directly towards funding loans; Kiva does not take a cut. Furthermore, Kiva does not charge interest to our Field Partners, who administer the loans. Kiva is primarily funded through the support of lenders making optional donations. We also raise funds through grants, corporate sponsors, and foundations. We are incredibly thankful for the support that has enabled us to do the work that has touched the lives of so many people. http://www.kiva.org/lend https://www.facebook.com/kiva https://twitter.com/kiva https://medium.com/@Kiva/lourdes-dream-to-show-the-world-a-woman-s-worth-978664cb596#.o41q6mh5l Voiceover by Martita: https://www.thevoicecrew.com/mycrewfile.php?id=1012
Views: 24909 Kiva
Taking The Open University‘s Course A305 as a starting point, this panel discussion will examine and interrogate experimental, open, and technological possibilities for the future of architecture education. Participants include Tim Benton, Lisa Haber-Thomson, K. Michael Hays, John May, and Mirko Zardini.
Views: 846 Harvard GSD
The founder of 5rights a civil society initiative to make the digital world a more transparent empowering place for young people under 18 proposes a framework of digital rights. Baroness Kidron tells her story through animations about George, a girl in middle school, and The right to know - The right to safety and support - The right to informed and conscious use – and The right to digital literacy will resonate with you (whether or not you have teenagers in the house) for a long time to come. Baroness Beeban Kidron is a filmmaker, activist, documentarian and co-founder of the educational charity Into Film. Since 2012, she is a lifetime peer of the UK House of Lords where she sits as a Crossbench Peer. Baroness Beeban Kidron is a filmmaker, activist, documentarian and co-founder of the educational charity Into Film. Since 2012, she is a lifetime peer of the UK House of Lords where she sits as a Crossbench Peer. 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: 5104 TEDx Talks
Cat lovers have no doubt seen it before: the intense focus, followed by a claws-extended, acrobatic leap, some serious hang time and a not-always-graceful landing. Watching a cat get airborne is always exhilarating, but even more so when it weighs 350 pounds. At the Oregon Zoo, a new enrichment game is helping to keep the African lions fit while bringing out their explosive predatory instincts. Keepers have been using a mega version of this standard pet store item — constructed out of butcher paper and dubbed the “Leaping Lion Toy” — to mentally stimulate the big cats while encouraging natural hunting behaviors. Story: http://www.oregonzoo.org/news/2016/05/leaping-lions-zoos-mega-cat-toy-has-whole-pride-pouncing
Views: 433324 Oregon Zoo