Data mining recently made big news with the Cambridge Analytica scandal, but it is not just for ads and politics. It can help doctors spot fatal infections and it can even predict massacres in the Congo. Hosted by: Stefan Chin Head to https://scishowfinds.com/ for hand selected artifacts of the universe! ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters: Lazarus G, Sam Lutfi, Nicholas Smith, D.A. Noe, سلطان الخليفي, Piya Shedden, KatieMarie Magnone, Scott Satovsky Jr, Charles Southerland, Patrick D. Ashmore, Tim Curwick, charles george, Kevin Bealer, Chris Peters ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://www.aaai.org/ojs/index.php/aimagazine/article/viewArticle/1230 https://www.theregister.co.uk/2006/08/15/beer_diapers/ https://www.theatlantic.com/technology/archive/2012/04/everything-you-wanted-to-know-about-data-mining-but-were-afraid-to-ask/255388/ https://www.economist.com/node/15557465 https://blogs.scientificamerican.com/guest-blog/9-bizarre-and-surprising-insights-from-data-science/ https://qz.com/584287/data-scientists-keep-forgetting-the-one-rule-every-researcher-should-know-by-heart/ https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853 http://dml.cs.byu.edu/~cgc/docs/mldm_tools/Reading/DMSuccessStories.html http://content.time.com/time/magazine/article/0,9171,2058205,00.html https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all&_r=0 https://www2.deloitte.com/content/dam/Deloitte/de/Documents/deloitte-analytics/Deloitte_Predictive-Maintenance_PositionPaper.pdf https://www.cs.helsinki.fi/u/htoivone/pubs/advances.pdf http://cecs.louisville.edu/datamining/PDF/0471228524.pdf https://bits.blogs.nytimes.com/2012/03/28/bizarre-insights-from-big-data https://scholar.harvard.edu/files/todd_rogers/files/political_campaigns_and_big_data_0.pdf https://insights.spotify.com/us/2015/09/30/50-strangest-genre-names/ https://www.theguardian.com/news/2005/jan/12/food.foodanddrink1 https://adexchanger.com/data-exchanges/real-world-data-science-how-ebay-and-placed-put-theory-into-practice/ https://www.theverge.com/2015/9/30/9416579/spotify-discover-weekly-online-music-curation-interview http://blog.galvanize.com/spotify-discover-weekly-data-science/ Audio Source: https://freesound.org/people/makosan/sounds/135191/ Image Source: https://commons.wikimedia.org/wiki/File:Swiss_average.png
Views: 141869 SciShow
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
Views: 1661 Audiopedia
For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html This video is about bioinformatics databases like NCBI, ENSEMBL, ClustalW, Swisprot, SIB, DDBJ, EMBL, PDB, CATH, SCOPE etc. Bioinformatics Listeni/ˌbaɪ.oʊˌɪnfərˈmætɪks/ is an interdisciplinary field that develops and improves on methods for storing, retrieving, organizing and analyzing biological data. A major activity in bioinformatics is to develop software tools to generate useful biological knowledge. Bioinformatics uses many areas of computer science, mathematics and engineering to process biological data. Complex machines are used to read in biological data at a much faster rate than before. Databases and information systems are used to store and organize biological data. Analyzing biological data may involve algorithms in artificial intelligence, soft computing, data mining, image processing, and simulation. The algorithms in turn depend on theoretical foundations such as discrete mathematics, control theory, system theory, information theory, and statistics. Commonly used software tools and technologies in the field include Java, C#, XML, Perl, C, C++, Python, R, SQL, CUDA, MATLAB, and spreadsheet applications. In order to study how normal cellular activities are altered in different disease states, the biological data must be combined to form a comprehensive picture of these activities. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This includes nucleotide and amino acid sequences, protein domains, and protein structures. The actual process of analyzing and interpreting data is referred to as computational biology. Important sub-disciplines within bioinformatics and computational biology include: the development and implementation of tools that enable efficient access to, use and management of, various types of information. the development of new algorithms (mathematical formulas) and statistics with which to assess relationships among members of large data sets. For example, methods to locate a gene within a sequence, predict protein structure and/or function, and cluster protein sequences into families of related sequences. The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches, however, is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: pattern recognition, data mining, machine learning algorithms, and visualization. Major research efforts in the field include sequence alignment, gene finding, genome assembly, drug design, drug discovery, protein structure alignment, protein structure prediction, prediction of gene expression and protein--protein interactions, genome-wide association studies, and the modeling of evolution. Bioinformatics now entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data. Over the past few decades rapid developments in genomic and other molecular research technologies and developments in information technologies have combined to produce a tremendous amount of information related to molecular biology. Bioinformatics is the name given to these mathematical and computing approaches used to glean understanding of biological processes. Source of the article published in description is Wikipedia. I am sharing their material. Copyright by original content developers of Wikipedia. Link- http://en.wikipedia.org/wiki/Main_Page
Views: 89300 Shomu's Biology
What is Data Mining? How is it different from Statistics? This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining. It is part of a series of 37 videos, all of which are available on YouTube. For more information: http://www.dataminingbook.com https://www.twitter.com/gshmueli https://www.facebook.com/dataminingbook Here is the complete list of the videos: • Welcome to Business Analytics Using Data Mining (BADM) • BADM 1.1: Data Mining Applications • BADM 1.2: Data Mining in a Nutshell • BADM 1.3: The Holdout Set • BADM 2.1: Data Visualization • BADM 2.2: Data Preparation • BADM 3.1: PCA Part 1 • BADM 3.2: PCA Part 2 • BADM 3.3: Dimension Reduction Approaches • BADM 4.1: Linear Regression for Descriptive Modeling Part 1 • BADM 4.2 Linear Regression for Descriptive Modeling Part 2 • BADM 4.3 Linear Regression for Prediction Part 1 • BADM 4.4 Linear Regression for Prediction Part 2 • BADM 5.1 Clustering Examples • BADM 5.2 Hierarchical Clustering Part 1 • BADM 5.3 Hierarchical Clustering Part 2 • BADM 5.4 K-Means Clustering • BADM 6.1 Classification Goals • BADM 6.2 Classification Performance Part 1: The Naive Rule • BADM 6.3 Classification Performance Part 2 • BADM 6.4 Classification Performance Part 3 • BADM 7.1 K-Nearest Neighbors • BADM 7.2 Naive Bayes • BADM 8.1 Classification and Regression Trees Part 1 • BADM 8.2 Classification and Regression Trees Part 2 • BADM 8.3 Classification and Regression Trees Part 3 • BADM 9.1 Logistic Regression for Profiling • BADM 9.2 Logistic Regression for Classification • BADM 10 Multi-Class Classification • BADM 11 Ensembles • BADM 12.1 Association Rules Part 1 • BADM 12.2 Association Rules Part 2 • Neural Networks: Part I • Neural Networks: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 1064 Galit Shmueli
This course aims to introduce advanced database concepts such as data warehousing, data mining techniques, clustering, classifications and its real time applications. SlideTalk video created by SlideTalk at http://slidetalk.net, the online solution to convert powerpoint to video with automatic voice over.
Views: 3830 SlideTalk
Social media data is hot stuff—but it sure can be tricky to understand. In this session, Michelle from Tableau's social media team will share how they analyze social media data from multiple sources. We'll compare methods for collecting data, and discuss tips for ensuring that it answers new questions as they arise. Whether you're new to social media analysis or have already started diving into your data, this session will provide key tips, tricks, and examples to help you achieve your goals.
Views: 11740 Tableau Software
Make sure to like & comment if you liked this video! Take Hank's course here: https://www.datacamp.com/courses/unsupervised-learning-in-r Many times in machine learning, the goal is to find patterns in data without trying to make predictions. This is called unsupervised learning. One common use case of unsupervised learning is grouping consumers based on demographics and purchasing history to deploy targeted marketing campaigns. Another example is wanting to describe the unmeasured factors that most influence crime differences between cities. This course provides a basic introduction to clustering and dimensionality reduction in R from a machine learning perspective, so that you can get from data to insights as quickly as possible. Transcript: Hi! I'm Hank Roark, I'm a long-time data scientist and user of the R language, and I'll be your instructor for this course on unsupervised learning in R. In this first chapter I will define ‘unsupervised learning’, provide an overview of the three major types of machine learning, and you will learn how to execute one particular type of unsupervised learning using R. There are three major types of machine learning. The first type is unsupervised learning. The goal of unsupervised learning is to find structure in unlabeled data. Unlabeled data is data without a target, without labeled responses. Contrast this with supervised learning. Supervised learning is used when you want to make predictions on labeled data, on data with a target. Types of predictions include regression, or predicting how much of something there is or could be, and classification which is predicting what type or class some thing is or could be. The final type is reinforcement learning, where a computer learns from feedback by operating in a real or synthetic environment. Here is a quick example of the difference between labeled and unlabeled data. The table on the left is an example with three observations about shapes, each shape with three features, represented by the three columns. This table, the one on the left is an example of unlabeled data. If an additional vector of labels is added, like the column of labels on the right hand side, labeling each observation as belonging to one of two groups, then we would have labeled data. Within unsupervised learning there are two major goals. The first goal is to find homogeneous subgroups within a population. As an example let us pretend we have a population of six people. Each member of this population might have some attributes, or features — some examples of features for a person might be annual income, educational attainment, and gender. With those three features one might find there are two homogeneous subgroups, or groups where the members are similar by some measure of similarity. Once the members of each group are found, we might label one group subgroup A and the other subgroup B. The process of finding homogeneous subgroups is referred to as clustering. There are many possible applications of clustering. One use case is segmenting a market of consumers or potential consumers. This is commonly done by finding groups, or clusters, of consumers based on demographic features and purchasing history. Another example of clustering would be to find groups of movies based on features of each movie and the reviews of the movies. One might do this to find movies most like another movie. The second goal of unsupervised learning is to find patterns in the features of the data. One way to do this is through ‘dimensionality reduction’. Dimensionality reduction is a method to decrease the number of features to describe an observation while maintaining the maximum information content under the constraints of lower dimensionality. Dimensionality reduction is often used to achieve two goals, in addition to finding patterns in the features of the data. Dimensionality reduction allows one to visually represent high dimensional data while maintaining much of the data variability. This is done because visually representing and understanding data with more than 3 or 4 features can be difficult for both the producer and consumer of the visualization. The third major reason for dimensionality reduction is as a preprocessing step for supervised learning. More on this usage will be covered later. Finally a few words about the challenges and benefits typical in performing unsupervised learning. In unsupervised learning there is often no single goal of the analysis. This can be presented as someone asking you, the analyst, “to find some patterns in the data.” With that challenge, unsupervised learning often demands and brings out the deep creativity of the analyst. Finally, there is much more unlabeled data than labeled data. This means there are more opportunities to apply unsupervised learning in your work. Now it's your turn to practice what you've learned.
Views: 2205 DataCamp
CAREERS IN DATA ANALYTICS - Salary , Job Positions , Top Recruiters What IS DATA ANALYTICS? Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses. As a term, data analytics predominantly refers to an assortment of applications, from basic business intelligence (BI), reporting and online analytical processing (OLAP) to various forms of advanced analytics. In that sense, it's similar in nature to business analytics, another umbrella term for approaches to analyzing data -- with the difference that the latter is oriented to business uses, while data analytics has a broader focus. The expansive view of the term isn't universal, though: In some cases, people use data analytics specifically to mean advanced analytics, treating BI as a separate category. Data analytics initiatives can help businesses increase revenues, improve operational efficiency, optimize marketing campaigns and customer service efforts, respond more quickly to emerging market trends and gain a competitive edge over rivals -- all with the ultimate goal of boosting business performance. Depending on the particular application, the data that's analyzed can consist of either historical records or new information that has been processed for real-time analytics uses. In addition, it can come from a mix of internal systems and external data sources. Types of data analytics applications : At a high level, data analytics methodologies include exploratory data analysis (EDA), which aims to find patterns and relationships in data, and confirmatory data analysis (CDA), which applies statistical techniques to determine whether hypotheses about a data set are true or false. EDA is often compared to detective work, while CDA is akin to the work of a judge or jury during a court trial -- a distinction first drawn by statistician John W. Tukey in his 1977 book Exploratory Data Analysis. Data analytics can also be separated into quantitative data analysis and qualitative data analysis. The former involves analysis of numerical data with quantifiable variables that can be compared or measured statistically. The qualitative approach is more interpretive -- it focuses on understanding the content of non-numerical data like text, images, audio and video, including common phrases, themes and points of view. At the application level, BI and reporting provides business executives and other corporate workers with actionable information about key performance indicators, business operations, customers and more. In the past, data queries and reports typically were created for end users by BI developers working in IT or for a centralized BI team; now, organizations increasingly use self-service BI tools that let execs, business analysts and operational workers run their own ad hoc queries and build reports themselves. Keywords: being a data analyst, big data analyst, business analyst data warehouse, data analyst, data analyst accenture, data analyst accenture philippines, data analyst and data scientist, data analyst aptitude questions, data analyst at cognizant, data analyst at google, data analyst at&t, data analyst australia, data analyst basics, data analyst behavioral interview questions, data analyst business, data analyst career, data analyst career path, data analyst career progression, data analyst case study interview, data analyst certification, data analyst course, data analyst in hindi, data analyst in india, data analyst interview, data analyst interview questions, data analyst job, data analyst resume, data analyst roles and responsibilities, data analyst salary, data analyst skills, data analyst training, data analyst tutorial, data analyst vs business analyst, data mapping business analyst, global data analyst bloomberg, market data analyst bloomberg
Views: 25552 THE MIND HEALING
Machine Learning Machine learning is a subfield of computer science (CS) and artificial intelligence (AI) that deals with the construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions. Besides CS and AI, it has strong ties to statistics and optimization, which deliver both methods and theory to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning, data mining, and pattern recognition are sometimes conflated. Machine learning tasks can be of several forms. In supervised learning, the computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs. Spam filtering is an example of supervised learning. In unsupervised learning, no labels are given to the learning algorithm, leaving it on its own to groups of similar inputs (clustering), density estimates orprojections of high-dimensional data that can be visualised effectively. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end. Topic modeling is an example of unsupervised learning, where a program is given a list of human language documents and is tasked to find out which documents cover similar topics. In reinforcement learning, a computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal or not. Definition In 1959, Arthur Samuel defined machine learning as a “Field of study that gives computers the ability to learn without being explicitly programmed”. Tom M. Mitchell provided a widely quoted, more formal definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E”. This definition is notable for its defining machine learning in fundamentally operational rather than cognitive terms, thus following Alan Turing's proposal in Turing's paper “Computing Machinery and Intelligence” that the question “Can machines think?” be replaced with the question “Can machines do what we (as thinking entities) can do?” Generalization: A core objective of a learner is to generalize from its experience. Generalization in this context is the ability of a learning machine to perform accurately on new, unseen tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases. These two terms are commonly confused, as they often employ the same methods and overlap significantly. They can be roughly defined as follows: 1. Machine learning focuses on prediction, based on known properties learned from the training data. 2. Data Mining focuses on the discovery of (previously)unknown properties in the data. This is the analysis step of Knowledge Discovery in Databases. The two areas overlap in many ways: data mining uses many machine learning methods, but often with a slightly different goal in mind. On the other hand, machine learning also employs data mining methods as “unsupervised learning” or as a preprocessing step to improve learner accuracy. Human Interaction Some machine learning systems attempt to eliminate the need for human intuition in data analysis, while others adopt a collaborative approach between human and machine
Views: 23156 sangram singh
Data mining, the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as groups of data records, is a powerful tool in fundraising. This session will focus on implementing data mining and using the results. Two organizations, the University of Puget Sound and M.D. Anderson Cancer Center, will describe their use of data mining to identify prospects for specific fund raising initiatives. The University of Puget Sound developed an alumni attachment score to gauge which constituents are most closely connected to the University in an effort to identify the next tier of alumni with whom engagement efforts should be focused. Additionally, a planned giving ranking score is used to identify potential planned giving donors. M.D. Anderson Cancer Center launched the Moon Shot Program in 2012 with the goal of translating scientific discoveries into better patient care -- faster -- by using innovative technology, setting ambitious goals and transforming our approach to end cancer once and for all. The Center mined existing data to align prospects to the appropriate areas for funding of the Moon Shot Program. Jill Steward Senior Product Manager, Abila Jill Steward is the Senior Product Manager with Abila responsible for the strategic direction of the enterprise level fund raising product Millennium. For over fifteen years, Jill has worked with Millennium software in report writing, training, implementation, product direction and as the customer ombudsman. Nancy Penner Manager, Systems Analyst Services, MD Anderson Cancer Center Nancy is Manager, Systems Analyst Services, The University of Texas MD Anderson Cancer Center. Nancy has been responsible for the management of the Millennium fund-raising software, data integrations, reporting and analytics for MD Anderson?s Development Office since 2001. The office is a major-gift oriented office that raises $200 million annually and growing. Under Nancy?s direction the systems solutions for the Development Office have expanded beyond the core Millennium application to include the use of Oversight?s continuous controls monitoring system for improved data integration and data quality. Sean Vincent Director of University Relations Information Services, University of Puget Sound Sean Vincent has served as the Director of University Relations Information Services at the University of Puget Sound in Tacoma, WA for the past thirteen years. Sean's prior roles at Puget Sound included Director of Annual Giving and Major Gifts Officer.
Views: 148 The DRIVE/conference
Once your smart devices can talk to you, who else are they talking to? Kashmir Hill and Surya Mattu wanted to find out -- so they outfitted Hill's apartment with 18 different internet-connected devices and built a special router to track how often they contacted their servers and see what they were reporting back. The results were surprising -- and more than a little bit creepy. Learn more about what the data from your smart devices reveals about your sleep schedule, TV binges and even your tooth-brushing habits -- and how tech companies could use it to target and profile you. (This talk contains mature language.) Check out more TED Talks: http://www.ted.com The TED Talks channel features the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes (or less). Look for talks on Technology, Entertainment and Design -- plus science, business, global issues, the arts and more. Follow TED on Twitter: http://www.twitter.com/TEDTalks Like TED on Facebook: https://www.facebook.com/TED Subscribe to our channel: https://www.youtube.com/TED
Views: 129618 TED
Dynamic Data Assimilation: an introduction by Prof S. Lakshmivarahan,School of Computer Science,University of Oklahoma.For more details on NPTEL visit http://nptel.ac.in
Views: 1809 nptelhrd
Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning. See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/
Views: 11499 Microsoft Research
This video is part of an online course being taught at the University of California, "ICS 5: Global Disruption and Information Technology". Only a portion of the course material is accessible via YouTube. Course Description: The world is changing rapidly. Environmental concerns, social transformations, and economic uncertainties are pervasive. However, certain human needs remain relatively constant—things like nutritious food, clean water, secure shelter, and close human social contact. This course seeks to understand how sociotechnical systems (that is, collections of people and information technologies) may support a transition to a sustainable civilization that allows for human needs and wants to be met in the face of global change. In this course, students will learn about how information technology works, and how humans and information technology interact. In addition it will provide students with a structured opportunity to interrogate what is important to them in life, and how communities and technologies can support those aspects of their lives. Topics covered will include: introductions to information technology, the science behind global change, and scientific studies of human wellbeing, and a range of topical discussions such as IT for local food production, computational systems to support resource sharing, resilient currency technologies, and localized, low-energy technological infrastructure.
Views: 9244 djp3
Author: Susan Athey Abstract: A large literature on causal inference in statistics, econometrics, biostatistics, and epidemiology (see, e.g., Imbens and Rubin  for a recent survey) has focused on methods for statistical estimation and inference in a setting where the researcher wishes to answer a question about the (counterfactual) impact of a change in a policy, or ""treatment"" in the terminology of the literature. The policy change has not necessarily been observed before, or may have been observed only for a subset of the population; examples include a change in minimum wage law or a change in a firm's price. The goal is then to estimate the impact of small set of ""treatments"" using data from randomized experiments or, more commonly, ""observational"" studies (that is, non-experimental data). The literature identifies a variety of assumptions that, when satisfied, allow the researcher to draw the same types of conclusions that would be available from a randomized experiment. To estimate causal effects given non-random assignment of individuals to alternative policies in observational studies, popular techniques include propensity score weighting, matching, and regression analysis; all of these methods adjust for differences in observed attributes of individuals. Another strand of literature in econometrics, referred to as ""structural modeling,"" fully specifies the preferences of actors as well as a behavioral model, and estimates those parameters from data (for applications to auction-based electronic commerce, see Athey and Haile  and Athey and Nekipelov ). In both cases, parameter estimates are interpreted as ""causal,"" and they are used to make predictions about the effect of policy changes. In contrast, the supervised machine learning literature has traditionally focused on prediction, providing data-driven approaches to building rich models and relying on cross-validation as a powerful tool for model selection. These methods have been highly successful in practice. This talk will review several recent papers that attempt to bring the tools of supervised machine learning to bear on the problem of policy evaluation, where the papers are connected by three themes. The first theme is that it important for both estimation and inference to distinguish between parts of the model that relate to the causal question of interest, and ""attributes,"" that is, features or variables that describe attributes of individual units that are held fixed when policies change. Specifically, we propose to divide the features of a model into causal features, whose values may be manipulated in a counterfactual policy environment, and attributes. A second theme is that relative to conventional tools from the policy evaluation literature, tools from supervised machine learning can be particularly effective at modeling the association of outcomes with attributes, as well as in modeling how causal effects vary with attributes. A final theme is that modifications of existing methods may be required to deal with the ""fundamental problem of causal inference,"" namely, that no unit is observed in multiple counterfactual worlds at the same time: we do not see a patient at the same time with and without medication, and we do not see a consumer at the same moment exposed to two different prices. This creates a substantial challenge for cross-validation, as the ground truth for the causal effect is not observed for any individual. ACM DL: http://dl.acm.org/citation.cfm?id=2785466 DOI: http://dx.doi.org/10.1145/2783258.2785466
Views: 3166 Association for Computing Machinery (ACM)
Facebook CEO Mark Zuckerberg will testify today before a U.S. congressional hearing about the use of Facebook data to target voters in the 2016 election. Zuckerberg is expected to offer a public apology after revelations that Cambridge Analytica, a data-mining firm affiliated with Donald Trump's presidential campaign, gathered personal information about 87 million users to try to influence elections. »»» 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: 130489 CBC News
Coding with Python - Automate Social - Grab Social Data with Python - Part 1 Coding for Python is a series of videos designed to help you better understand how to use python. In this video we discover a API that will help us grab social data (twitter, facebook, linkedin) using just a person's email address. API - FullContact.com Django is awesome and very simple to get started. Step-by-step tutorials are to help you understand the workflow, get you started doing something real, then it is our goal to have you asking questions... "Why did I do X?" or "How would I do Y?" These are questions you wouldn't know to ask otherwise. Questions, after all, lead to answers. View all my videos: http://bit.ly/1a4Ienh Get Free Stuff with our Newsletter: http://eepurl.com/NmMcr The Coding For Entrepreneurs newsletter and get free deals on premium Django tutorial classes, coding for entrepreneurs courses, web hosting, marketing, and more. Oh yeah, it's free: A few ways to learn: Coding For Entrepreneurs: https://codingforentrepreneurs.com (includes free projects and free setup guides. All premium content is just $25/mo). Includes implementing Twitter Bootstrap 3, Stripe.com, django south, pip, django registration, virtual environments, deployment, basic jquery, ajax, and much more. On Udemy: Bestselling Udemy Coding for Entrepreneurs Course: https://www.udemy.com/coding-for-entrepreneurs/?couponCode=youtubecfe49 (reg $99, this link $49) MatchMaker and Geolocator Course: https://www.udemy.com/coding-for-entrepreneurs-matchmaker-geolocator/?couponCode=youtubecfe39 (advanced course, reg $75, this link: $39) Marketplace & Dail Deals Course: https://www.udemy.com/coding-for-entrepreneurs-marketplace-daily-deals/?couponCode=youtubecfe39 (advanced course, reg $75, this link: $39) Free Udemy Course (40k+ students): https://www.udemy.com/coding-for-entrepreneurs-basic/ Fun Fact! This Course was Funded on Kickstarter: http://www.kickstarter.com/projects/jmitchel3/coding-for-entrepreneurs
Views: 46737 CodingEntrepreneurs
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: 111 Info HPCNow!
Exciting Video ~ How to calculate your own NBA home court advantage. Which teams have the biggest home court advantage / road disadvantage ? 12/8/18 Methodology includes stats, injuries, matchups, and trends. Looking for the Edge. Data mining to find the best matchups. Major bullet points and X-factors that lead to sports forecasting predictions. Top Picks NBA Parlay. Sports Betting Strategies Strategy Tips. HD high definition. This video may be of interest to sports fans, draftkings, fanduel, vegas, etc. Let's share our love of sports in a friendly way. Please Subscribe. Here are some resources: https://www.usatoday.com/sports/nba/sagarin/ http://homepage.divms.uiowa.edu/~dzimmer/sports-statistics/HCAinbasketball.pdf https://www.boydsbets.com/nba-home-court-advantage/ ESPN http://www.espn.com/nba/scoreboard NBA Atlanta Hawks Boston Celtics Brooklyn Nets Charlotte Hornets Chicago Bulls Cleveland Cavaliers Dallas Mavericks Denver Nuggets Detroit Pistons Golden State Warriors Houston Rockets Indiana Pacers LA Clippers Los Angeles Lakers Memphis Grizzlies Miami Heat Milwaukee Bucks Minnesota Timberwolves New Orleans Pelicans New York Knicks Oklahoma City Thunder Orlando Magic Philadelphia 76ers Phoenix Suns Portland Trail Blazers Sacramento Kings San Antonio Spurs Toronto Raptors Utah Jazz Washington Wizards LeBron
Views: 177 FanMD
Fortnite Battle Royale NEW Leaked Skins (Fortnite leaked skins): In today's video I show you some Fortnite Battle Royale leaked skins and customization items from datamining. These Fortnite Battle Royale new skins include some Fortnite Battle Royale legendary skins and more items! I hope you enjoy seeing these Fortnite new skins Donate: https://youtube.streamlabs.com/axrorayt Thumbnail Image By: Battlefront Captures Channel: https://www.youtube.com/channel/UCA1oEnRYKBmFDAcoKso2pAA Music by: Ben Sounds Website: http://www.bensound.com/royalty-free-music/2 Thank you all for the Subscribers, Comments & Likes you guys are amazing. See you in the next video!
Views: 680 Axrora
Microsoft Excel, this list covers all the basics you need to start entering your data and building organized workbooks Main Play list : http://goo.gl/O5tsH2 (70+ Video) Subscribe Now : http://goo.gl/2kzV8M Topics include: 1. What is Excel and what is it used for? 2. Using the menus 3. Working with dates and times 4. Creating simple formulas 5. Formatting fonts, row and column sizes, borders, and more 6. Inserting shapes, arrows, and other graphics 7. Adding and deleting rows and columns 8. Hiding data 9. Moving, copying, and pasting 10. Sorting and filtering data 11. Securing your workbooks 12. Tracking changes
Views: 424 tutorbeta
fuzzy logic in artificial intelligence in hindi | fuzzy logic example | #28 Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning. The approach of FL imitates the way of decision making in humans that involves all intermediate possibilities between digital values YES and NO. The conventional logic block that a computer can understand takes precise input and produces a definite output as TRUE or FALSE, which is equivalent to human’s YES or NO. The inventor of fuzzy logic, Lotfi Zadeh, observed that unlike computers, the human decision making includes a range of possibilities between YES and NO, such as − CERTAINLY YES POSSIBLY YES CANNOT SAY POSSIBLY NO CERTAINLY NO well,academy,Fuzzy logic in hindi,fuzzy logic in artificial intelligence in hindi,artificial intelligence fuzzy logic,fuzzy logic example,fuzzy logic in artificial intelligence,fuzzy logic with example,fuzzy logic in artificial intelligence in hindi with exapmle,fuzzy logic,what is fuzzy logic in hindi,what is fuzzy logic with example,introduction to fuzzy logic
Views: 118259 Well Academy
by Matt Wolff & Brian Wallace & Xuan Zhao Machine learning techniques have been gaining significant traction in a variety of industries in recent years, and the security industry is no exception to it's influence. These techniques, when applied correctly, can help assist in many data driven tasks to provide interesting insights and decision recommendations to analyst. While these techniques can be powerful, for the researchers and analyst who are not well versed in machine learning, there can exist a gap in understanding that may prevent them from looking at and applying these tools to problems machine learning techniques could assist with. The goal of this presentation is to help researchers, analyst, and security enthusiast get their hands dirty applying machine learning to security problems. We will walk the entire pipeline from idea to functioning tool on several diverse security related problems, including offensive and defensive use cases for machine learning. Through these examples and demonstrations, we will be able to explain in a very concrete fashion every step involved to tie in machine learning to the specified problem. In addition, we will be releasing every tool built, along with source code and related datasets, to enable those in attendance to reproduce the research and examples on their own. Machine learning based tools that will be released with this talk include an advanced obfuscation tool for data exfiltration, a network mapper, and command and control panel identification module.
Views: 5807 Black Hat
People may have forgotten about the #DeleteFacebook campaign, but data science professionals learned their biggest lessons from the incident. Here are three things to remember when dealing with data: http://bit.ly/2IMUGM4 Christopher Wylie made an incredible revelation last week that shook the world of data science and social media. The revelation? Cambridge Analytica, a data analytics firm that worked for the election campaign of Donald Trump accessed data from millions of Facebook profiles in the US, resulting in one of the biggest data breaches ever revealed. Using the personal information of these Facebook users, they allegedly built a software program that influenced the elections. As a big data analytics firm, Cambridge Analytica had some moral and ethical responsibilities to protect the data they harvested from the users. The breach negatively affected Facebook leading to the #DeleteFacebook Campaign. For a data scientist, the campaign can be looked as a learning curve and a lesson to define the ethical code of conduct. Three things a data scientist can learn from the campaign 1. With great power comes great responsibility Data is without a doubt the new world's power. Every organisation and industry has realised that the only way they can run their business effectively is through harnessing data and understanding specific patterns. The bigger the company, the larger and more complex the data they deal with. But with great data comes great responsibilities. If used correctly, this can revolutionize businesses. However, if misused, it would be a disaster for the business and for the trust between the company and its stakeholders. Citing an example of the recent #DeleteFacebook controversy, Cambridge Analytica had some ethical and moral responsibility to protect the data obtained. It included not creating software to influence and predict choices during elections. As a big data scientist, the most important aspect of your big data training should be ethics training. You must be consciously aware of your duties towards your employer, regulators and users who both provide and use your data. 2. Set clear data mining boundaries Data mining should be limited to collecting data, which is truly necessary for the organisation's growth. Irrelevant data only makes the data analysis process complicated and increases the risk of data breach. Having lots of data doesn't necessarily mean that you can process and synthesize all of it for the company's progress. If you can expertly create the model and give results by using only 100 data points, the data mining process should stop there. It is also vital that the data mined is aggregated to protect the private information and to encourage transparency within the organisation. The #DeleteFacebook campaign is the recent example of how a big company like Facebook, which collects the most private user information can be negatively affected by the incorrect use of data mining, even by third parties. Had the social media giant worked on the principle of minimal data collection, the data breach may have probably never taken place. 3. Always have a Plan B Every time you open your phone with an active internet connection, you give away a little information about yourself, which is used by applications and the websites. Following the ethical code, every company always tries to protect user data, primarily so that sensitive information is not exposed. However, there is no saying what can happen in the future. Even Facebook wasn’t aware that Cambridge Analytica was using its data unethically. When the news broke out, Facebook lost around $42 billion in valuation in a single day. As a data scientist tasked with user data, it is crucial that you have a Plan B in case of a data breach. Chart a data breach response plan to limit possible damage. Apart from having technical guidelines in place, you would need to involve operations, public relations and administration teams to help guide the company through the crisis. The plan must be run through simulations and made foolproof for every scenario. Arm yourself with a clear vision and goal, educate yourself on your responsibilities and authority as a data analyst and execute the plan to ensure that you achieve zero tolerance for data leakage.
Views: 115 Manipal ProLearn
Excerpts from the report below... contact us for the full report: Scientific misconduct is defined by federal government as fabrication, falsification, or plagiarism in proposing, performing, or reviewing research, or in reporting research results . Several high-profile cases of research fraud have shown how scientific misconduct not only hinders evolution of science and leads to abuse of research funds, but also can have consequences for patients and undermine public trust in research in general [3,11,13-16,19,20]. Nevertheless, past couple of decades has seen an increase in instances of fraud, which needs to be recognized and addressed.  Although scientific misconduct does not include honest error or differences of opinion,  a methodical review of over 2000 retracted biomedical and life-science research articles revealed that only about 21% of retractions were attributable to error, while, about 67% of retractions were attributable to misconduct, and that the percentage of scientific articles retracted because of scientific misconduct has increased by almost 10-folds since 1975.  A systematic meta-analysis from 2009 showed that about 2% of researchers admitted to fabricating, falsifying or modifying data at least once, while 33% admitted to other questionable research practices such as “data-mining” and “data-cooking”. ) .... Ensuring ethical laboratory and research practices should be on the mind of every researcher. Because successful collective progress in academia is built upon trust that everyone is contributing as accurate, high quality data as possible to the scientific discourse, it is not only important to science, but also in the researcher’s best interest to build and maintain a reputation as an honest, ethically responsible researcher. The one thing that most of our interviewees seemed to emphasize most is the importance of honesty- honesty at all stages of science from data acquiring and recording to analyzing and publishing. Honesty with human subjects, with advisors, with colleagues, and with readers of publications. Even though there will be pressure to generate data/publish and pressure to get positive results, most important is that you are ensuring your work represents a truth in nature- because research is ultimately about trying to find the truth. An important part of this is that a young researcher must strive to ensure that experiments are well thought out and discussed to ensure this honesty is achieved and, in ensuring that goal, relevant information should be accurately entered into their laboratory notebook. As, it’s not just about convenience and organization, but it’s rather unethical to mishandle data in a way that obscures its origination, whether intentional or unintentional. And most importantly, learning the best ways to approach science with the goal of finding the truth is a central part of the graduate learning experience and everyone in academia is responsible in different ways in ensuring this education is at the forefront. It should be no debate that the only good science is ethical science....
Views: 31 Class Action
Stryd, VeloPress, and Sansego assembled a panel of power meter experts to discuss the state of the art in using power meters for running and triathlon. See the ways a power meter can make you a stronger, faster runner and learn how to use a running power meter at www.runwithpower.net, which includes guides from RUN WITH POWER: The Complete Guide to Power Meters for Running by Jim Vance. The panelists included: * Dr. Andrew Coggan, exercise physiologist and pioneering researcher in the use of power meters * Jim Vance, TrainingBible coach and author of the book RUN WITH POWER: The Complete Guide to Power Meters for Running * Craig "Crowie" Alexander, 3-time Ironman World Champion and founder of Sansego coaching * Frank Jakobsen, Sansego coach * Jamie Williamson, co-founder of Stryd, the first wearable power meter for running The video of the full 45-minute panel discussion was led by Bob Babbitt and covered these topics: * The benefits of using a power meter for running and triathlon * The difficulties overcome in creating a running power meter * The major difference between cycling power and running power * How running power meters can help you develop more than one running technique to use at different speeds * How power meters for running are like a portable biomechanics laboratory * Power meters can be a training diagnostic tool, especially for long runs * How the running power meter lets runners train at the correct intensity * Power meters improve Training Stress Scores * Stryd can see the difference in training stress between running on treadmills and running on pavement. * How specialized brick workouts can zero in on your best running form off the bike * Envelope runs, a new way to train for more efficient run form * What's coming soon from Stryd * How power meters will revolutionize pacing on hilly courses and race pacing * Why runners should adopt power as soon as possible instead of waiting for the technology to mature * Which parts of the book RUN WITH POWER have been most helpful to readers * Self-tests and new running form and techniques to try * How a power meter is a useful tool even for runners who prefer to run by feel * How coaches can use a power meter to identify strengths and weaknesses in their athletes * How a power meter can help you find the best running shoes for you * Why power meters become more valuable as courses or conditions become more difficult * How Stryd is using data mining of user data * Where Stryd is headed to help runners improve efficiency For more on running power meters, please visit www.runwithpower.net.
Views: 6085 VeloPress
Title: Towards Decision Support and Goal Achievement: Identifying Action-Outcome Relationships From Social Media Authors: Emre KicKiman, Matthew Richardson Abstract: Every day, people take actions, trying to achieve their personal, high-order goals. People decide what actions to take based on their personal experience, knowledge and gut instinct. While this leads to positive outcomes for some people, many others do not have the necessary experience, knowledge and instinct to make good decisions. What if, rather than making decisions based solely on their own personal experience, people could take advantage of the reported experiences of hundreds of millions of other people? In this paper, we investigate the feasibility of mining the relationship between actions and their outcomes from the aggregated timelines of individuals posting experiential microblog reports. Our contributions include an architecture for extracting action-outcome relationships from social media data, techniques for identifying experiential social media messages and converting them to event timelines, and an analysis and evaluation of action-outcome extraction in case studies. ACM DL: http://dl.acm.org/citation.cfm?id=2783310 DOI: http://dx.doi.org/10.1145/2783258.2783310
Views: 135 Association for Computing Machinery (ACM)
MIKE SCHMIDT Mike Schmidt works in the Cornell Computational Synthesis Lab (CCSL) at Cornell University. His research includes symbolic regression and related evolutionary algorithms. He is the co-designer of Eureqa, a free software tool for detecting equations and hidden mathematical relationships in data. Its goal is to identify the simplest mathematical formulas which could describe the underlying mechanisms that produced the data. About TEDx In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)
Views: 4860 TEDx Talks
The Planetary Nervous System can be imagined as a global sensor network, where 'sensors' include anything able to provide static and dynamic data about socio-economic, environmental or technological systems which measure or sense the state and interactions of the components that make up our world. Such an infrastructure will enable real-time data mining - reality mining - using data from online surveys, web and lab experiments and the semantic web to provide aggregate information. FuturICT will closely collaborate with Sandy Pentland's team at MIT's Media Lab, to connect the sensors in today's smartphones (which comprise accelerometers, microphones, video functions, compasses, GPS, and more). One goal is to create better compasses than the gross national product (GDP), considering social, environmental and health factors. To encourage users to contribute data voluntarily, incentives and micropayment systems must be devised with privacy-respecting capabilities built into the data-mining, giving people control over their own data. This will facilitate collective and self-awareness of the implications of human decisions and actions. Two illustrative examples for smart-phone-based collective sensing applications are the open streetmap project and a collective earthquake sensing and warning concept.
Views: 1289 FuturICT
In addition to using the standard, Select, Copy & Paste process, you can create a Web Query in Excel. The advantage of the Web Query is that when you "Refresh" it, you now have access to the most current information - without leaving Excel. Web Queries are great for setting up a system to gather the most current Sports Scores, Stock Prices or Exchange Rates. Watch as I demonstrate the process to follow to set this up in Excel. I invite you to visit my online shopping website - http://shop.thecompanyrocks.com - to see all of the resources that I offer you. Danny Rocks The Company Rocks
Views: 229490 Danny Rocks
Usama Fayyad, Ph.D. is Chief Data Officer at Barclays. His responsibilities, globally across Group, include the governance, performance and management of our operational and analytical data systems, as well as delivering value by using data and analytics to create growth opportunities and cost savings for the business. He previously led OASIS-500, a tech startup investment fund, following his appointment as Executive Chairman in 2010 by King Abdullah II of Jordan. He was also Chairman, Co-Founder and Chief Technology Officer of ChoozOn Corporation/ Blue Kangaroo, a mobile search engine service for offers based in Silicon Valley. In 2008, Usama founded Open Insights, a US-based data strategy, technology and consulting firm that helps enterprises deploy data-driven solutions that effectively and dramatically grow revenue and competitive advantage. Prior to this, he served as Yahoo!'s Chief Data Officer and Executive Vice President where he was responsible for Yahoo!'s global data strategy, architecting its data policies and systems, and managing its data analytics and data processing infrastructure. The data teams he built at Yahoo! collected, managed, and processed over 25 terabytes of data per day, and drove a major part of ad targeting revenue and data insights businesses globally. In 2003 Usama co-founded and led the DMX Group, a data mining and data strategy consulting and technology company specializing in Big Data Analytics for Fortune 500 clients. DMX Group was acquired by Yahoo! in 2004. Prior to 2003, he co-founded and served as Chief Executive Officer of Audience Science. He also has experience at Microsoft where led the data mining and exploration group at Microsoft Research and also headed the data mining products group for Microsoft's server division. From 1989 to 1996 Usama held a leadership role at NASA's Jet Propulsion Laboratory where his work garnered him the Lew Allen Award for Excellence in Research from Caltech, as well as a US Government medal from NASA. He spoke at the University of Michigan Symposium on Data and Computational Science on April 23, 2014.
Afghanistan is one of the most heavily mined countries in the world. The Mine Action Programme of Afghanistan (MAPA) explain what's being done to rid the country of this terrible legacy. Mine Action Programme of Afghanistan (MAPA) Collectively known as the Mine Action Programme of Afghanistan (MAPA), mine action implementers in Afghanistan form one of the largest mine action programmes in the world. Together, these agencies have a twenty year history of successfully delivering mine action in Afghanistan and have cleared over 18,000 hazard areas throughout the country. The MAPA was the first 'humanitarian' (i.e. non-military) mine action programme in the world and encompasses all pillars of mine action: advocacy, demining, stockpile destruction, mine risk education (MRE), and victim assistance (VA). Over 30 mine action organizations currently work in Afghanistan, employing over 14,000 personnel. These partners, which include national and international actors, both from the commercial and not for‐profit sector deliver a wide range of mine action services including manual demining, mechanically assisted clearance, mine dog detection assets, Explosive Ordnance Disposal (EOD), survey, MRE, victim assistance activities, and data collection. About MACCA/DMC and Mine Action Coordination In 2002 the Government of Afghanistan entrusted interim responsibility for mine action to the United Nations, via a coordination body managed by the United Nations Mine Action Service (UNMAS). In January 2008, through the modality of an Inter‐Ministerial Board (IMB) for Mine Action, the Government designated the Department of Mine Clearance (DMC) under the Afghan National Disaster Management Authority (ANDMA) to work jointly with the UN coordination body, MACCA. DMC and MACCA are jointly responsible for the coordination, with all stakeholders, of all mine action activities in Afghanistan. Meetings are held on a monthly basis with Implementing Partners to discuss planning, security, new technologies, and any other important issues arising. Based on both the expressed desire of the Government of Afghanistan, and the United Nations' strategic goal of assisting in the development of national institutions, MACCA is also responsible for supporting the development of national capacity for mine action management to the Government of Afghanistan. The MACCA employs national personnel and international staff to coordinate and provide support to mine action operations through its headquarters in Kabul and Area Mine Action Centres (AMACs). AMACs, staffed entirely by Afghans, are located in Kabul, Herat, Kandahar, Mazar‐i‐Sharif, Kunduz, Gardez, and Jalalabad. They work directly with the impacted communities, government representatives, UN offices, and aid organizations in their areas of responsibility. Directed by: Sam French Cinematography: Jake Simkin Edited by: Sam French
Views: 5325 Devpics
Common Core Goes Global November 20, 2013 Mary Jo Anderson Read at: http://www.crisismagazine.com/2013/common-core-goes-global [A]t the request of educators I wrote the World Core Curriculum, the product of the United Nations, the meta-organism of human and planetary evolution. — Robert Muller, former U.N. Assistant Secretary General The education reform known as Common Core State Standards (CCSS) for grades K-12, adopted by forty plus states and more than half of the U.S. dioceses, is designed to produce a universal “work force ready” population prepared to self-identify as “global citizens.” Many education professionals have been critical of CCSS. But even they may not know the philosophical reason why financiers like Bill Gates have bankrolled the Common Core system. The same sources of funding for Common Core in the United States are promoting similar methods and aligned texts world wide through the auspices of the United Nations. In Crisis, readers learned that Common Core is financed with over $150 million from the Bill and Melinda Gates Foundation. The collaboration of the Gates Foundation and the United Nations Educational Scientific and Cultural Organization (UNESCO) has been well publicized. In addition, Gates, on behalf of his Microsoft Corporation, signed a 26-page Cooperation Agreement in 2004 between Microsoft and UNESCO to develop a “master curriculum” which included benchmarks and assessments. The agreement stipulates that “UNESCO will explore how to facilitate content development.” Some have decried Common Core as the nationalization of American education. Far more dangerous, however, is the globalism of Common Core that demotes American values, undermines American constitutional principles and detaches students from their families and faith. Common Core is simply the newest attempt in the decades-old battle (Outcome Based Education, Goals 2000) to impose a U.N. globalist worldview aimed at “peace,” sustainability and economic stability at the expense of freedom. Briefly, the globalist philosophy calls for the establishment of a global culture based on a commitment to sustainable processes and humanistic ethics to ensure world peace and “fair” distribution of natural resources. The U.N. serves as the hub for this globalist hope. Adherents believe that some form of world congress and world citizenship is the end point of political evolution, and, therefore it is inevitable. What is not certain, in their view, is the time of fulfillment. Those who hold this philosophy are passionate—they fear that unless a form of world convergence of mind and political will arrives very soon, the planet may fail from wars, global warming and similar threats. Pick up popular magazines and you’ll find “world leaders,” celebrities and pundits who espouse some version of globalism. How would globalism work at ground level? A nation is permitted to keep its surface culture, such as language, music, and cuisine. But patriotism, religion, and individualism are anathema, as each competes with the globalist vision of world harmony. Moral codes that cannot be adapted to a multicultural vision, agreed upon in a world congress, must be jettisoned. But back on the ground, it’s difficult to convince a people to abandon their country and culture, not to mention national resources; resistance would be too great. The quickest effective approach is to invest in education to ensure that the coming generation will embrace the principles of globalism as a natural consequence of their formation. Previous Crisis articles have detailed the lack of academic rigor of CCSS for both math and English Language Arts. Teachers have reported disturbing “aligned texts” that contain crude, sexually explicit reading selections for young teens. Parents have questioned multiple examples of anti-American sentiment (the Boston Tea Party as a terrorist attack, for example). Despite this outcry, Common Core defenders insist that the standards are necessary, even though it only prepares students for admission to junior college. If the standards are substandard, why are hundreds of millions of Gates and other foundation monies, as well as over a billion dollars in government carrots, being pumped into this ‘transformation” of education? The goal is not academic excellence, but to reconstruct the nations of the world into a new, interdependent model. Their educational model is aimed at an economically stable world with “workforce ready” workers who share the same globalist vision. Read at: http://www.crisismagazine.com/2013/common-core-goes-global Related: http://eagnews.org/common-core-architect-david-colemans-history-with-the-ayers-and-obama-led-chicago-annenberg-challenge/ http://www.breitbart.com/big-government/2013/12/04/roots-of-common-core-lie-in-association-between-barack-obama-and-bill-ayers/ http://freedomoutpost.com/obama-ayers-and-the-muslim-connection-to-common-core/
Views: 26 TEXAS LIBERTY ADVOCATE NETWORK ACTION
The goal of this project is to develop a Microsoft Windows-based Computer Grid infrastructure that will support high performance scientific computing and integration of multi source biometric applications. The University of Houston Microsoft Windows-based Computer Grid (WING) includes not only the Computer Science and the Technology Department networks, but also includes nodes in China, Germany, and several other countries. The total amount of available storage exceeds 4 Terabytes. Four specific biomedical applications developed at University of Houston are the basis of this project: Computational tracking of Human Learning using Functional Brain Imaging Monitoring Human Physiology at a Distance by using Infrared Technology Multimodal Face Recognition and Facial Expression Analysis Relating Video, Thermal Imaging, and EEG Analysis ΓÇö integrate and analyze simultaneously recorded brain activity, infrared images, and 3D video This Biomedical Data Grid project meets the following technical requirements: Rapid application development (use of the Microsoft Visual Studio .NET technology) Visual modeling interfaces (forms driven Graphical User Interfaces) Database Connectivity (interface with Microsoft SQL Server 2005) Query support (clients can store, update, delete, retrieve database metadata) Context-sensitive, role-based access (Microsoft Windows Server 2003, ASP.NET) Robust security (HIPPA compliance through MicrosoftΓÇÖs Authentication and Authorization from IIS and ASP.NET) Connectivity to other biomedical resources (PACS, DICOM, XML) The Biomedical Data Grid application is developed using Microsoft Windows Server 2003, Microsoft Virtual Server 2005, Microsoft Visual Studio .NET Beta 2, and the Microsoft SQL Server 2005. A web client will be able to securely upload biomedical files to a web server while metadata related to these files will be stored in the SQL Server 2005 database for the purpose of querying, data mining, etc. Post-processing and simulation steps on biomedical data will be using a Master node Web Service that automatically distributes a large set of parameter or sensitivity analysis tasks to Slave nodes on the Computing Grid. We will give an overview of our project and provide a few examples of our biomedical applications.
Views: 15 Microsoft Research
http://www.visacoach.com/cr1-visa-timeline.html As of 2018, the answer is 12 to 15 months on average. This is much slower than 2017. I call this the Trump Effect. President Trump after taking office in January 2017 has mandated that USCIS vigorously enforce and administer immigration laws, take no short cuts. The goal is to restrict Legal immigration while stopping illegal immigration. The result is delays. To Schedule your Free Consultation with Fred Wahl, the Visa Coach visit http://www.visacoach.com/talk.html or Call - 1-800-806-3210 ext 702 or 1-213-341-0808 ext 702 Bonus eBook “5 Things you Must Know before Applying for your Visa” get it at http://www.visacoach.com/five.html Fiancee or Spouse visa, Which one is right for you? http://imm.guru/k1vscr1 What makes VisaCoach Special? Ans: Personally Crafted Front Loaded Presentations. Front Loaded Fiance Visa Petition http://imm.guru/front Front Loaded Spouse Visa Petition http://imm.guru/frontcr1 K1 Fiancee Visa http://imm.guru/k1 K1 Fiance Visa Timeline http://imm.guru/k1time CR1 Spousal Visa http://imm.guru/cr1 CR1 Spouse Visa Timeline http://imm.guru/cr108 Green Card /Adjustment of Status http://imm.guru/gc As of 2018, the answer is 12 to 15 months on average, 5 - 7 months USCIS, 5 - 6 months NVC, 2 months Consulate Two different departments of the US government are involved. USCIS (homeland security) and the Department of State. From Mid 2017 through to 2018 Homeland Security recently is getting their job done relatively slowly, currently taking 5 to 7 months. (this compares to processing times of a rapid 2 to 3 months just a year ago, and well under the 5 month Policy standard they have set for themselves. Why is USCIS now taking 2 to 3 times as long? I call this the Trump Effect. President Trump after taking office in January 2017 has mandated that USCIS vigorously enforce and administer immigration laws, take no short cuts. The goal is to restrict Legal immigration while stopping illegal immigration. "We have to get much tougher, much smarter, and less politically correct," Trump said. What this means is that they are very closely examining and scrutinizing all cases looking for reasons to deny. In addition cases that regularly had their interviews waived now specifically there is an Executive order that no interviews regardless of the strength of their evidences, may be waived. The result is USCIS has more work to do, has more bases to touch in the processing of EACH case. And while President Trump has promised to hire more staff to handle the increased load, so far no new staff has been hired, but the workload has increased. This is the Trump Effect. More work, with same staff. The result is that USCIS processing times for spouse visas have stretched to take at least 5 to 7 months. And it is possible this may even get worse, depending on how many new steps USCIS is asked to take, such as "extreme vetting" and "social media data mining" that are new labor intensive steps that have been proposed but not implemented yet. In addition to the general slow down due to the "Trump Effect" what also affects how long it takes for USCIS to approve your case is a function of how complete your petition is, how busy the processing center is, how current your FBI file is, and a bit of luck. The most obvious source of added delay is caused by incomplete and sloppy petitions. When USCIS finds a problem, processing grinds to a halt, and it is stopped until the problem is fixed. Sometimes the errors are so big that they don't bother asking for corrections and simply deny a case outright. Once USCIS finishes their part, the case is passed to the US Department of State. The Department of State has a processing center in New Hampshire, called the National Visa Center or NVC. The NVC first contacts your spouse, asking her to confirm a "Choice of Agent". Basically this is to confirm that You are allowed to be copied on correspondences sent to your spouse. It sounds strange as you are the original sponsor of the petition, but I believe this is a privacy issue, that NVC must address. After NVC receives the signed Choice of Agent form. They contact you via email with invoices for adjustment of status, and visa application fees. Once paid, you then then submit a packet of documents to NVC. I call this the "mini petition". This includes the proof of payment of fees, civil documents, financial evidences, etc. Once NVC is satisfied that all required documents have been presented, it forwards your case on to the American Consulate responsible to issue the visa. And at the same time notifies you and your spouse the date and time your spouse's interview has been scheduled for at the consulate. This is usually about 2 months later.The Approval/Denial decision is made during the interview. This is where the higher VisaCoach standard, for crafting "front loaded presentations" wins the day.
Views: 17172 Visa Coach
Towards Decision Support and Goal Achievement: Identifying Action-Outcome Relationships From Social Media KDD 2015 Emre Kcman Matthew Richardson Every day, people take actions, trying to achieve their personal, high-order goals. People decide what actions to take based on their personal experience, knowledge and gut instinct. While this leads to positive outcomes for some people, many others do not have the necessary experience, knowledge and instinct to make good decisions. What if, rather than making decisions based solely on their own personal experience, people could take advantage of the reported experiences of hundreds of millions of other people? In this paper, we investigate the feasibility of mining the relationship between actions and their outcomes from the aggregated timelines of individuals posting experiential microblog reports. Our contributions include an architecture for extracting action-outcome relationships from social media data, techniques for identifying experiential social media messages and converting them to event timelines, and an analysis and evaluation of action-outcome extraction in case studies.
Views: 0 Research in Science and Technology
http://www.salford-systems.com In this 25-minute data mining tutorial you will learn what cost functions are, why they are important, and explore some of the cost functions and evaluation criteria available to you as a data analyst. We will start with an introduction into what cost functions are, in general, and then continue the discussion by reviewing cost functions available for regression models, and available for classification models. These cost functions include: Least Squares Deviation Cost Least Absolute Deviation Cost, and Huber-M Cost.
Views: 302 Salford Systems
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: 237 Health Catalyst
Support us : https://www.instamojo.com/@exambin/ Download our app : http://examb.in/app Environmental Impact Assessment Developmental projects in the past were undertaken without any consideration to their environmental consequences. As a result the whole environment got polluted and degraded. In view of the colossal damage done to the environment, governments and public are now concerned about the environmental impacts of developmental activities. So, to assess the environmental impacts, the mechanism of Environmental Impact Assessment also known as EIA was introduced. EIA is a tool to anticipate the likely environmental impacts that may arise out of the proposed developmental activities and suggest measures and strategies to reduce them. EIA was introduced in India in 1978, with respect to river valley projects. Later the EIA legislation was enhanced to include other developmental sections since 1941. EIA comes under Notification on Environmental Impact Assessment (EIA) of developmental projects 1994 under the provisions of Environment (Protection) Act, 1986. Besides EIA, the Government of India under Environment (Protection) Act 1986 issued a number of other notifications, which are related to environmental impact assessment. EIA is now mandatory for 30 categories of projects, and these projects get Environmental Clearance (EC) only after the EIA requirements are fulfilled. Environmental clearance or the ‘go ahead’ signal is granted by the Impact Assessment Agency in the Ministry of Environment and Forests, Government of India. Projects that require clearance from central government can be broadly categorized into the following sectors • Industries • Mining • Thermal power plants • River valley projects • Infrastructure • Coastal Regulation Zone and • Nuclear power projects The important aspects of EIA are risk assessment, environmental management and Post product monitoring. Functions of EIA is to 1. Serve as a primary environmental tool with clear provisions. 2. Apply consistently to all proposals with potential environmental impacts. 3. Use scientific practice and suggest strategies for mitigation. 4. Address all possible factors such as short term, long term, small scale and large scale effects. 5. Consider sustainable aspects such as capacity for assimilation, carrying capacity, biodiversity protection etc... 6. Lay down a flexible approach for public involvement 7. Have a built-in mechanism of follow up and feedback. 8. Include mechanisms for monitoring, auditing and evaluation. In order to carry out an environmental impact assessment, the following are essential: 1. Assessment of existing environmental status. 2. Assessment of various factors of ecosystem (air, water, land, biological). 3. Analysis of adverse environmental impacts of the proposed project to be started. 4. Impact on people in the neighborhood. Benefits of EIA • EIA provides a cost effective method to eliminate or minimize the adverse impact of developmental projects. • EIA enables the decision makers to analyses the effect of developmental activities on the environment well before the developmental project is implemented. • EIA encourages the adaptation of mitigation strategies in the developmental plan. • EIA makes sure that the developmental plan is environmentally sound and within limits of the capacity of assimilation and regeneration of the ecosystem. • EIA links environment with development. The goal is to ensure environmentally safe and sustainable development. Environmental Components of EIA: The EIA process looks into the following components of the environment: • Air environment • Noise component : • Water environment • Biological environment • Land environment EIA Process and Procedures Steps in Preparation of EIA report • Collection of baseline data from primary and secondary sources; • Prediction of impacts based on past experience and mathematical modelling; • Evolution of impacts versus evaluation of net cost benefit; • Preparation of environmental management plans to reduce the impacts to the minimum; • Quantitative estimation of financial cost of monitoring plan and the mitigation measures. Environment Management Plan • Delineation of mitigation measures including prevention and control for each environmental component, rehabilitation and resettlement plan. EIA process: EIA process is cyclical with interaction between the various steps. 1. Screening 2. Scoping 3. Collection of baseline data 4. Impact prediction 5. Mitigation measures and EIA report 6. Public hearing 7. Decision making 8. Assessment of Alternatives, Delineation of Mitigation Measures and Environmental Impact Assessment Report 9. Risk assessment
Views: 16187 Exambin
Exciting Video ~ For You ! Top Picks NHL My Christmas Present For You! Top 4 Picks NHL Hockey 12/13/18 Sports Betting Tips Strategies 4U!Sports Betting Strategies Strategy Tips. HD high definition. December 13, 2018 is the date of production. Methodology includes stats, injuries, matchups, and trends. Looking for the Edge. Data mining to find the best matchups. Major bullet points and X-factors that lead to sports forecasting predictions. This video may be of interest to sports fans, draftkings, fanduel, vegas, etc. Let's share our love of sports in a friendly way. Please Subscribe. Here are some resources: TSN https://www.tsn.ca/nhl/scores ESPN http://www.espn.com/nhl/scoreboard?date=20180208 NHL https://www.nhl.com NBC http://scores.nbcsports.com/nhl/standings_conference.asp NHL hockey teams are Tampa Bay Lightning Vegas Golden Knights Anaheim Ducks Carolina Hurricanes Los Angeles Kings New York Rangers Chicago Blackhawks Florida Panthers Detroit Red Wings Edmonton Oilers Montreal Canadiens Vancouver Canucks Arizona Coyotes Ottawa Senators Buffalo Sabres Washington Capitals Winnipeg Jets Boston Bruins Nashville Predators St. Louis Blues Toronto Maple Leafs Pittsburgh Penguins San Jose Sharks Calgary Flames New Jersey Devils Dallas Stars Philadelphia Flyers Columbus Blue Jackets Minnesota Wild Colorado Avalanche New York Islanders Olympics
Views: 298 FanMD
✦ We talk about No Man's Sky's next update, when it might drop and what has been rumored to be included in it. Will it finally include multiplayer, or just add to the combat focus of the game? Data mining suggest a 4th alien race and tracking missiles along with other things. No Man's Sky's updates have come on a three month cycle so it's safe to assume we will be getting another update in June! ✦ This channel is largely based off of YOU, the community! So if there is a game you want to know more about or want me to cover on the channel, make sure you leave it in the comments and I'll check it out! ✦ Remember to LIKE the video if you enjoyed it and SUBSCRIBE for endless Destiny 2 and No Man's Sky videos! _________________________________________________________________ ✦ CHECK OUT my PATREON! Currently I have a goal set to help me start saving up for a new PC so I will be able to play more of the games I cover on JustJarrod Gaming. Allowing me to provide my own gameplay as well as give more knowledge and game experience tailored to the games I'll be covering. A better PC also means better editing and more polished videos and content! PATREON: https://www.patreon.com/justjarrod ✦ FOLLOW me on TWITTER for channel updates, video updates, game news, game updates, and just to get to know me a bit more! TWITTER: https://twitter.com/JustJarrod_ ✦ Background Music Provided by NCS: https://www.youtube.com/watch?v=QfhF0V9VlJA https://www.youtube.com/watch?v=BWdZjZV6bEk ✦ Outro Music Provided by NCS: Subtact - Away https://www.youtube.com/watch?v=0Tp-G...
Views: 2769 JustJarrod
Microsoft Excel, this list covers all the basics you need to start entering your data and building organized workbooks Main Play list : http://goo.gl/O5tsH2 (70+ Video) Subscribe Now : http://goo.gl/2kzV8M Topics include: 1. What is Excel and what is it used for? 2. Using the menus 3. Working with dates and times 4. Creating simple formulas 5. Formatting fonts, row and column sizes, borders, and more 6. Inserting shapes, arrows, and other graphics 7. Adding and deleting rows and columns 8. Hiding data 9. Moving, copying, and pasting 10. Sorting and filtering data 11. Securing your workbooks 12. Tracking changes
Views: 61385 tutorbeta
https://goo.gl/UBwUkn Testing or Data Warehouse Testing Tutorial Before we pick up anything about ETL Testing its vital to find out about Business Intelligence and Dataware. We should begin – What is BI? Business Intelligence is the way toward gathering crude information or business information and transforming it into data that is valuable and more important. The crude information is the records of the every day exchange of an association, for example, communications with clients, organization of back, and administration of representative et cetera. These information's will be utilized for "Announcing, Analysis, Data mining, Data quality and Interpretation, Predictive Analysis". What is Data Warehouse? An information distribution center is a database that is intended for question and examination as opposed to for exchange handling. The information stockroom is developed by incorporating the information from numerous heterogeneous sources.It empowers the organization or association to unite information from a few sources and isolates examination workload from exchange workload. Information is transformed into great data to meet all venture revealing prerequisites for all levels of clients. What is ETL? ETL remains for Extract-Transform-Load and it is a procedure of how information is stacked from the source framework to the information distribution center. Information is removed from an OLTP database, changed to coordinate the information distribution center blueprint and stacked into the information stockroom database. Numerous information distribution centers likewise join information from non-OLTP frameworks, for example, content documents, inheritance frameworks and spreadsheets. Let perceive how it functions For instance, there is a retail location which has distinctive divisions like deals, promoting, coordinations and so forth. Each of them is dealing with the client data autonomously, and the way they store that information is very unique. The business division have put away it by client's name, while promoting office by client id. Presently on the off chance that they need to check the historical backdrop of the client and need to comprehend what the distinctive items he/she purchased attributable to various showcasing efforts; it would be extremely repetitive. The arrangement is to utilize a Datawarehouse to store data from various sources in a uniform structure utilizing ETL. ETL can change divergent informational collections into a brought together structure.Later utilize BI devices to infer significant bits of knowledge and reports from this information. The accompanying chart gives you the ROAD MAP of the ETL procedure Extract Extract applicable information Transform Transform information to DW (Data Warehouse) arrange Build keys - A key is at least one information characteristics that extraordinarily recognize a substance. Different sorts of keys are essential key, interchange key, outside key, composite key, surrogate key. The datawarehouse possesses these keys and never enables some other element to dole out them. Cleansing of information :After the information is separated, it will move into the following stage, of cleaning and accommodating of information. Cleaning does the oversight in the information and in addition recognizing and settling the blunders. Acclimating implies settling the contentions between those information's that is incongruent, with the goal that they can be utilized as a part of an undertaking information distribution center. Notwithstanding these, this framework makes meta-information that is utilized to analyze source framework issues and enhances information quality. Load Load information into DW ( Data Warehouse) Build totals - Creating a total is outlining and putting away information which is accessible in reality table keeping in mind the end goal to enhance the execution of end-client inquiries. What is ETL Testing? ETL testing is done to guarantee that the information that has been stacked from a source to the goal after business change is precise. It additionally includes the confirmation of information at different center stages that are being utilized amongst source and goal. ETL remains for Extract-Transform-Load. ETL Testing Process Like other Testing Process, ETL additionally experience distinctive stages. The diverse periods of ETL testing process is as per the following ETL testing is performed in five phases Identifying information sources and prerequisites Data securing Implement business rationales and dimensional Modeling Build and populate information Build Reports https://youtu.be/IDIQYB9DzZ0
Views: 12 Software Testing Masterminds
Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 6: Application to Bioinformatics – Signal peptide prediction http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 2840 WekaMOOC
In this video, we will continue with our use of the Tweepy Python module and the code that we wrote from Part 1 of this series: https://www.youtube.com/watch?v=wlnx-7cm4Gg The goal of this video will be to understand how Tweepy handles pagination, that is, how can we use Tweepy to comb over the various pages of tweets? We will see how to accomplish this by making use of Tweepy's Cursor module. In doing so, we will be able to directly access tweets, followers, and other information directly from our own timeline. We will also continue to improve the code that we wrote from Part 1 Relevant Links: Part 1: https://www.youtube.com/watch?v=wlnx-7cm4Gg Part 2: https://www.youtube.com/watch?v=rhBZqEWsZU4 Part 3: https://www.youtube.com/watch?v=WX0MDddgpA4 Part 4: https://www.youtube.com/watch?v=w9tAoscq3C4 Part 5: https://www.youtube.com/watch?v=pdnTPUFF4gA Tweepy Website: http://www.tweepy.org/ Cursor Docs: http://docs.tweepy.org/en/v3.5.0/cursor_tutorial.html API Reference: http://docs.tweepy.org/en/v3.5.0/api.html GitHub Code for this Video: https://github.com/vprusso/youtube_tutorials/tree/master/twitter_python/part_2_cursor_and_pagination My Website: vprusso.github.io This video is brought to you by DevMountain, a coding boot camp that offers in-person and online courses in a variety of subjects including web development, iOS development, user experience design, software quality assurance, and salesforce development. DevMountain also includes housing for full-time students. For more information: https://devmountain.com/?utm_source=Lucid%20Programming Do you like the development environment I'm using in this video? It's a customized version of vim that's enhanced for Python development. If you want to see how I set up my vim, I have a series on this here: http://bit.ly/lp_vim If you've found this video helpful and want to stay up-to-date with the latest videos posted on this channel, please subscribe: http://bit.ly/lp_subscribe
Views: 7859 LucidProgramming
http://www.visacoach.com/k1-visa-timeline.html How long does it take to get a K1 Fiance visa? In 2018 most cases typically take 7 to 9 months on average from the time USCIS receives the I-129F fiance visa application until the K-1 visa is embossed onto the foreign fiancee’s passport. Today's question is: How long does it take to get a K1 Fiance visa? As of January 2018, the answer is 7 to 9 months on average. 5 - 7 months USCIS 1/2 month NVC 1 - 2 months Consulate I regularly get calls from people saying those numbers must be wrong, because they found a website or person who promised a MUCH shorter processing time so whats their secret? Well the secret is they are either telling you what "you want to hear" so they can get your money, or just referring to one step of the process, not combining and adding up ALL the steps from initial submission of your petition, to visa embossed onto your fiancee's passport When I give time estimates I always use what is relevant to the couple, and that is starting from the day USCIS receives the petition, ending on the day your foreign fiancee gets the visa. Two separate departments of the US government are involved. USCIS (homeland security) and the Department of State. From Mid 2017 through to 2018 Homeland Security recently is getting their job done relatively slowly, currently taking 5 to 7 months. (this compares to processing times of a rapid 2 to 3 months just a year ago, and well under the 5 month Policy standard they have set for themselves. Why is USCIS now taking 2 to 3 times as long? I call this the Trump Effect. President Trump after taking office in January 2017 has mandated that USCIS vigorously enforce and administer immigration laws, take no short cuts. The goal is to restrict Legal immigration while stopping illegal immigration. "We have to get much tougher, much smarter, and less politically correct," Trump said. What this means is that they are very closely examining and scrutinizing all cases looking for reasons to deny. In addition cases that regularly had their interviews waived now specifically there is an Executive order that no interviews regardless of the strength of their evidences, may be waived. The result is USCIS has more work to do, has more bases to touch in the processing of EACH case. And while President Trump has promised to hire more staff to handle the increased load, so far no new staff has been hired, but the workload has increased. This is the Trump Effect. More work, with same staff. The result is that USCIS processing times for fiance visas have stretched to take at least 5 to 7 months. And it is possible this may even get worse, depending on how many new steps USCIS is asked to take, such as "extreme vetting" and "social media data mining" that are new labor intensive steps that have been proposed but not implemented yet. USCIS Processing includes a backround check by the FBI In addition to the general slow down due to the "Trump Effect" what also affects how long it takes for USCIS to approve your case is a function of how complete your petition is, how busy the processing center is, how current your FBI file is, and a bit of luck. The most obvious source of added delay is caused by incomplete and sloppy petitions. When USCIS finds a problem, processing grinds to a halt, and it is stopped until the problem is fixed. Sometimes the errors are so big that they don't bother asking for corrections and simply deny a case outright. Once USCIS finishes their part, the case is passed to the US Department of State. The Department of State has a processing center in New Hampshire, called the National Visa Center or NVC. NVC basically assigns a new Department of State case number, then forwards the file on to the American Consulate reponsible to issue the visa. Once NVC has completed their actions, a few weeks later the case file physically arrives at the consulate assigned to process your Fiancee. Within a few weeks, the consulate contacts your Fiancee directly with instructions on booking the interview, attending the medical and final document checklists. Some consulates are busier, more efficient, and or work faster than others. In Philippines the process is very efficient and fast. Interviews can be booked by the fiancee about a month later. In Vietnam and China it may be 2 to 3 months later before the consulate advises the interview can be scheduled. The Approval/Denial decision is made during the interview. Then in about 2 weeks the passport with its new K1 visa is returned. Just as in medicine commercials on TV. "Your results may vary". Some of my clients get their visas faster, some slower. If in 2018 you anticipate 7 to 9 months average K1 Fiance Visa processing time, you won't be far off.
Views: 23162 Visa Coach
Computational Biology in the 21st Century: Making Sense out of Massive Data Air date: Wednesday, February 01, 2012, 3:00:00 PM Category: Wednesday Afternoon Lectures Description: The last two decades have seen an exponential increase in genomic and biomedical data, which will soon outstrip advances in computing power to perform current methods of analysis. Extracting new science from these massive datasets will require not only faster computers; it will require smarter algorithms. We show how ideas from cutting-edge algorithms, including spectral graph theory and modern data structures, can be used to attack challenges in sequencing, medical genomics and biological networks. The NIH Wednesday Afternoon Lecture Series includes weekly scientific talks by some of the top researchers in the biomedical sciences worldwide. Author: Dr. Bonnie Berger Runtime: 00:58:06 Permanent link: http://videocast.nih.gov/launch.asp?17563
Views: 5030 nihvcast