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: 146975 SciShow
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: 47480 CodingEntrepreneurs
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: 27601 THE MIND HEALING
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: 95665 Shomu's Biology
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: 312 Salford Systems
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: 2362 DataCamp
The best way to improve personal banking and meet your financial goals is to ditch your traditional bank, says Josh Reich, CEO of Simple. Reich's online platform and Simple card gives users access to ATMs and real-time banking that instantly shows transactions on mobile devices -- all for free. Simple banking helps people plan their future with features like the daily Safe-to-Spend Balance which highlights savings goals and gives easy to understand account information. Co-founding Simple in 2009, Reich's background includes running a data mining consulting firm, a quantitative strategy group at a $10-billion fund and he did a stint at Root Exchange.
Coding With Python :: Learn API Basics to Grab Data with Python This is a basic introduction to using APIs. APIs are the "glue" that keep a lot of web applications running and thriving. Without APIs much of the internet services you love might not even exist! APIs are easy way to connect with other websites & web services to use their data to make your site or application even better. This simple tutorial gives you the basics of how you can access this data and use it. If you want to know if a website has an api, just search "Facebook API" or "Twitter API" or "Foursquare API" on google. Some APIs are easy to use (like Locu's API which we use in this video) some are more complicated (Facebook's API is more complicated than Locu's). More about APIs: http://en.wikipedia.org/wiki/Api Code from the video: http://pastebin.com/tFeFvbXp If you want to learn more about using APIs with Django, learn at http://CodingForEntrepreneurs.com for just $25/month. We apply what we learn here into a Django web application in the GeoLocator project. The Try Django Tutorial Series is designed to help you get used to using Django in building a basic landing page (also known as splash page or MVP landing page) so you can collect data from potential users. Collecting this data will prove as verification (or validation) that your project is worth building. Furthermore, we also show you how to implement a Paypal Button so you can also accept payments. 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: 431997 CodingEntrepreneurs
Deep Learning Crash Course playlist: https://www.youtube.com/playlist?list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07 Highlights: Garbage-in, Garbage-out Dataset Bias Data Collection Web Mining Subjective Studies Data Imputation Feature Scaling Data Imbalance #deeplearning #machinelearning
Views: 1492 Leo Isikdogan
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: 23658 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: 149 The DRIVE/conference
In this video, we learn how to calculate a linear regression line. Coupled with What If Analysis to set a goal, we can predict how long it will take to reach that goal. This is a fun and fairly advanced problem that can be used for some simple predictive analytics. Play with my YouTube data! https://bielite.com/#try To enroll in my introductory Power BI course: https://www.udemy.com/learn-power-bi-fast/?couponCode=CHEAPEST Daniil's Blog Post: https://xxlbi.com/blog/simple-linear-regression-in-dax/
Views: 2471 BI Elite
Jeff Bezos Blue Origin, which Launch Satellites for Telesat Company, Battling SpaceX For Space Internet Supremacy INTRO Jeff Bezos' Blue Origin announced Thursday, January 31, that it has signed an agreement to launch satellites for Canadian telecommunications company Telesat. Telesat is one of several companies racing to build satellite internet in low Earth orbit, a list that includes Elon Musk’s SpaceX, and Richard Branson-backed OneWeb. Blue Origin has agreed to provide multiple launches on its yet-to-be-built New Glenn rocket, to get Telesat’s spacecraft into low Earth orbit, or LEO. In this video, Engineering Today will discuss about Jeff Bezos Blue Origin, which Launch Satellites For Telesat Company, Battling SpaceX And Others For Space Internet Supremacy. So Let’s get started. GOAL FOR HIGH-SPEED INTERNET As per the agreement announced, Blue Origin will launch satellites for Telesat, which it is hoping to launch the first time in 2020, giving the company the ability to reach orbit. Currently, Blue Origin's space tourism-focused New Shepard vehicle – which could start launching people this year – can only perform short hops to suborbit. A number of other companies including SpaceX and OneWeb, are also involved in this race to develop a functioning global space internet. Telesat’s first LEO satellite was launched a year ago for orbital testing. The company is expected to offer first-generation data services in the early 2020s. That time frame meshes with Blue Origin’s development plan for the orbital-class New Glenn rocket, which is currently scheduled to have its first launch from the company’s Florida complex in 2020. Blue Origin is also currently testing a suborbital spaceship known as New Shepard, which could start carrying people by the end of this year. BLUE ORIGIN WITH TELESAT “Blue Origin’s powerful New Glenn rocket, is a disruptive force in the launch services market which, in turn, will help Telesat disrupt the economics and performance of global broadband connectivity,” said Dan Goldberg, Telesat’s President and CEO, in a separate statement. “Telesat is working with a range of world-class companies to build, deploy and operate our advanced, global LEO network.” “Blue Origin is honored, that Telesat has selected our powerful New Glenn rocket, to launch Telesat’s innovative LEO satellite constellation into space,” Blue Origin CEO Bob Smith said in a statement. Bob Smith also said, he and his teammates were “excited to be partnering with this industry leader, on their disruptive satellite network architecture.” “New Glenn’s 7-meter fairing, with its huge mass and volume capabilities, is a perfect match for Telesat’s constellation plans while reducing launch costs per satellite,” Smith said. As mentioned, Telesat is not the only company hoping to make waves in the space internet market, which has been valued at $127.7 billion. Elon Musk’s SpaceX has been working on its own service, called Starlink, with the goal of launching a rather astonishing 12,000 satellites in total. Nearly two years ago, Blue Origin struck a deal with OneWeb, for five launches on the New Glenn, starting in 2021. OneWeb executive chairman Greg Wyler, has been quoted as saying, that each of those launches could put as many as 80 satellites in low Earth orbit. OneWeb has launch agreements with several other providers as well, including Virgin Orbit, and the European Arianespace consortium. Its first satellite launch had been scheduled to lift off from Arianespace’s complex in French Guiana, next month, but a problem with the Russian-made Soyuz rocket has forced a delay. Both OneWeb and SpaceX are aiming to start offering LEO satellite broadband services in 2020 or so. SPACEX’S STARLINK SpaceX is in the midst of a $500 million funding round to raise money for its Starlink satellite constellation, as well as its Starship super-rocket. The company’s facility in Redmond, Wash., is playing the lead role in developing the satellites for Starlink. SpaceX CEO Elon Musk referred to the challenge surrounding the Starlink and Starship projects during a teleconference, that focused on the financial state of his other major venture, Tesla. “SpaceX has two absolutely insane projects that would normally bankrupt a company, Starship and Starlink, and so SpaceX has to be incredibly spartan with expenditures until those programs reach fruition,” Musk told financial analysts. Finances would be less of a problem for Blue Origin’s Bezos, who ranks as the world’s richest individual with a net worth currently estimated at $142 billion, compared to Musk’s $21 billion. Though, Financial terms of Telesat’s deal with Blue Origin were not made public. SpaceX is aiming to launch its first batch of satellites this year, with its full constellation deployed by the mid-2020s. We’re not just talking about SpaceX, OneWeb and Telesat.
Views: 4300 ENGINEERING TODAY
Innovations in ways to compile, assess and act on the ever-increasing quantities of health data are changing the practice and police of medicine. Statisticians Laura Hatfield and Sherri Rose will discuss recent methodological advances and the impact of big data on human health. Speakers: Laura Hatfield, PhD Associate Professor, Department of Health Care Policy, Harvard Medical School Sherri Rose, PhD Associate Professor, Department of Health Care Policy, Harvard Medical School Like Harvard Medical School on Facebook: https://goo.gl/4dwXyZ Follow on Twitter: https://goo.gl/GbrmQM Follow on Instagram: https://goo.gl/s1w4up Follow on LinkedIn: https://goo.gl/04vRgY Website: https://hms.harvard.edu/
Views: 3935 Harvard Medical School
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: 2939 WekaMOOC
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: 3359 Association for Computing Machinery (ACM)
Borderlands 2 shieldless playthrough on normal mode, Jakobs Absolute Allegiance playthrough. Heavily underleveled at this point. As this is a fairly open map, I've used the vehicle a lot. Map is packed with SGT Loaders, which makes scoring crits and killing them loaders fast much harder... Next stop: Saturn. Thread on GBX forums: http://forums.gearboxsoftware.com/t/a... For more info on allegiances, check out my Guide on BL2 allegiance on official GBX forums: http://forums.gearboxsoftware.com/t/g... and on Steam: http://steamcommunity.com/sharedfiles... If you like my videos be sure to leave a LIKE and consider to SUBSCRIBE for more content. _______________________ Borderlands 2 builds upon the gameplay elements introduced in its predecessor. It is a first-person shooter that includes character-building elements found in role-playing games, leading Gearbox to call the game a "role-playing shooter." At the start of the game, players select one of four new characters, each with a unique special skill and with proficiencies with certain weapons. From then on, players take on quests assigned through non-player characters or from bounty boards, each typically rewarding the player with experience points, money, and sometimes a reward item. Players earn experience by killing foes and completing in-game challenges (such as getting a certain number of kills using a specific type of weapon). As they gain levels from experience growth, players can then allocate skill points into a skill tree that features three distinct specializations of the base character Borderlands 2 begins with the player fighting to the death to win a priceless cache of loot in a gladiatorial tournament run by Handsome Jack for his personal amusement. The player succeeds and gains notoriety, but Handsome Jack sees this as a threat to his popularity with the people. At the tournament's end he denies the reward and leaves the player for dead in a tundra. The mysterious Guardian Angel from the first game then contacts the player and explains that Handsome Jack must be killed, directing the player to rescue the four original vault hunters from Hyperion's clutches to accomplish this. Although Jack's full intentions are unknown, part of his plan involves finding an ancient evil named "The Warrior" that is located somewhere on Pandora.
Views: 113 Exotek
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: 133226 TED
What is The History of Bitcoin: Super Easy Explanation - https://blockgeeks.com/ We’ll start at the very beginning by understanding the history of blockchain. The very first blockchain in the world was Bitcoin. An anonymous person or group known as Satoshi Nakamoto published a document in an online cryptography forum in November 2008 and revealed the first details of how it would work, describing it as a “peer-to-peer electronic cash system”. The whitepaper is available today at bitcoin.org/bitcoin.pdf. It allows any 2 people to pseudonymously send money to each other no matter where they are in the world. It is a borderless currency. The main benefit of Bitcoin is that it does not require any centralized authority or institution to operate. This is in contrast to today’s centralized financial systems that depend on the existence of a central bank or government to mint money. If for any reason the central authority were to shutdown, the money would become worthless. In a decentralized system like Bitcoin, there is no central authority and the system can continue to operate as long as there are members in its peer-to-peer network. The goal of the whitepaper was to describe how the different parts of the Bitcoin protocol would operate and be kept secure. A new type of database, called a blockchain, would keep track of a single history of all Bitcoin transactions and it would be maintained by everyone in the network. The database would be publicly available for anyone to view and inspect, and anyone can download a copy of the same database. This provides data redundancy and makes sure the data is never lost, but also provides a way for anyone to verify the transactions in the database themselves. A block in the database just stores a sequence of transactions, and a sequence of blocks is called a blockchain. Each block is identified by an incrementing number and a unique Sha-256 hash. The hash for a block is calculated using the transactions inside it, as well as the previous block’s hash, which forms a chain of hashes. The data in the blocks is secured using a cryptographic algorithm called proof-of-work, which also keeps all members of the network and the database in sync to prevent double-spending. In this context, preventing double-spending means preventing anyone from spending money they dont have. Proof-of-work is used to generate new blocks for the database, also known as mining, and the reward for mining a new block is given to the miner by creating new Bitcoins in the system. This is the only way new Bitcoins can be created. Anyone on the network can be a miner and a new block is mined roughly every 10 minutes, which includes the latest set of verified transactions. The first release for Bitcoin was version 0.1 written in C++ by Satoshi and published on SourceForge in January 2009 under the open-source MIT license. Anyone could download the source code and run it to join the network, also known as becoming a node in the network. This is the original version 0.1 source code written by Satoshi. We can see the hard-coded genesis block, which is the very first block in the chain. The hash for the block can be verified by using any Bitcoin blockchain explorer. Let’s copy and paste this hash into the blockchain explorer available at blockchain.info. We can see that this hash is for block number 0, and that it has only one transaction in it which is the mining reward, and the reward amount of 50 Bitcoin was given to this Bitcoin address. We can also see this 50 Bitcoin reward for the genesis block in the original source code. The genesis block is a special case needed to start the blockchain and is the only block that is hard-coded, whereas every subsequent block is calculated using proof-of-work. Satoshi’s motivation for creating Bitcoin is revealed in the piece of data he included in the genesis block: a newspaper headline from The Times that read ‘Chancellor on brink of second bailout for banks’. The date of the newspaper is proof that the genesis block was created on or after Jan 3 2009. Satoshi developed the source code mostly himself up until mid-2010, when he handed it off to the open-source community. It is now maintained under the project called Bitcoin Core. The software is currently at version 0.15.1 and is available for download at bitcoin.org. This is still the most popular Bitcoin client, and its estimated that there are over 10 thousand nodes running the Bitcoin network today using various clients. Satoshi disappeared from public view in late 2010, his identity still unknown to this day. The only way someone could prove that they are Satoshi is by using the same encryption keys used when posting the original whitepaper in the online cryptography forum. To read more check out https://blockgeeks.com/
Views: 11998 Blockgeeks
Data science is an interdisciplinary field about processes and systems to extract knowledge or insights from data. Knowledge areas Data science include statistics, computer science, design and social sciences. In this lecture John Murphy discusses the Foursquare Case Study. DataScienceInsight.com was launched by Paul Fielding, John Murphy and Martin Vonderheiden with to goal to teach Data Science. to Non-Data Scientists. Learn to clean up messy data, uncover insights, make predictions using data science tools, and clearly communicate critical findings. Understand the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results.
Views: 83 DataScienceInsight
Data visualization is an effective way to communicate your nonprofits impact because humans are able to process images 60,000x faster than we can process text. Thus, images are a powerful tool while conveying your nonprofits message. You will learn: -how to make data visualizations on Google Sheets -how to choose the right type of graph -how to customize and organize a more effective visualization -how to embed and use this interactive data viz We know that it is not always the most accessible thing to for non-coders, but luckily, we have found a simple way for data viz using Google Sheets, which makes it extremely simple, accessible, and easy to update. 1. Use the right type of graph (01:03) What’s your goal with the viz? Is it to highlight relationships or to show composition? Nailing down these questions will help you decide on which graph to use. Here are the options on Google Sheets, and how we would recommend using them: Gauge chart: show goal and progress Map: show breadth of your programs Motion chart: emphasize progress/growth over time 2. Organize and clean (01:39) After choosing your graph type, Google Sheets will generate that chart for you, but all its features may not be relevant in your situation. Some questions that you may want to ask during this stage include: Are background lines necessary? Does the sorting order make sense? Remove or sort things more clearly to match your needs after answering these questions. 3. Format labels and legends (02:09) The next step is to go in even closer and edit the labels and legends so that people can better understand what exactly the chart is showing. This is the stage that you should: Add title and name of axis Remove legend if necessary 4. Customize design (02:22) Although Google Sheets does not have the best tools to manipulate the colors since there is not a custom option, you can get pretty close by trying to match your colors to the palette provided. 5. Publish and embed (02:43) Now just publish this graph on Google Sheets. Once you've done that, you can copy the embed code and paste it onto your site. Embedding the interactive graph is the same process as embedding a Youtube video, so it's You can assemble these graphs together and tie them with a narrative to show the impact that your nonprofit has made. Get out there are start making some awesome graphs to show off your great work! ------- Whole Whale is a digital agency that leverages data and technology to increase the impact of nonprofits. In the same way the Inuits used every part of whale, Whole Whale leverages existing resources to see, "What else can this do for us?" By using data analysis, digital strategy, web development, and training, WW builds a 'Data Culture' within every nonprofit organization they work with. ------- Check us out on Facebook : https://www.facebook.com/WholeWhale Tweet us: https://twitter.com/WholeWhale Visit our website: http://wholewhale.com/
Views: 2669 WholeWhale
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: 429 tutorbeta
This video is intended for people who want to learn about cell referencing and how to conduct a what-if analysis in Microsoft Excel. Topics covered in this lesson include relative cell references, absolute cell references, mixed cell references, the difference between functions and formulas, and conducting a what-if analysis in Excel. You may jump to any of these topics by using the links below: 1. Cell referencing: (1:07) 2. Relative cell references: (1:25) 3. Absolute cell references: (4:02) 4. Mixed cell references: (7:28) 5. Functions vs. formulas: (10:08) 6. Conducting a what-if analysis: (11:49)
Views: 31953 Dr. Daniel Soper
Machine Learning. Convolutional Neural Networks. Deep Learning Neural Networks. What is all the hype about? What are these technologies, what are they good for, and can we use them for anything useful right now? This session requires no background in any of these areas, and will introduce you to machine learning on iOS with a worked example. Download course materials here: https://store.raywenderlich.com/downloads/812 Watch the full course here: https://store.raywenderlich.com/products/rwdevcon-2017-vault-bundle --- About www.raywenderlich.com: https://www.raywenderlich.com/384-reactive-programming-with-rxandroid-in-kotlin-an-introduction raywenderlich.com is a website focused on developing high quality programming tutorials. Our goal is to take the coolest and most challenging topics and make them easy for everyone to learn – so we can all make amazing apps. We are also focused on developing a strong community. Our goal is to help each other reach our dreams through friendship and cooperation. As you can see below, a bunch of us have joined forces to make this happen: authors, editors, subject matter experts, app reviewers, and most importantly our amazing readers! --- From Wikipedia: https://en.wikipedia.org/wiki/Machine_learning Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed. The name machine learning was coined in 1959 by Arthur Samuel. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders, and computer vision. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data
Views: 453 raywenderlich.com
Advanced Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 2: Installing with Apache Spark http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/msswhT https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 2573 WekaMOOC
China moon mission: China far side Moon landing launch a new space race? Chang'e 4 touch on lunar! China far side of the moon mission is just the start of its space ambitions china moon landing. Chinese lunar goddess Chang'e, the Yutu 2 rover is making history as it sends back images and other data from the far side of the moon. The rover touched down , delivered to the moon by the Chang'e 4 probe, a historical first for humankind -- the far side of the moon has not previously been visited -- and a major achievement for China's increasingly impressive space program. Its success "opened a new chapter in humanity's exploration of the moon. Beijing Aerospace Control Center reacting to the touchdown, alongside one of the first images sent back by Chang'e 4 of the moon's far side. Keeping that in mind in this video, Engineering Today will discuss about China far side of the moon mission with achievements in space. Will China’s moon landing launch a new space race far side of the moon? CHINA'S ACHIEVEMENTS IN SPACE China first engaged in space activities. In 1978, Deng Xiaoping articulated China's space policy noting that, as a developing country, China would not take part in a space race. Instead, China's space efforts have focused on both launch vehicles and satellites. China's first space station, Tiangong-1. Missions like Chang'e 4. Its first lunar mission, Chang'e 1, orbited the moon and a rover landed on the moon. China's future plans-include a new space station, a lunar base and possible sample return missions from Mars china moon landing China moon mission, in China, where economic concerns are becoming increasingly pressing amid an ongoing trade war with the US -- was more limited than for the previous lunar mission, the success of Chang'e 4, and the global acclaim it has brought, will be a significant boost to the Chinese space program china moon landing. DREAMS OF SPACE The first stage of China's space dream. In 2020, the next lunar mission, Chang'e 5, is due to land on the moon. Lunar mission in the 2030s. China put a citizen on the moon. The Tiangong 2 space lab has been in orbit for over two years. "Our overall goal is that, by around 2030, China will be among the major space powers of the world," Wu Yanhua, deputy chief of the National Space Administration, said in 2016. But despite these big steps forward, China still has a long way to catch up in the space race. As Chang'e 4 was preparing to descend to the lunar surface, NASA sent back photos of Ultima Thule, the first ever flyby of an object in the Kuiper Belt, a collection of asteroids and dwarf planets a billion miles beyond Pluto. One achievement could see China leapfrog the US, however, and make history in the progress: landing an astronaut on Mars. RED PLANET Not since Gene Cernan climbed on board the Apollo 17 lunar module to return to Earth has humanity stepped foot on anything outside our planet far side of the moon. no one wants to be the first country to leave a corpse on the moon. This isn't to say the manned lunar missions were useless. Those advancements will be key in delivering a person to Mars, a far, far harder task. China will make its first visit to Mars with an unmanned probe set to launch by the end of next year, followed by mars MOON MINING China's space program is about more than that. The moon plays host to a wealth of mineral resources. China already dominates the global supply of REM, and exclusive access to the moon's supply could provide huge economic advantages. In addition to REM, the moon also possesses a large amount of Helium-3. Chinese space scientist, has long advocated for Helium-3 mining as a reason for moon missions. A NEW SPACE RACE? the Chinese space program, is its slow and steady pace. Because of the secrecy that surrounds many aspects of the Chinese space program, its exact capabilities are unknown. However, the program is likely on par with its counterparts of china moon landing. In terms of military applications, China has also demonstrated significant skills. In 2007, it undertook an anti-satellite test, launching a ground-based missile to destroy a failed weather satellite. While successful, the test created a cloud of orbital debris that continues to threaten other satellites. The movie "Gravity" illustrated the dangers space debris poses to both satellites and humans far side of the moon. The U.S., unlike other countries, has not engaged in any substantial cooperation with China because of national security concerns. In fact, a 2011 law bans official contact with Chinese space officials. Does this signal a new space race between the U.S. and China space race. Chinese space program at the International Astronautical Conference in Germany and discussed areas where China and the U.S. can work together for china moon landing. The Trump administration has used the threat posed by China and Russia to support its argument for a new independent military branch, a Space Force.
Views: 26379 ENGINEERING TODAY
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
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: 136 Association for Computing Machinery (ACM)
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: 126625 Well Academy
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: 6339 VeloPress
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: 7 Software Testing Masterminds
90 second summary video: https://youtu.be/Dl08xpfOVf0 Links to related materials: http://ands.org.au/presentations/index.html#15-09-16 CC-BY-NC This presentation would be of particular interest to: -- researchers: publishing articles based on clinical research data -- support staff: managing health and clinical data This presentation will look at the practical considerations, for researchers, of publishing articles about clinical data, and preparing clinical data for sharing and publication. Iain Hrynaszkiewicz is Head of Data and HSS Publishing at Nature Publishing Group and Palgrave Macmillan, where his responsibilities include developing new areas of open research publishing and data policy. He is publisher of Scientific Data and helps develop open access monograph publishing. Iain previously worked at Faculty of 1000 and BioMed Central as an Editor and Publisher, of multidisciplinary life science journals and evidence-based medicine journals, and the Current Controlled Trials clinical trial registry. He has led various initiatives, and published several articles, related to data sharing, open access, open data and the role of publishers in reproducible research. Research funders, regulators, legislators, academics and the pharmaceutical industry are working to increase transparency of clinical research data while protecting research participant privacy. Journals and publishers are also involved and some have been strengthening their policies on researchers providing access to the data supporting published results, and providing new ways to publish and link to data online. Scientific Data (Nature Publishing Group), which publishes descriptions of scientifically valuable datasets, in July 2015 launched a public consultation and published draft guidelines on linking journal articles and peer review more robustly and consistently with clinical data that are only available on request. -- Editorial: http://www.nature.com/articles/sdata201534 -- Guidance for publishing descriptions of non-public clinical datasets: http://biorxiv.org/content/early/2015/06/30/021667 More information: -- ANDS Sensitive Data Guide: http://ands.org.au/datamanagement/sensitivedata.html -- ANDS youtube playlist for Sensitive Data and Ethics: https://www.youtube.com/playlist?list=PLG25fMbdLRa5pvodHMYDi3c0LTu8N3Ks- (includes a 1min video on the benefits of publishing sensitive data)
Views: 649 Australian Research Data Commons - ARDC
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Mark Seifter In this mega-recitation, we cover Problem 1 from Quiz 1, Fall 2009. We begin with the rules and assertions, then spend most of our time on backward chaining and drawing the goal tree for Part A. We end with a brief discussion of forward chaining. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 27062 MIT OpenCourseWare
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: 14548 Microsoft Research
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: 1297 FuturICT
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: 19333 Exambin
Download PDF Here: https://goo.gl/dvt8ip For VIDEO updates follow us at ------- ► फेसबुक : https://www.facebook.com/DrishtiMediaVideos ► ट्विटर : https://twitter.com/DrishtiVideos ► इन्स्टाग्राम : https://www.instagram.com/drishtiias ► टेलीग्राम : https://t.me/drishtiiasofficial ► दृष्टि आई.ए.एस इंग्लिश यूट्यूब चैनल: https://bit.ly/2Srv0ZI ___________________________________________ The ultimate goal of Drishti IAS is to assist you in your preparations for the Civil Services Examination and meeting your expectations in this regard. Now Drishti IAS Youtube channel is ‘YONO’ for you, i.e. ‘You Need Only One’ as your civil services preparation guide. As promised, we start a new initiative-‘CURRENT NEWS’- a programme anchored by Amrit Upadhyay which is fully dedicated to upsc and state pcs exams. We all know that news is the best source for current affairs which has a huge weightage in all competitive exams. Our aim is to include all the important news events of the last week relevant from UPSC perspective. Your feedback indicates that our new venture is up to the mark to meet the needs and expectations of the aspirants. We left no stone unturned to provide all the relevant information which is crucial for the examinations, like UPSC, CSE, UPPCS, MPPCS, RPCS and UKPCS etc. In this bulletin, we are presenting the analysis of the current events that happened last week (21st Dec to 27th Dec.) Contents of this bulletin are: 1. COMPUTER SURVEILLANCE 2. DRAFT IT RULES 3. CONSUMER PROTECTION BILL 4. HEALTH SECTOR IN INDIA 5. SDG INDIA INDEX 6. NEW RULES FOR E-COMMERCE COMPANIES 7. PUBLIC CREDIT REGISTRY 8. BIMAL JALAN PANEL 9. STRATEGIC ACTION PLAN FOR MSME 10. NATIONAL SUPERCOMPUTING MISSION 11. CHINA HONGYUN PROJECT 12. ASIATIC LIONS 13. ILLEGAL POACHING OF LEOPARDS 14. CHABAHAR PORT 15. LALITGIRI ARCHAEOLOGICAL MUSEUM 16. NITI AYOG SECOND DELTA RANKING 17. TIME ZONE 18. RAT HOLE MINING 19. INDONESIA TSUNAMI 20. JAPAN TO RESUME COMMERCIAL WHALING दृष्टि आईएएस का अंतिम लक्ष्य आपकी उम्मीदों को पूरा करते हुए, सिविल सेवा परीक्षा की तैयारी में आपका सहयोग करना है। अब दृष्टि आईएएस का यूट्यूब चैनल आपकी परीक्षा की तैयारी का एक विश्वसनीय स्रोत बन चुका है। आपसे किये गए वादे के अनुसार हमने महत्त्वपूर्ण साप्ताहिक घटनाक्रमों पर आधारित ‘करेंट न्यूज’ का बुलेटिन देना शुरू किया है। आपसे मिले फीडबैक से पता चलता है कि हम आपकी उम्मीदों पर खरा उतरने में सफल रहे हैं। हम UPSC, UPPCS, MPPCS, RPCS, UKPCS जैसी परीक्षाओं में पूछे जाने वाले करेंट अफेयर्स को इस बुलेटिन के जरिये कवर करते हैं। हम एक बार फिर पिछले सप्ताह (21 से 27 दिसंबर) की कुछ महत्त्वपूर्ण खबरों के साथ आपके सामने हैं।
Views: 140749 Drishti IAS
International Journal of Database Management Systems ( IJDMS ) ISSN : 0975-5705 (Online); 0975-5985 (Print) http://airccse.org/journal/ijdms/index.html ******************************************************************* Scope & Topics The International Journal of Database Management Systems (IJDMS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the database management systems & its applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this filed, and establishing new collaborations in these areas. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Database management systems. Topics of interest include, but are not limited to, the following . Constraint Modelling and Processing . Data and Information Integration & Modelling . Data and Information Networks . Data and Information Privacy and Security . Data and Information Quality . Data and Information Semantics . Data and Information Streams . Data Management in Grid and P2P Systems . Data Mining Algorithms . Data Mining Systems, Data Warehousing, OLAP . Data Structures and Data Management Algorithms . Database and Information System Architecture and Performance . DB Systems & Applications . Digital Libraries . Distributed, Parallel, P2P, and Grid-based Databases . Electronic Commerce and Web Technologies . Electronic Government & eParticipation . Expert Systems and Decision Support Systems . Expert Systems, Decision Support Systems & applications . Information Retrieval and Database Systems . Information Systems . Interoperability . Knowledge Acquisition, discovery & Management . Knowledge and information processing . Knowledge Modelling . Knowledge Processing . Metadata Management . Mobile Data and Information . Multi-databases and Database Federation . Multimedia, Object, Object Relational, and Deductive Databases . Pervasive Data and Information . Process Modelling . Process Support and Automation . Query Processing and Optimization . Semantic Web and Ontologies . Sensor Data Management . Statistical and Scientific Databases . Temporal, Spatial, and High Dimensional Databases . Trust, Privacy & Security in Digital Business . User Interfaces to Databases and Information Systems . Very Large Data Bases . Workflow Management and Databases . WWW and Databases . XML and Databases Paper Submission: Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdms/index.html
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: 286 Health Catalyst
International Journal of Database Management Systems ( IJDMS ) ISSN : 0975-5705 (Online); 0975-5985 (Print) http://airccse.org/journal/ijdms/index.html Scope & Topics The International Journal of Database Management Systems (IJDMS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the database management systems & its applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this filed, and establishing new collaborations in these areas. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Database management systems. Topics of interest include, but are not limited to, the following . Constraint Modelling and Processing . Data and Information Integration & Modelling . Data and Information Networks . Data and Information Privacy and Security . Data and Information Quality . Data and Information Semantics . Data and Information Streams . Data Management in Grid and P2P Systems . Data Mining Algorithms . Data Mining Systems, Data Warehousing, OLAP . Data Structures and Data Management Algorithms . Database and Information System Architecture and Performance . DB Systems & Applications . Digital Libraries . Distributed, Parallel, P2P, and Grid-based Databases . Electronic Commerce and Web Technologies . Electronic Government & eParticipation . Expert Systems and Decision Support Systems . Expert Systems, Decision Support Systems & applications . Information Retrieval and Database Systems . Information Systems . Interoperability . Knowledge Acquisition, discovery & Management . Knowledge and information processing . Knowledge Modelling . Knowledge Processing . Metadata Management . Mobile Data and Information . Multi-databases and Database Federation . Multimedia, Object, Object Relational, and Deductive Databases . Pervasive Data and Information . Process Modelling . Process Support and Automation . Query Processing and Optimization . Semantic Web and Ontologies . Sensor Data Management . Statistical and Scientific Databases . Temporal, Spatial, and High Dimensional Databases . Trust, Privacy & Security in Digital Business . User Interfaces to Databases and Information Systems . Very Large Data Bases . Workflow Management and Databases . WWW and Databases . XML and Databases Paper Submission: Authors are invited to submit papers for this journal through e-mail [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdms/index.html
Views: 7 Ijdms Journal
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.
The overview of this video series provides an introduction to text analytics as a whole and what is to be expected throughout the instruction. It also includes specific coverage of: – Overview of the spam dataset used throughout the series – Loading the data and initial data cleaning – Some initial data analysis, feature engineering, and data visualization About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam-collection-dataset The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5JLp0 See what our past attendees are saying here: https://hubs.ly/H0f5JZl0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 68404 Data Science Dojo
Each year the loss of life and property due to mesoscale storms such as tornados and hurricanes is substantial. The current state of the art in predicting severe storms like tornados is based on static, fixed resolution forecast simulations and storm tracking with advanced radar. It is not good enough to accurately predict these storms with the accuracy needed to save lives. What is needed is the ability to do on-the-fly data mining of instrument data and to use the acquired information to launch adaptive workflows that can dynamically marshal resources to run ensembles simulations on-demand. These workflows need to be able to monitor the simulations and, where possible, retarget radars to gather more data to initialize higher resolution models that can focus the predictions. This scenario is not possible now, but it is the goal of the NSF LEAD project. To address these problems we have built a service oriented architecture that allows us to dynamically schedule remote data analysis and computational experiments. The Grid of resources used include machines at Indiana, Alabama, NCSA, Oklahoma, UNC and Ucar/Unidata, and soon Teragrid. The users gateway to the system is a web portal and a set of desktop client tools. Five primary persistent services are used to manage the workflows: a metadata repository called MyLEAD that keeps track of each users work, a WS-Eventing based pub-sub notification system, a BPEL based workflow engine, a web service registry for soft-state management of services and the portal server. An application factory service is used by the portal to create transient instances of the data mining and simulation applications that are orchestrated with the BPEL workflows. As the workflows execute they publish status metadata via the notification system to the users MyLEAD space. The talk will present several open research challenges that are common to many e-Science efforts.
Views: 14 Microsoft Research
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: 10637 LucidProgramming
✦ 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: 2771 LOOTastic!
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: Enrol Now for PGDDS Course: https://www.manipalprolearn.com/data-science/post-graduate-diploma-in-data-science-full-time-manipal-academy-higher-education?action 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: 122 Manipal ProLearn