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Data Mining: How You're Revealing More Than You Think
 
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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: 137212 SciShow
Data Mining College Students
 
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A university police Lieutenant wrote an article in the Chronicle for Higher Education arguing that we should begin collecting massive amounts of personal data of college students. Ana Kasparian and Jayar Jackson discuss on TYT University. https://chronicle.com/article/Mining-Student-Data-Could-Save/129231/
Views: 8324 ThinkTank
Kenneth Cukier: Big data is better data
 
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Self-driving cars were just the start. What's the future of big data-driven technology and design? In a thrilling science talk, Kenneth Cukier looks at what's next for machine learning — and human knowledge. TEDTalks is a daily video podcast of 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 much more. Find closed captions and translated subtitles in many languages at http://www.ted.com/translate Follow TED news on Twitter: http://www.twitter.com/tednews Like TED on Facebook: https://www.facebook.com/TED Subscribe to our channel: http://www.youtube.com/user/TEDtalksDirector
Views: 301624 TED
Reading Financial Statements of Mining Stocks (Part I)
 
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See our full introductory course on investing in mining stocks: http://www.informedtrades.com/f434/ In this video, I go over the basics of interpreting financial statements for mining stocks. For a much richer look at this subject, see our collection of videos on Reading Financial Statements for Stock Selection. The basic points addressed in this video are as follows: 1. The purpose of reading financial statements is to help us identify the best and most promising companies; those with the best shot at having the most profits. Such companies have the most share price appreciation and thus benefit investors accordingly. 2. Financials should be viewed in a relative context; relative to other companies in the mining sector and companies of similar sizes. Because of this, when you are looking to make investments in mining stocks, you may wish to consider first creating a large data set or spreadsheet, and then narrowing down from there. 3. Google Finance is a tool I've found very useful in researching stocks listed on US and Canadian exchanges. Just type the name of the company in and you can see their financial data. We'll discuss using their scanning tool to find companies later in this series. Here are the terms noted in this video: Market Capitalization -- the market value of the company; how much it would cost to buy up all the shares. I like to think of $1 billion to $2 billion as the "sweet spot" where stocks have proven themselves a bit and have some committment from other investors but still have great upside potential. Below $500 million are the very high reward/high risk plays, and above 20 billion are often the "blue chip" mining stocks that are very stable and yield dividends. Market capitalization gives us an instant risk profile of companies. Current Ratio -- Current assets divided by current liabilities. This is a measure of how fiscally solvent the company is; a current ratio below one suggests a company that may have trouble sustaining operations. Especially for miners that are pre-production, ensuring they have enough capital to sustain operations is vital. For the really small companies with a market capitalization of $100 million or less, I like to see a current ratio of 8 and up. Debt to Asset Ratio -- This is related to current ratio, but it accounts for long-term debt and illiquid assets as well. If a company has lots of long-term debt, that can be a problem if it needs to secure more credit in the future, and it can make unappealing as an acquisition target. Price to Book Ratio -- Take market capitalization, and divide it by the Equity amount listed on the balance sheet and you get your price to book ratio. This is basically a measurement of how much the stock is trading relative to how much its assets (property rights, mining material, etc) are worth. I like to see price to book ratios of under 3 for companies with a market capitalization of over $1 billion. Sometimes, when there are big sell-offs, you can find price to book ratios of under 2 and sometimes even under 1 -- meaning the company is selling for below what it's assets are worth. Sometimes this is a signal as to there being a larger problem with the company, but it can often be a signal of mispricing as well if what caused the sell off was an extreme move driven by an irrational panic of some kind. 4. Lastly it is worth noting that being selective is very important for those looking to succeed in stock picking. The big winners are few and far between, and so using financial statement analysis to filter out stocks is a part of the game for many stock investors. Trade US gold stocks free for 60 days with TD Ameritrade, the broker used by InformedTrades co-founder David Waring: http://bit.ly/y5lsg2
Views: 9133 InformedTrades
Data and Goliath: Bruce Schneier on the Hidden Battles to Collect Your Data and Control Your World
 
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http://democracynow.org - Leading security and privacy researcher Bruce Schneier talks about about the golden age of surveillance and his new book, "Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World." The book chronicles how governments and corporation have built an unprecedented surveillance state. While the leaks of Edward Snowden have shed light on the National Security Agency's surveillance practices, less attention has been paid to other forms of everyday surveillance — license plate readers, facial recognition software, GPS tracking, cellphone metadata and data mining Watch Part 2 of this interview: http://www.democracynow.org/blog/2015/3/13/part_2_bruce_schneier_on_the Democracy Now!, is an independent global news hour that airs weekdays on 1,300+ TV and radio stations Monday through Friday. Watch our livestream 8-9am ET: http://democracynow.org Please consider supporting independent media by making a donation to Democracy Now! today: http://democracynow.org/donate FOLLOW DEMOCRACY NOW! ONLINE: Facebook: http://facebook.com/democracynow Twitter: https://twitter.com/democracynow YouTube: http://youtube.com/democracynow SoundCloud: http://soundcloud.com/democracynow Daily Email: http://democracynow.org/subscribe Google+: https://plus.google.com/+DemocracyNow Instagram: http://instagram.com/democracynow Tumblr: http://democracynow.tumblr Pinterest: http://pinterest.com/democracynow iTunes: https://itunes.apple.com/podcast/democracy-now!-audio/id73802554 TuneIn: http://tunein.com/radio/Democracy-Now-p90/ Stitcher Radio: http://www.stitcher.com/podcast/democracy-now
Views: 4039 Democracy Now!
Data Mining: Mastering Data Mining Skills | Part - 2
 
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In this video, Qasim Ali Shah talking on the topic "DATA MINING SKILLS". In this session you will know about the content of trainers. He is giving some useful tips to all students, like: how should you can select your topic to speak effectively and after this what type of content will be helpful for your topic. You will know so many more after watching this video regarding above given topic. ===== ABOUT Qasim Ali Shah ===== Qasim Ali Shah is a Public Speaker- Teacher- Writer- Corporate Trainer & Leader for every age group- Businessmen- Corporate executives- Employees- Students- Housewives- Networkers- Sportsmen and for all who wish everlasting Success- Happiness- Peace and Personal Growth. He helps people to change their belief & thought pattern- experience less stress and more success in their lives through better communication- positive thinking and spiritual knowledge. ===== FOLLOW ME ON THE SOCIALS ===== - Qasim Ali Shah: https://goo.gl/6BKcxu - Google+: https://goo.gl/uPyGvT - Twitter: https://goo.gl/78MVoA - Website : https://goo.gl/Tgjy6u ===== Team Member: Waqas Nasir =====
Views: 7999 Qasim Ali Shah
Naive Bayes Classifier Algorithm Example Data Mining | Bayesian Classification | Machine Learning
 
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naive Bayes classifiers in data mining or machine learning are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features. Naive Bayes has been studied extensively since the 1950s. It was introduced under a different name into the text retrieval community in the early 1960s,and remains a popular (baseline) method for text categorization, the problem of judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, etc.) with word frequencies as the features. With appropriate pre-processing, it is competitive in this domain with more advanced methods including support vector machines. It also finds application in automatic medical diagnosis. for more refer to https://en.wikipedia.org/wiki/Naive_Bayes_classifier naive bayes classifier example for play-tennis Download PDF of the sum on below link https://britsol.blogspot.in/2017/11/naive-bayes-classifier-example-pdf.html *****************************************************NOTE********************************************************************************* The steps explained in this video is correct but please don't refer the given sum from the book mentioned in this video coz the solution for this problem might be wrong due to printing mistake. **************************************************************************************************************************************** All data mining algorithm videos Data mining algorithms Playlist: http://www.youtube.com/playlist?list=PLNmFIlsXKJMmekmO4Gh6ZBZUVZp24ltEr ******************************************************************** book name: techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar *********************************************
Views: 38707 fun 2 code
Introduction to Process Mining: Turning (Big) Data into Real Value
 
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With process mining, you can make your process visible in less than 5 minutes, based on log data you already have in your IT systems. Learn what process mining is, and how it works, in less than 2 minutes! (Animation work by 908video)
Views: 64664 P2Mchannel
Statistical Aspects of Data Mining (Stats 202) Day 1
 
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Google Tech Talks June 26, 2007 ABSTRACT This is the Google campus version of Stats 202 which is being taught at Stanford this summer. I will follow the material from the Stanford class very closely. That material can be found at www.stats202.com. The main topics are exploring and visualizing data, association analysis, classification, and clustering. The textbook is Introduction to Data Mining by Tan, Steinbach and Kumar. Googlers are welcome to attend any classes which they think might be of interest to them. Credits: Speaker:David Mease
Views: 213570 GoogleTechTalks
Text Mining in Publishing
 
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TEXT MINING AND SCHOLARLY PUBLISHING: This short video by John Bond of Riverwinds Consulting discusses Text Mining and the Scholarly Publishing Industry. MORE VIDEOS on TEXT MINING and Scholarly Publishing can be found at: https://www.youtube.com/playlist?list=PLqkE49N6nq3jY125di1g8UDADCMvCY1zk FIND OUT more about John Bond and his publishing consulting practice at www.RiverwindsConsulting.com SEND IDEAS for John to discuss on Publishing Defined. Email him at [email protected] or see http://www.PublishingDefined.com CONNECT Twitter: https://twitter.com/JohnHBond LinkedIn: https://www.linkedin.com/in/johnbondnj Google+: https://plus.google.com/u/0/113338584717955505192 Goodreads: https://www.goodreads.com/user/show/51052703-john-bond YouTube: https://www.youtube.com/c/JohnBond BOOKS by John Bond: The Story of You: http://www.booksbyjohnbond.com/the-story-of-you/about-the-book/ You Can Write and Publish a Book: http://www.booksbyjohnbond.com/you-can-write-and-publish-a-book/about-the-book/ TRANSCRIPT: Hi there. I am John Bond from Riverwinds Consulting and this is Publishing Defined. Today I am going to discuss text mining as it relates to scholarly publishing. Text mining also goes by the phrase text data mining or text analytics. Text mining in scholarly publishing is the process of deriving high-quality information from peer reviewed articles and other content. It does this by processing large amounts of information and looking for patterns within the data, and then evaluating and interpreting the results. Text mining is most beneficial to researchers or other power users of technical content. It is very different from a keyword search such that you might perform with Google. A key word search likely produces thousands of web links with no uniformity in the results and certainly no ability to draw meaningful conclusions. An example: let’s say you are researching bladder cancer in men and you are looking for specific biomarkers for other disease states. You probably don’t have the time to review all the literature you might find through a search at PubMed. Text mining will review the available literature. It understands the parts of speech (nouns, verbs), recognizes abbreviations, takes term frequency into account, and other natural language processes. It will filter through all the content, extracts relevant facts, spot patterns, and provides the researcher with a more condensed set of results and statements than a literature search or a cursory review of abstracts ever could. It knows bladder cancer is a disease state. It knows, in this instance, to look for men as opposed to women. It understands what a biomarker is and how to apply this term to other disease states. It understands bladder cancer is a phrase and not being used as two separate terms. Text mining software involves high level programming and such concepts as word frequency distribution, pattern recognition, information extraction, and natural language processing as well as other programming concepts well beyond the scope of this video. The overall goal is to turn text into data for analysis and thereby help to draw conclusions. However, the results of text mining in and of themselves is not the end product, just part of the process. Individual text mining tools or enterprise level ones have become more common with researchers, librarians, and large for profit and not for profit organizations, and they will only grow. Aside from a text mining tool, an application is also necessary to check that the content being mined is licensed and to provide appropriate links to the content. Text mining is important to publishers or any group that holds large stores of full text articles or databases because this information as a whole has greater value than each individual part. Text mining can help extract that value. A key point for publishers is that the text mining tool and its user, such as a researcher, needs to have access to the content either by it being open access, through a subscription, or through a purchase. Subscription publishers see revenue when content is accessed or purchased. All publishers see article downloads and page views from text mining efforts. Either way, text mining as a tool in research, in medicine, in pharmaceutical R&D will only continue to grow in importance. Well that’s it. Please subscribe to my YouTube channel or click on the playlist to see more videos about text mining in scholarly publishing. And make comments below or email me with questions. Thank so much and take care.
Views: 262 John Bond
Get cheaper flights online with a VPN!
 
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Sign up for Private Internet Access VPN and start saving money on YOUR flights at https://www.privateinternetaccess.com/pages/linus-tech-tips/linus2 Discuss on the forum: coming soon Our Affiliates, Referral Programs, and Sponsors: https://linustechtips.com/main/topic/... Linus Tech Tips merchandise at http://www.designbyhumans.com/shop/Li... Linus Tech Tips posters at http://crowdmade.com/linustechtips Our Test Benches on Amazon: https://www.amazon.com/shop/linustech... Our production gear: http://geni.us/cvOS Twitter - https://twitter.com/linustech Facebook - http://www.facebook.com/LinusTech Instagram - https://www.instagram.com/linustech Twitch - https://www.twitch.tv/linustech Intro Screen Music Credit: Title: Laszlo - Supernova Video Link: https://www.youtube.com/watch?v=PKfxm... iTunes Download Link: https://itunes.apple.com/us/album/sup... Artist Link: https://soundcloud.com/laszlomusic Outro Screen Music Credit: Approaching Nirvana - Sugar High http://www.youtube.com/approachingnir... Sound effects provided by http://www.freesfx.co.uk/sfx/
Views: 659901 Linus Tech Tips
Fiance Visa Denial due to Social Media Data Mining
 
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http://www.visacoach.com/visa-denial-social-media-data-mining.html USCIS recently hired contractors to research social media to provide additional data for the extreme vetting of Fiance, Spouse and other visa applicants. Expect one’s social media “skeletons” to lead to denial. To Schedule your Free Consultation with Fred Wahl, the Visa Coach visit http://www.visacoach.com/talk.html or Call - 1-800-806-3210 ext 702 or 1-213-341-0808 ext 702 Bonus eBook “5 Things you Must Know before Applying for your Visa” get it at http://www.visacoach.com/five.html Fiancee or Spouse visa, Which one is right for you? http://imm.guru/k1vscr1 What makes VisaCoach Special? Ans: Personally Crafted Front Loaded Presentations. Front Loaded Fiance Visa Petition http://imm.guru/front Front Loaded Spouse Visa Petition http://imm.guru/frontcr1 K1 Fiancee Visa http://imm.guru/k1 K1 Fiance Visa Timeline http://imm.guru/k1time CR1 Spousal Visa http://imm.guru/cr1 CR1 Spouse Visa Timeline http://imm.guru/cr108 Green Card /Adjustment of Status http://imm.guru/gc USCIS recently announced new contracts given to companies to search through social media to collect data on Fiance and other visa applicants. Collection starts October 18. If you have any "suspect" exposure, you have only a few more days to take it down. One of VisaCoach's clients has already experienced denial due to his Facebook presence. This couple's case was as near perfect as we have seen. They were young and in love. They had known each other for a few years and had met more than once. They were evenly matched by age, values and religion. Their "front loaded" petition was awesome and included many solid evidences of their bona fides. The American sponsor even accompanied his fiancé to the interview to demonstrate his sincerity and support for the petition. After a brief interview where the sponsor was not allowed to join in nor asked any questions before, during or after, the consular officer, denied the case. The couple was devastated and confused. What could have gone wrong? A consular officer who exhibits professionalism will state the reasons for denial in writing. And provide this to the rejected applicant immediately, often at the close of the interview itself. You may not agree with the decision, but at least know what it was and then have a starting point for renewed efforts. The officer refused to provide any verbal or written explanation. All the couple had was the fiancee's memory of the interview. I asked her to write a transcript of what happened, to recall exactly what was said and even what the body language was, so that we could study this in an attempt to reconstruct what MIGHT have been in the consular officer’s mind. What seemed odd and out of context, was the consular officer made some comments about "conservative values" and what is a "woman's role in society and in the home". Those comments seemed rather strange at the time and the foreign born fiancé had no idea where those comments came from. Eventually it dawned on us. The American sponsor is active on FaceBook. He is outspoken and his views are somewhat "anti feminist". He had posted on his social media pages, and entered into many online debates, his ideas on conservative values, and HIS ideas about a women's role in the home and society. He is not a bad guy. Not a bad husband. He was just expressing his free speech. He just had some strong views that are not popular, that are not considered "politically correct". The consular officer did her own internet search, found his activity and "Was NOT amused", and denied, putting this loving couple's life's on hold. Was it fair or reasonable that they were denied?. No, I don't think so. Happy end to the story. We took down his Facebook account, reapplied, and six months later they had their visa and began their married life together in Alaska. One random consular officer searching on Facebook ended in a denial. What will happen when ALL Fiance and Spouse applications are accompanied by a detailed dossier of one's online statements, comments jokes, embarrassments, positive and negative feedback from friends or trolls? Expect disaster. Expect many more denials, simply due to exercising a US Citizen's right to free speech. In Conclusion: "Freedom of Speech", doesn't mean freedom to get your visa. The prudent path is prior to applying for a Fiance or Spouse visa to make sure there are no skeletons in your online closet. Clean or temporarily remove, or make private, potentially controversial aspects of your online and public presence before proceeding with your visa application.
Views: 10985 Visa Coach
What the Heck Does “Data Science” Really Mean? The Dr. Data Show
 
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In this episode of The Dr. Data Show, Eric Siegel answers the question, "What the heck do 'data science' and 'big data' really mean?" Sign up for future episodes and more info: http://www.TheDoctorDataShow.com Attend Predictive Analytics World: http://www.pawcon.com Read Dr. Data's book: http://www.thepredictionbook.com Welcome to "The Dr. Data Show"! I'm Eric Siegel. “Data science.” “Big data.” What the hell do these buzzwords really, specifically mean? Are they just cockamamie -- intentionally vague jargon that overhypes and overpromises? Or are these terms actually helpful -- do they somehow designate, like, the most profound impact of the Information Age? Well, I’ll start with the vague and overhyping side and then circle back to why these buzzwords may matter after all. It’s time for the Dr. Data buzzword smackdown. There are a lotta problems with these words. First, "data scientist" is redundant. It's like calling a librarian a "book librarian." If you're doing science, it involves data. Duh! Furthermore, don't tell anyone I said this, but real sciences like physics and chemistry don't have "science" in their name. Your science is trying too hard if it has to call itself a science: Social science, political science, data science, and I gotta say -- even though I have three degrees in it and was a professor of it -- computer science is an arbitrarily defined field. It's just the amalgam of everything to do with computers -- as a concept and as an appliance -- from the engineering of how to build them and the deep mathematics about their theoretical limitations to how to make them more user friendly, and even business strategies for managing a team of programmers... Universities might as well also have a "toaster science" department, which covers the engineering of better toasters as well as the culinary arts on how to best cook with them. But I digress. Ok, next buzzword: “Big data.” First of all, it's just grammatically incorrect. It’s like looking at the Pacific Ocean and saying “big water.” It should be “a lotta data” or “plenty of data.” But the real problem with "big data" is that it emphasizes the size. 'Cause what’s exciting about data isn't how much of it there is per se -- it's about how quickly it's growing -- which is amazing by the way. There’s always so much more data today than there was yesterday. So we're gonna run out of adjectives really quickly: “big data,” “bigger data,” “even bigger data,” “the biggest data.” Actually, there’s been a long-running conference called the International Conference on Very Large Databases since 1975. I’m not joking. That's before the first Star Wars movie came out! Now, in some cases, people use the terms data science and big data just to refer to machine learning, i.e., when computers learn from the experience encoded in data. That's the topic of most episodes of this program, The Dr. Data Show. It’s a show about machine learning -- which is a well-defined field and by the way is also often called predictive analytics, especially when you're talking about its deployment in the private or public sector. I would urge folks to use the well-defined terms machine learning or predictive analytics if in fact that's what you’re specifically talking about. But as for data science and big data, in their general usage they suffer from a terrible case of vagueness. The have a wide range of subjective definitions, which compete and conflict. Basically, they're often used to mean nothing more specific than "some clever use of data." The terms don't necessarily refer to any particular technology, method, or value proposition. They're just plain subjective -- you can use them to mean whichever technology you'd like: machine learning, data visualization, or even just basic reporting. But much worse than that, this vagueness often serves to mislead and misrepresent by alluding to capabilities that don't exist. For example, the popular press -- as well certain analytics vendors -- sometimes use "data science" to denote some whole collection of methods that includes machine learning as well as some other advanced methods. The problem is, those other advanced methods are implied but often actually just don't really exist. They're vaporware. This confusion is sometimes inadvertent -- such as when journalists aren’t fully knowledgeable of the topic yet want it to sound as powerful as possible -- but, either way, the end result is souped-up hype that overpromises and circulates misinformation. All these issues, by the way, also apply to the older-school term "data mining," also totally subjective. Besides, calling it "data mining" is like instead of "gold mining," saying “dirt mining.” Malfunction, failed analogy... 'Cause we aren't searching for data, we're searching within data... For the complete transcript and more: http://www.TheDoctorDataShow.com
Views: 363 Eric Siegel
12. Clustering
 
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag discusses clustering. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 66505 MIT OpenCourseWare
Scammed on ebay... Testing the 56 CORE system!
 
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Thanks so much for Audible for sponsoring this video! Get Your FREE 30 Day Audible Trial Today! Head to http://audible.com/linus or text Linus to 500-500. Enter our competition to win a free 6-month subscription by Tweeting @LinusTech screenshots of your favorite Audio Books! #NewYearNewMe Buy Intel Xeon processors on Amazon: http://geni.us/9deQri Discuss on the forum: https://linustechtips.com/main/topic/890948-scammed-on-ebay-testing-the-56-core-system/ Our Affiliates, Referral Programs, and Sponsors: https://linustechtips.com/main/topic/75969-linus-tech-tips-affiliates-referral-programs-and-sponsors Linus Tech Tips merchandise at http://www.designbyhumans.com/shop/LinusTechTips/ Linus Tech Tips posters at http://crowdmade.com/linustechtips Our production gear: http://geni.us/cvOS Twitter - https://twitter.com/linustech Facebook - http://www.facebook.com/LinusTech Instagram - https://www.instagram.com/linustech Twitch - https://www.twitch.tv/linustech Intro Screen Music Credit: Title: Laszlo - Supernova Video Link: https://www.youtube.com/watch?v=PKfxmFU3lWY iTunes Download Link: https://itunes.apple.com/us/album/supernova/id936805712 Artist Link: https://soundcloud.com/laszlomusic Outro Screen Music Credit: Approaching Nirvana - Sugar High http://www.youtube.com/approachingnirvana Sound effects provided by http://www.freesfx.co.uk/sfx/
Views: 5573549 Linus Tech Tips
Data Preprocessing Steps for Machine Learning & Data analytics
 
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#Pandas #DataPreProcessing #MachineLearning #DataAnalytics #DataScience Data Preprocessing is an important factor in deciding the accuracy of your Machine Learning model. In this tutorial, we learn why Feature Selection , Feature Extraction, Dimentionality Reduction are important. We also learn about the famous methods which can be used for the purpose. Data Preprocessing is a very important step in Data Analytics which is ignored by many. To make your models accurate you have to ensure proper preprocessing as the Machine Learning model is highly dependent on data. For all Ipython notebooks, used in this series : https://github.com/shreyans29/thesemicolon Facebook : https://www.facebook.com/thesemicolon.code Support us on Patreon : https://www.patreon.com/thesemicolon Python for Data Analysis book : http://amzn.to/2oDief8 Pattern Recognition and Machine Learning : http://amzn.to/2p6mD6R
Views: 9433 The SemiColon
How to Get Amazon.com Book Reviews | Book Coach Guru Steven E's Tips for Online Amazon book selling
 
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http://www.bestsellerguru.com – In this video, I'll show you how to get Amazon.com book reviews and how it can be a great way to market your book. ***************************** I can't understand all the negative reviews! this book literally contains everything i could ever ask for in a. Amazon customer reviews a million random digits with. There are 16 amazon's new review rules the changes and how they affect authors. Jeff bezos and amazon's book review policy is destroying indie amazon books 135 photos & 72 reviews bookstores 4601 26th data. Donald trump supporters trolling megyn kelly's new book with amazon reviews deleted in a purge aimed at manipulation professor who harassed ivanka on airplane hit bad all. 335 book reviews for 8 different books. I don't necessarily 26 amazon has been accused of fixing the reviews for hillary clinton's campaign book (above, clinton with on september 19). Uk book store featuring critically acclaimed books, new check back regularly to find your next favourite bookbooks for people who want take over the world amazon review logo, omnivoracious, and hungry good are trademarks of 16 we all more reviews but until you have a huge readership at time writing, 1 top customer reviewer on has only 6 imagine breaking writing memoir with dream getting it published. Amazon best books of the month. Free book reviews amazon. Full review 17 the amazon page for fox news star's book 'settle more' had hundreds of one star reviews within hours its release tuesday, with 22 amazon's decision to delete thousands has generated an uproar, but company not offered a public explanation 25 matthew lasner, hunter college professor, seen his peppered and wry comments on book, high this dataset is obsolete; Please see our updated jmcauley. Recipe for spanokopita? Check! name of every important before you can post a review, need to have an amazon account upload video, or if prefer the old review page book reviews. Amazon accused of removing negative reviews hillary clinton's. I love books, and i well. Then your book agent receives 50 rejections from hot 72 reviews of amazon books 'amazon it pretty awesome! i know there aren't many bookstores around now days but this place takes the cake dataset contains product and metadata amazon, including music is at times hard to read because we think was published for 16 data set download folder, description. If you are welcome to the amazon. 16 hilariously inappropriate amazon reviews buzzfeed. Edu data amazon review will be made available (for research purposes 12 while i was in seattle last week, sure to stop by the original books check it out. Uk's book storehow to get amazon's top customer reviewers review your. Uci machine learning repository amazon book reviews data set. We scour reviews and book news, we swap books amongst ourselves, spend our nights weekends tearing through as many of the best amazon's top customer reviewers have helped millions their fellow take a minute to explore written by these customersfree is blog that indie books, interviews authors generally talks about whatever amuses them in literary world. Five star reviews there was no need to devote so much of the book that guy. Amazon's new review rules what authors need to know. Last year some dedicated book bloggers found all their reviews had 27 16 hilariously inappropriate amazon. Amazon books seattle bookstore review business insider. Amazon customer reviews a million random digits with amazon help submit review.
Views: 1351 Steven E Schmitt
Data Mining with Weka (2.2: Training and testing)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 2: Training and testing http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 68599 WekaMOOC
Part 2: Bruce Schneier on the Hidden Battles to Collect Your Data and Control Your World
 
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http://democracynow.org - Leading security and privacy researcher Bruce Schneier talks about about the golden age of surveillance and his new book, "Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World." The book chronicles how governments and corporation have built an unprecedented surveillance state. While the leaks of Edward Snowden have shed light on the National Security Agency's surveillance practices, less attention has been paid to other forms of everyday surveillance — license plate readers, facial recognition software, GPS tracking, cellphone metadata and data mining. Watch Part 1 of this interview: http://www.democracynow.org/2015/3/13/data_and_goliath_bruce_schneier_on Democracy Now!, is an independent global news hour that airs weekdays on 1,300+ TV and radio stations Monday through Friday. Watch our livestream 8-9am ET: http://democracynow.org Please consider supporting independent media by making a donation to Democracy Now! today: http://democracynow.org/donate FOLLOW DEMOCRACY NOW! ONLINE: Facebook: http://facebook.com/democracynow Twitter: https://twitter.com/democracynow YouTube: http://youtube.com/democracynow SoundCloud: http://soundcloud.com/democracynow Daily Email: http://democracynow.org/subscribe Google+: https://plus.google.com/+DemocracyNow Instagram: http://instagram.com/democracynow Tumblr: http://democracynow.tumblr Pinterest: http://pinterest.com/democracynow iTunes: https://itunes.apple.com/podcast/democracy-now!-audio/id73802554 TuneIn: http://tunein.com/radio/Democracy-Now-p90/ Stitcher Radio: http://www.stitcher.com/podcast/democracy-now
Views: 1579 Democracy Now!
Tenses | Basic English Grammar in Hindi (all 12 parts of tenses)explanation in hindi by Sanjeev sir
 
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I Recommend this book for practice :- http://amzn.to/2gzNYiG (affiliate) FOR ANY QUERY ABOUT RULES AND METHOD whatsapp [email protected] mail me @ [email protected] follow me on [email protected]://www.facebook.com/be.banker.1 follow me on [email protected] follow me on :- instagram https://www.instagram.com/be_banker/?hl=en --------------------------------------------------------------------------------------------------------- -------------------------------------------------------------------------------------------------------- FOR MORE IMPORTANT VIDEOS: (1) VEDIC MULTIPLICTION ⇒ https://www.youtube.com/watch?v=hQmEg37eZw0 (2)VEDIC MULTIPLICTION ⇒https://www.youtube.com/watch?v=pw4o0EyX7OI (3)VEDIC MULTIPLICTION ⇒https://www.youtube.com/watch?v=_4LqeC3nFKQ (4))VEDIC MULTIPLICTION ⇒https://www.youtube.com/watch?v=jv-KDCzcJ_Y (5)SQUARE ROOT IN 3 SECOND⇒https://www.youtube.com/watch?v=10Hl6TWglmk (6)CUBE ROOT IN 5 SECOND⇒https://www.youtube.com/watch?v=jZrL32TetgU (7)5 TIPS TO BE BANK PO⇒https://www.youtube.com/watch?v=GNZAUswpVm4 -------------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------------- BE BANKER:-https://www.youtube.com/c/bebanker THIS CHANNEL IS ALL ABOUT STUDY, THAT IS FOR BANK AND OTHER SIMILAR COMPETITIVE EXAMS ,WHICH MIGHT HELP TO SCHOOL GOERS,COLLEGE GOERS AND ONE WHO WANTS TO LEARN... THIS CHANNEL PROVIDES TIPS, TRICKS, STRATEGIES AND OTHER STUDY STUFFS ... SO BE WITH BE BANKER :) ----------------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------------- --------------------------------------------------------------------------------------------------------------IT is an initiative by BY SANJEEV SIR to assist students who cannot afford costly coaching or require some more time to understand the concept taught in huge size class rooms. Students who are preparing for Government Jobs SSC, Banking, IBPS, SBI, Clerical, Probationary Officer, PO, RRB, Railways, Apprentice, LIC, FCI, Army, Airforce, AFCAT, NDA, CDS, MBA Entrance Exams , CAT, XAT , IIFT, IRMA, NMAT, MHCET, CMAT, MAT, ATMA, BBA, CLAT, LSAT, HOTEL MANAGEMENT, NTSE, OLYMPIADS, MCA, NIMCET, HTET, CTET , IIT, JEE have access to Qualitative and Comprehensive Video Sessions of on Quantitative Aptitude ( Maths), Reasoning ( Verbal and Nonverbal), English ( Grammar, Vocabulary, Comprehension etc ) General Knowledge, Data Interpretation, Data Analysis, Data Sufficiency, Current Affairs FREE OF COST on this channel.
Views: 5770916 BE BANKER
Python Text Mining with nltk
 
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Link to our course :  http://rshankar.com/courses/autolayoutyt7/ In this course, we have been looking at Regular expressions, a tool that helps us mine text but in this video i wish to give you a flavor of a Python package called nltk. Since this course is about finding patterns in text, it is only fair that you know about another package that offers a lot of help in this direction. Reference: https://www.nltk.org/ https://en.wikipedia.org/wiki/Text_mining https://www.deviantart.com/sirenscall/art/The-Highwayman-26312892 https://www.deviantart.com/enricogalli/art/Moby-Dick-303519647 Images courtesy: Designed by Freepik from www.flaticon.com Script: If you look at jobs advertised for data analysts or data scientists, you will often come across the term - text mining It is the process of deriving useful information from text. Text mining is in itself a fascinating subject and involves tasks such as text classification, text clustering, sentiment analysis and much more. The goal of text mining is to turn text into data for analysis. In this course, we have been looking at Regular expressions, a tool that helps us mine text but in this video i wish to give you a flavor of a Python package called nltk. Since this course is about finding patterns in text, it is only fair that you know about another package that offers a lot of help in this direction. nltk stands for the natural language toolkit and is an open source community driven project. nltk helps us build Python programs to work with human language data. So for example if you wish to create a spam detection program, or movie review program, nltk offers a lot of helper functions. The goal of this video to inform you that such a package exists and show you some basic functionality. If you like what you see, do let me know and I will add more videos on this subject. So we will start with a new Jupyter notebook. I already have the nltk package . If you do not, you will need to get it, please. nltk comes with some example books. We can import these books or corpora as follows. Perhaps some of these titles may be familiar to you. So lets take Moby Dick. Its data is stored in a Text object. Can we find how many words the book contains? Ok, now how about unique words? Hmm. Less than 10 percent of the total words. An interesting thing we may wish to do is examine the frequency of words. This is often done with speeches of various politicians. So for example you may wish to see the most frequent words spoken by a politician before an election and the frequency after elections. So lets import FreqDist and assign to it the text of Moby Dick. So the keys of this object are all the words and we can see the values which are the frequency of the words. Moby Dick is a story of a whale. Lets see how many times this word figures in the book. The keys are case sensitive of course. Let us now focus on popular words in the book. But not words such as ‘has’ or ‘the’ So lets say we want to find the words of length greater than 6 which appear more than 100 times in the book. And lets sort these words for good measure. Interesting set of words. Some such as Captain would be expected i guess. Lets come back to a topic we have seen before - Word tokenization. So we have our sentence like so. And we want to break this sentence into various tokens or words. Earlier we used the function split() so lets do that again. As you can see, the output in this case bundles the full stop with a word. Also what about the word shouldn’t. Is it one token or 2? nltk provides a function that is more language syntax aware. Lets use it. I will leave you to evaluate the differences. One last thing. Here we have a slice of a wonderful poem called the HighwayMan. Now we wish to break this text into its sentences. Can we do it? Regular expressions can help but why use Regex when we have a solution. nltk offers a sent_tokenize function. Lets use it. Isn’t this poem beautiful.. Ok guys thats it for now. If you want more videos on this subject do let me know. Take care.
Views: 110 funza Academy
MMIS 643 Data Mining Assignment 3 Solutions
 
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A. A neural network typically starts out with random coefficients (weights); hence, it produce essentially random predications when presented with its first case. What is the key ingredients by which the net (neural network) evolves to produce a more accurate predication? (Please answer your question as clearly and concisely as possible.) (10 points) B. Consider the Boston Housing Data file (The schema of the data file is given on page 27 in Table 2.2 of the textbook.). (40 points) a. Study the Neural Networks Prediction example from the URL: http://www.solver.com/xlminer/help/neural-networks-classification-intro, and following the example step by step. b. Using XLMINER’s neural network routine to fit a model using XLMINER default values for neural network parameters by using the predictors such as CRIM, ZN, INDUS, CHAS, NOX, RM, AGE, DIS, RAD to classify the value of CAT.MEDV. i. Record the RMS errors for the training data and the validation data, and observe the lift charts for repeating the process, changing the number of epochs to 300, 3000, 10,000, 20,000. ii. What happens to RMS error for the training data set as the number of epochs increases? iii. What happens to RMS error for the validation data set as the number of epochs increases? iv. Comments on the appropriate number of epochs for the model. Note: (Please use the Prediction Option of the Neural Network in order to get RMS) C. For Association Rule Mining, please define the following terms: (10 points) a. Support b. Confidence c. Lift D. Study the Association Mining example from the URL: http://www.solver.com/xlminer/help/associationrules. E. Problem 13. 3 on page 277-278 of the textbook, Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner, 2 edition, 2010, by Galit Shmueli, Nitin R. Patel, and Peter C. Bruce, ISBN: 978-0-470-52682-8. The data file is attached. (40 points) Note: 1. The data files are posted along Written Assignment #3.
Views: 331 Libraay Downloads
Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World
 
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Leading security and privacy researcher Bruce Schneier talks about about the golden age of surveillance and his new book, "Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World." The book chronicles how governments and corporation have built an unprecedented surveillance state. While the leaks of Edward Snowden have shed light on the National Security Agency’s surveillance practices, less attention has been paid to other forms of everyday surveillance — license plate readers, facial recognition software, GPS tracking, cellphone metadata and data mining. For full episodes of Democracy Now! click the link: http://freespeech.org/collection/democracy-now
Views: 234 freespeechtv
Data mining
 
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Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amount of data, not the extraction of data itself. It also is a buzzword, and is frequently also applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The popular book "Data mining: Practical machine learning tools and techniques with Java" (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis", or "analytics" -- or when referring to actual methods, artificial intelligence and machine learning -- are more appropriate. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 1624 Audiopedia
EmoText for opinion mining in long texts
 
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http://socioware.de https://www.researchgate.net/publication/278383087_Opinion_Mining_and_Lexical_Affect_Sensing EmoText for opinion mining in long texts illustrates a domain-independent approach to opinion mining. A thorough description is available in the book "Opinion mining and lexical affect sensing". Empirically revealed that texts should contain not less than 200 words for reliable classification. The engine evaluates features (lexical, stylometric, grammatical, deictic) using different evaluation methods and uses the SMO or NaiveBayes classifiers from the WEKA data mining toolkit for text classification. Statistical EmoText formed a basis for the statistical framework for experimentation and rapid prototyping. The approach was tested on the following English corpora: a Pang corpus with weblogs, Berardinelli movie review corpus with movie reviews, a corpus with spontaneous dialogues (the SAL corpus), and a corpus with product reviews.
Views: 964 Alexander Osherenko
Data Mining with Weka (1.1: Introduction)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 1: Introduction http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 118446 WekaMOOC
How to find median of a continuous frequency distribution ?
 
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Learn how to find the median of a continuous frequency distribution from this video. To view more Educational content, please visit: https://www.youtube.com/appuseriesacademy To view Nursery Rhymes, please visit: https://www.youtube.com/appuseries To view Content in other Languages, please visit: www.youtube.com/appuseries + "Language". For Example for Hindi content: www.youtube.com/appuserieshindi To Buy Books and CDs, please visit : https://www.appuseries.com
Views: 183867 AppuSeriesAcademy
Jose van Djick - Social Media and the Culture of Connectivity
 
01:24:54
In less than a decade, social media platforms such as Facebook, YouTube, Twitter and LinkedIn have come to deeply penetrate our daily habits of communication and socializing. While most sites started out as amateur-driven community platforms, virtually all have turned into large corporations that do not just facilitate global connections, but have become global data mining companies. This lecture will reflect on how social media have become normalized in everyday life. What has become the meaning of social activities such as "sharing," "liking," "following," and "trending" in a world dominated by Facebook and Twitter? And what are the implications of the fact that large portions of everyday life are increasingly commercialized and engineered through social media? Facebook's and Twitter's algorithms do not simply reflect our behavior and habits, but actively steer and manipulate social activities. At the heart of the social media's industry's surge is the battle over information control: who owns the data generated by online social activities? The lecture addresses the question of user power in the ecosystem of connective media.
The Agile Future of HR and Talent Acquisition - Prof. Dr. Armin Trost
 
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Presentation by Prof. Dr. Armin Trost, Author, Consultant and Professor at Furtwangen University, held at Textkernel's conference "Intelligent Machines and the Future of Recruitment" on 2 June 2016 in Amsterdam. Human resource management in the 21st century will have little to do with what has been promoted in recent years or decades and written in the text-books. Instead of finding “the right people, at the right time and at the right place” we will make the employees and their individual preferences, talents, life plans, and ambitions the focus of attention. We will say goodbye to mechanistic, technocratic, and often bureaucratic approaches. They fit in a past that was stable and predictable. If you regard your employees as your most valuable asset, you will give them freedom, trust, and responsibility. Moreover you will appreciate individuality and individual life-plans. Human resources management will therefore deal less with hierarchical processes, systems, responsibilities, KPIs, etc., in the future. Rather, it will be about how to empower teams to think on their own responsibility, communicate, collaborate, learn, and develop their talent in the long term. HR-Technology will be there to make the life of managers and employees easier instead of supporting the HR-function only. For instance, in the area of recruiting all this will lead to a more intense usage of social networks, artificial intelligence, big data, data mining etc.
Views: 13317 Textkernel
Nominal, ordinal, interval and ratio data: How to Remember the differences
 
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Learn the difference between Nominal, ordinal, interval and ratio data. http://youstudynursing.com/ Research eBook on Amazon: http://amzn.to/1hB2eBd Check out the links below and SUBSCRIBE for more youtube.com/user/NurseKillam For help with Research - Get my eBook "Research terminology simplified: Paradigms, axiology, ontology, epistemology and methodology" here: http://www.amazon.com/dp/B00GLH8R9C Related Videos: http://www.youtube.com/playlist?list=PLs4oKIDq23AdTCF0xKCiARJaBaSrwP5P2 Connect with me on Facebook Page: https://www.facebook.com/NursesDeservePraise Twitter: @NurseKillam https://twitter.com/NurseKillam Facebook: https://www.facebook.com/laura.killam LinkedIn: http://ca.linkedin.com/in/laurakillam Quantitative researchers measure variables to answer their research question. The level of measurement that is used to measure a variable has a significant impact on the type of tests researchers can do with their data and therefore the conclusions they can come to. The higher the level of measurement the more statistical tests that can be run with the data. That is why it is best to use the highest level of measurement possible when collecting information. In this video nominal, ordinal, interval and ratio levels of data will be described in order from the lowest level to the highest level of measurement. By the end of this video you should be able to identify the level of measurement being used in a study. You will also be familiar with types of tests that can be done with each level. To remember these levels of measurement in order use the acronym NOIR or noir. The nominal level of measurement is the lowest level. Variables in a study are placed into mutually exclusive categories. Each category has a criteria that a variable either has or does not have. There is no natural order to these categories. The categories may be assigned numbers but the numbers have no meaning because they are simply labels. For example, if we categorize people by hair color people with brown hair do not have more or less of this characteristic than those with blonde hair. Nominal sounds like name so it is easy to remember that at a nominal level you are simply naming categories. Sometimes researchers refer to nominal data as categorical or qualitative because it is not numerical. Ordinal data is also considered categorical. The difference between nominal and ordinal data is that the categories have a natural order to them. You can remember that because ordinal sounds like order. While there is an order, it is also unknown how much distance is between each category. Values in an ordinal scale simply express an order. All nominal level tests can be run on ordinal data. Since there is an order to the categories the numbers assigned to each category can be compared in limited ways beyond nominal level tests. It is possible to say that members of one category have more of something than the members of a lower ranked category. However, you do not know how much more of that thing they have because the difference cannot be measured. To determine central tendency the categories can be placed in order and a median can now be calculated in addition to the mode. Since the distance between each category cannot be measured the types of statistical tests that can be used on this data are still quite limited. For example, the mean or average of ordinal data cannot be calculated because the difference between values on the scale is not known. Interval level data is ordered like ordinal data but the intervals between each value are known and equal. The zero point is arbitrary. Zero simply represents an additional point of measurement. For example, tests in school are interval level measurements of student knowledge. If you scored a zero on a math test it does not mean you have no knowledge. Yet, the difference between a 79 and 80 on the test is measurable and equal to the difference between an 80 and an 81. If you know that the word interval means space in between it makes remembering what makes this level of measurement different easy. Ratio measurement is the highest level possible for data. Like interval data, Ratio data is ordered, with known and measurable intervals between each value. What differentiates it from interval level data is that the zero is absolute. The zero occurs naturally and signifies the absence of the characteristic being measured. Remember that Ratio ends in an o therefore there is a zero. Typically this level of measurement is only possible with physical measurements like height, weight and length. Any statistical tests can be used with ratio level data as long as it fits with the study question and design.
Views: 317302 NurseKillam
58011070 Data Mining : Data Visualization
 
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นางสาวลลิตา สรวมชีพ 58011070 sec 2 Data Mining 01236057
Lecture - 34 Data Mining and Knowledge Discovery
 
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Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 133206 nptelhrd
Portfolio Plus Prospector | Data Mining Analytics for Loan Business
 
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Cars on Credit relies on Portfolio Plus Prospector, an analytics and data mining tool, to run their car loan business effectively.
Shyam Sankar: The rise of human-computer cooperation
 
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Brute computing force alone can't solve the world's problems. Data mining innovator Shyam Sankar explains why solving big problems (like catching terrorists or identifying huge hidden trends) is not a question of finding the right algorithm, but rather the right symbiotic relationship between computation and human creativity. TEDTalks is a daily video podcast of 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). TED stands for Technology, Entertainment, Design, and TEDTalks cover these topics as well as science, business, global issues, the arts and more. Find closed captions and translated subtitles in a variety of languages at http://www.ted.com/translate. Follow TED on Twitter: http://www.twitter.com/tednews Like TED on Facebook: https://www.facebook.com/TED If you have questions or comments about this or other TED videos, please go to http://support.ted.com
Views: 51188 TED
Data Mining Skills - ( Part 3 ) By Qasim Ali Shah Students
 
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About Qasim Ali Shah, Qasim Ali Shah is a Public Speaker- Teacher- Writer- Corporate Trainer & Leader for every age group- Businessmen- Corporate executives- Employees- Students- Housewives- Networkers- Sportsmen and for all who wish everlasting Success- Happiness- Peace and Personal Growth. He helps people to change their belief & thought pattern- experience less stress and more success in their lives through better communication- positive thinking and spiritual knowledge. Facebook Page. https://www.facebook.com/m.adil081 Twitter, https://twitter.com/madil081 Instagram, https://www.instagram.com/m.adil081/ Youtube, https://www.youtube.com/channel/UCCdzENcuIZq-2IDgokQw-yg Google+, https://plus.google.com/u/0/115997488022496813711 Tune.pk, https://tune.pk/user/QasimAliShahStudents Web, https://madil081.blogspot.com/ Web 2 https://madil081.tumblr.com/
More Data Mining with Weka (2.1: Discretizing numeric attributes)
 
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More Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 1: Discretizing numeric attributes http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/QldvyV https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 18357 WekaMOOC
Mod-01 Lec-02 Data Mining, Data assimilation and prediction
 
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Dynamic Data Assimilation: an introduction by Prof S. Lakshmivarahan,School of Computer Science,University of Oklahoma.For more details on NPTEL visit http://nptel.ac.in
Views: 1683 nptelhrd
Why Did IBM buy Netezza for $17.1 Billion?
 
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http://www.TheInternetTimeMachine.com looks at the data mining topic of text analytics and why it is so valuable in todays online marketing world.
Views: 6778 tldsvsm15
Moneyball (2011) Movie Trailer - HD - Brad Pitt
 
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The story of Oakland A's general manager Billy Beane's successful attempt to put together a baseball club on a budget by employing computer-generated analysis to draft his players. Director: Bennett Miller Writers: Steven Zaillian (screenplay), Aaron Sorkin (screenplay) Stars: Brad Pitt, Robin Wright and Jonah Hill Via: http://movies.yahoo.com/trailers/
Views: 2349466 Movieclips Trailers
Data Privacy Is a Fundamental Human Need & Essential for Individual Autonomy
 
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This video is a short excerpt from the Democracy Now! interview with security and privacy researcher Bruce Schneier. Watch it: http://owl.li/KifBK Bruce Schneier talks about about the golden age of surveillance and his new book, "Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World." The book chronicles how governments and corporation have built an unprecedented surveillance state. While the leaks of Edward Snowden have shed light on the National Security Agency's surveillance practices, less attention has been paid to other forms of everyday surveillance — license plate readers, facial recognition software, GPS tracking, cellphone metadata and data mining. AMY GOODMAN: Governments tell us, "If you have nothing to hide, you have nothing to fear." Why should you be concerned about government surveillance, Bruce? BRUCE SCHNEIER: Well, I mean, that’s ridiculous on the face of it. Those same government officials who say that don’t tell you all of their secrets, give you copies of all of their emails and correspondence. Privacy is not about something to hide. Privacy isn’t something that you only have if you’re a criminal. Privacy is about individual autonomy. It’s about presenting yourself to the world. It’s about being in charge of what you say about yourself and what you reveal about yourself. When we’re private, we have control of our person. When we’re exposed, when we’re surveilled, we’re stripped of that control, we’re stripped of that freedom. We don’t feel secure. We don’t feel like we have something to hide. We feel like we’re under the microscope. We feel like prey. Privacy is a fundamental human need, and it’s not about something to hide. I think that’s a very wrong characterization, and we should fight it at every opportunity. Democracy Now!, is an independent global news hour that airs weekdays on 1,300+ TV and radio stations Monday through Friday. Watch our livestream 8-9am ET at http://democracynow.org. Please consider supporting independent media by making a donation to Democracy Now! today: http://democracynow.org/donate FOLLOW DEMOCRACY NOW! ONLINE: Facebook: http://facebook.com/democracynow Twitter: https://twitter.com/democracynow YouTube: http://youtube.com/democracynow SoundCloud: http://soundcloud.com/democracynow Daily Email: http://democracynow.org/subscribe Google+: https://plus.google.com/+DemocracyNow Instagram: http://instagram.com/democracynow Tumblr: http://democracynow.tumblr Pinterest: http://pinterest.com/democracynow iTunes: https://itunes.apple.com/podcast/democracy-now!-audio/id73802554 TuneIn: http://tunein.com/radio/Democracy-Now-p90/ Stitcher Radio: http://www.stitcher.com/podcast/democracy-now
Views: 1289 Democracy Now!
What people miss about the gender wage gap
 
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It's more complex than women earning 79 cents for every dollar a man makes. Subscribe to our channel! http://goo.gl/0bsAjO Read the full article by Sarah Kliff: http://www.vox.com/2016/8/1/12108126/gender-wage-gap-explained-real Check out the studies: http://scholar.harvard.edu/files/goldin/files/dynamics_of_the_gender_gap_for_young_professionals_in_the_financial_and_corporate_sectors.pdf When there is talk about the gender wage gap, often the statistic heard is, “Women earn 79 cents for every dollar a man makes.” While this is factually correct, it does not encompass the nuances of the wage gap. The answer is in the complexity of this problem. Career types and child-rearing duties are both in the equation to closing the gender wage gap. Vox.com is a news website that helps you cut through the noise and understand what's really driving the events in the headlines. Check out http://www.vox.com to get up to speed on everything from Kurdistan to the Kim Kardashian app. Check out our full video catalog: http://goo.gl/IZONyE Follow Vox on Twitter: http://goo.gl/XFrZ5H Or on Facebook: http://goo.gl/U2g06o
Views: 2339632 Vox
Ultimate FREE VBUCKS Farming Guide in Fortnite! | Earn 1000 VBUCKS A Day!
 
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Earn yourself up to 1000 V-Bucks everyday with these EASY and FAST methods! Track VBUCK Missions: https://www.stormshield.one/pve Daily login bonuses / rewards as well as mission quests that you guys can completely everyday will help you Earn A lot of V Bucks per day! We take a look at the best ways... Check out my Other FORTNITE Videos: Get 3 FREE Fortnite Skins https://youtu.be/D0rI19rjSCA Get FORTNITE Save The World Free https://youtu.be/Njj5q-WzX68 Scammer Gets Scammed Compilation https://www.youtube.com/watch?v=4XCSpGxUbmk Top 5 FORTNITE Glitches https://youtu.be/3O1Lw47AZ98 BLITZ Event Coming Soon https://youtu.be/Ne-QEbjOA00 Deadly Battle Royale Snipes! https://youtu.be/1mY4vilYjZY My names Jake but channel name JAKRS, i upload and live stream Rocket League gameplay, such as insane trade ups, big new exclusive crate openings to get all the latest items to showcase to the viewers you guys watching! Follow me on These! Ps4 name: JAKRSYT Twitter: https://twitter.com/JakrsYT Instagram: https://www.instagram.com/jakrsyt/ Check out some other videos of mine below: BUYING PickaPixels WHOLE INVENTORY!!! 400 KEYS BIGGEST TRADE IN ROCKET LEAGUE With PickaPixel - https://www.youtube.com/watch?v=POgEZFmLxVM INSANE ROCKET LEAGUE 40x TURBO Crate Opening! (20XX, PAINTED KALOS & PAINTED ENDO) - https://www.youtube.com/watch?v=PGEi339RUF4
Views: 589567 JAKRS
Advances in Regularization: Bridge Regression and Coordinate Algorithms by Giovanni Seni  20120604
 
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http://www.sfbayacm.org/event/advances-regularization-bridge-regression-and-coordinate-descent-algorithms Speaker: Giovanni Seni A widely held principle in Statistical model inference is that accuracy and simplicity are both desirable. But there is a tradeoff between the two: a flexible (more complex) model is often needed to achieve higher accuracy, but it is more susceptible to overfitting and less likely to generalize well. Regularization techniques "damp down" the flexibility of a model fitting procedure by augmenting the error function with a term that penalizes model complexity. Minimizing the augmented error criterion requires a certain increase in accuracy to "pay" for the increase in model complexity (e.g., adding another term to the model). This talk offers a concise introduction to this topic and a review of recent developments leading to very fast algorithms for parameter estimation with various types of penalties. It concludes with an example in R, showing an application of the techniques to a document classification task with 1-Million predictors. Speaker Bio Giovanni Seni is currently a Senior Data Scientist with Intuit. As an active data mining practitioner in Silicon Valley, he has over 15 years R&D experience in statistical pattern recognition, data mining, and human-computer interaction applications. He has been a member of the technical staff at large technology companies, and a contributor at smaller organizations. He holds five US patents and has published over twenty conference and journal articles. His book with John Elder, "Ensemble Methods in Data Mining - Improving accuracy through combining predictions", was published in February 2010 by Morgan & Claypool. Giovanni is also an adjunct faculty at the Computer Engineering Department of Santa Clara University, where he teaches an Introduction to Pattern Recognition and Data Mining class.
Minecraft 1.3.1 / 1.3 - ALLE NEUERUNGEN (Emeralds, Books, Tripwire, Cacao, Cheats, Commands)
 
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Facebook: http://www.facebook.com/ConCrafterTV Infos | http://de.minecraftwiki.net/wiki/Versionsgeschichte ················································································­······························ minecraft mine craft trailer sonic ether review mojang 1.3 1.3.1 1.3.0 notch release 1.2.5 update news download installation german deutsch was ist jeb neu concrafter con crafter zeigt das neue neuerungen lan cacao ender chest tripwire cheats emeralds emerald smaragd trade npc commands chest news ················································································­······························ + Single-player now runs a server internally + Publish the single-player instance to LAN + Automatically detect LAN worlds in multi-player screen + Added "cheats" option in single-player (enables commands) + Added "bonus chest" option to give players a quicker start in a new world + Added adventure mode (work in progress) + Added trading with villagers + Added emeralds, emerald blocks and emerald ore + Added cocoa beans to jungles + Added the Ender Chest + Added tripwires + Added new creative mode inventory with search functionality + Made it possible to gain enchantment orbs from mining ore and smelting + Added writeable books + Added "large biomes" world type + Added temples to jungles and deserts + Added chat settings + Added option to turn off and view snooper data + Added more information on the debug screen (F3) * Decreased max enchantment level from 50 to 30 * The /tp command can now send players to a specific coordinate * Mobs can spawn on flat surfaces (such as up-side-down stairs) * Updated language files * Mobs are much less likely to glitch through blocks * Server list can be reordered * Nearby items in the world will auto-stack * Stars are smaller and brighter * The "pick block" functionality has been improved * Certain items that previously wasn't stackable can now be stacked * Hill biomes are slightly taller * Wooden log blocks can now be placed side-ways * Wooden half-blocks now act as wood * Water slowly drips through leaves during rain * Minecarts and boats will be placed by dispensers, if possible * Gravel texture has been modified
Views: 67673 ConCrafter | LUCA
About the book Data Modeling Made Simple with ER/Studio Data Architect
 
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Data Modeling Made Simple with ER/Studio Data Architect will provide the business or IT professional with a practical working knowledge of data modeling concepts and best practices, along with how to apply these principles with ER/Studio.
Views: 203 Steve Hoberman
SMOTE - Supersampling Rare Events: Machine Learning with R
 
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Follow me on Twitter @amunategui Check out my new book "Monetizing Machine Learning": https://amzn.to/2CRUO Brief introduction to the SMOTE R package to super-sample/ over-sample imbalanced data sets. SMOTE will use bootstrapping and k nearest neighbor to synthetically create additional observations. Data sets with a target frequency of less than 15% are usually considered as imbalanced/rare. If you liked this video - give me a thumbs up! Thx Companion code on GitHub: https://github.com/amunategui/SMOTE-Oversample-Rare-Events Check out my 11 lectures about 'Reducing High Dimensional Data in R' on Udemy.com, $19 COUPON: https://www.udemy.com/practical-data-science-reducing-high-dimensional-data-in-r/?couponCode=1111 Support these videos, check out my in-depth classes on Udemy.com (discounts and specials) at http://amunategui.github.io/udemy/ Original SMOTE white paper: https://www.jair.org/media/953/live-953-2037-jair.pdf
Views: 17019 Manuel Amunategui
What Is Meant By Classifier In Data Mining?
 
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Br data analysis task is an example of numeric prediction, where 11 feb 2017 all classification techniques assume some knowledge the. Edudata mining evaluation of classifiers. Once a datification scheme has been created, security standards that specify appropriate handling practices for each category and storage define the data's lifecyle requirements should be mining classificationwhat is classification? What prediction? Issues regarding prediction set t split into two subsets t1 t2 with sizes n1 n2 respectively, gini index of contains examples from n classes, gini(t) defined as. Data mining classification what is classification? Usual examples and regression data with weka, part 2 clustering ibm. Classification and prediction nyu computer science. Given a database of tuples and set classes, the classification problem is to define mapping where each tuple objective analyze input data develop an accurate description or model for class using features present in. Binary4 example6 probabilities8 data structure. The process of identifying the relationship and effects this on outcome future values objects is defined as regression. By simple definition, in classification clustering analyze a set of data and generate grouping rules which can be used to typically the learning task like any mining is an iterative process proaches, algorithm settings, before good classifier found. Classification is a two step process. A study on classification techniques in data mining ieee xplore. Classifiers for educational data mining semantic scholar. What's an example of this its simplicity means it's generally faster and more efficient than other algorithms, especially over large datasets. Regression helps in identifying 11 may 2010 this second article of the series, we'll discuss two common data mining methods classification and clustering which can be used to do more powerful you could have best about your customers (whatever that even means), but if don't apply right models it, it will just garbage abstract is a process inferring knowledge from such huge. Data mining classification and prediction slideshare. A classifier is a tool in data mining that takes bunch of representing things we want to classify and attempts predict which class the new belongs. Classification in data mining eecs. Data mining classification & prediction tutorialspoint. 04 classification in data mining slideshare. What is datification? Definition from whatis. The term 'classifier' sometimes also refers to the mathematical function, implemented by a therefore, 80. Data mining has three major components clustering or classification, association rules and sequence analysis. Mean absolute error and other coefficient. Poznan, poland mean squared error. Do not hesitate to ask any questions or read books!. Top 10 data mining algorithms, explained kdnuggets. Branches are added by making the same information gain calculation for data defined location on tree of classification can be applied to simpl
Views: 42 Roselyn Wnuk Tipz
NEWS - WORLD - No to Data-Mining on Facebook and Google - March 20, 2013
 
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No to Data-Mining on Facebook and Google The European Socialist and Democrat members of the European Parliament's committee on legal affairs yesterday strongly opposed a draft proposal to let online companies avoid having to seek explicit prior consent from citizens on the use of their personal data. The legislation was adopted in committee by 14 in favour, with 6 against and 1 abstention, and will now move to the Parliament's civil liberties committee. Socialist MEP Françoise Castex vehemently criticised the position of the European right-wing parties, labelling the vote "a major blow for EU citizens, sacrificed on the altar of US online giants such as Google and Facebook". "It is amazing how the European right stubbornly protects intellectual property rights while being deliberately lax when it comes to supporting citizens' right to protect their personal data", added Castex. A recent study by the University of Cambridge showed that by applying algorithms to Facebook 'Likes', it is possible to capture detailed profiles of the ethnicity, geographical locations, sexual preferences, drug use or religion of social network users. In their conclusions, the researchers rightly recommend reinforcing the privacy settings for 'Likes' and call for greater transparency on the use made of personal data. Referring to the study, Françoise Castex said: "The collection of personal data is not a new phenomenon, however the scale of it is worrying on the Internet. EU citizens are becoming more aware and suspicious. Unfortunately, many of them decide to quit these social networks, in just one month (last December), in England alone 600,000 users left Facebook. "There is an urgent need to secure and maintain public confidence in the digital economy. The solution is not to follow the siren calls of the lobbyists, but to strengthen users' data protection and rights. "The S&D Group will keep fighting to oblige online companies to inform the users and require their explicit consent for collecting, processing or selling their data. "We also want to protect citizens from any form of discrimination resulting from data-mining and profiling, and to heavily penalise companies in cases of abuse or leakage of personal data." Watch more on Canal Internacional www.ci-webtv.com
Views: 146 webtvcanalinter
Combine Data Sources in Google Data Studio | Lesson 2
 
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In this lesson we're going to setup our data sources in Google Sheets with the help of Supermetrics. We're going to pull data from Facebook Ads and Google Analytics and prepare it to later be used in Google Data Studio. Google Sheets combined with Supermetrics is a great way to control data that later goes into our data dashboards - it gives you the possibility to change data around, clean it up and even combine data sources (which is not possible in Google Data Studio itself). Previous video: http://bit.ly/2f4FgZl #GoogleDataStudio #DataSources #DataVizualisation 🔗 Links mentioned in the video: Google Data Sheet: http://bit.ly/2tUX3EF Full Playlist: http://bit.ly/2hkwUx4 Coding is for Losers: http://codingisforlosers.com/ Ben Collins: http://benlcollins.com/ The Dashboard Plan: https://measureschool.com/dashboardplan Google Data Studio:http://bit.ly/2bcb7zt Google Sheets: http://bit.ly/1GAUvK5 Supermetrics: http://bit.ly/2hmxvOR 🎓 Learn more from Measureschool: http://measureschool.com/products GTM Copy Paste https://chrome.google.com/webstore/detail/gtm-copy-paste/mhhidgiahbopjapanmbflpkcecpciffa 🚀Looking to kick-start your data journey? Hire us: https://measureschool.com/services/ 📚 Recommended Measure Books: https://kit.com/Measureschool/recommended-measure-books 📷 Gear we used to produce this video: https://kit.com/Measureschool/measureschool-youtube-gear 👍 FOLLOW US FACEBOOK: http://www.facebook.com/measureschool TWITTER: http://www.twitter.com/measureschool
Views: 27241 Measureschool

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