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Text Analytics - Ep. 25 (Deep Learning SIMPLIFIED)
 
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Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics. Deep Learning TV on Facebook: https://www.facebook.com/DeepLearningTV/ Twitter: https://twitter.com/deeplearningtv Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency. Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words. One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word. The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector. Two popular tools: Word2Vec: https://code.google.com/archive/p/word2vec/ Glove: http://nlp.stanford.edu/projects/glove/ Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse. Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language. Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis: “He turned around a team otherwise known for overall bad temperament” In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive. Credits Nickey Pickorita (YouTube art) - https://www.upwork.com/freelancers/~0147b8991909b20fca Isabel Descutner (Voice) - https://www.youtube.com/user/IsabelDescutner Dan Partynski (Copy Editing) - https://www.linkedin.com/in/danielpartynski Marek Scibior (Prezi creator, Illustrator) - http://brawuroweprezentacje.pl/ Jagannath Rajagopal (Creator, Producer and Director) - https://ca.linkedin.com/in/jagannathrajagopal
Views: 46303 DeepLearning.TV
28c3: Datamining for Hackers
 
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Download high quality version: http://bit.ly/rBS7SW Description: http://events.ccc.de/congress/2011/Fahrplan/events/4732.en.html Stefan Burschka: Datamining for Hackers Encrypted Traffic Mining This talk presents Traffic Mining (TM) particularly in regard to VoiP applications such as Skype. TM is a method to digest and understand large quantities of data. Voice over IP (VoIP) has experienced a tremendous growth over the last few years and is now widely used among the population and for business purposes. The security of such VoIP systems is often assumed, creating a false sense of privacy. Stefan will present research into leakage of information from Skype, a widely used and protected VoIP application. Experiments have shown that isolated phonemes can be classified and given sentences identified. By using the dynamic time warping (DTW) algorithm, frequently used in speech processing, an accuracy of 60% can be reached. The results can be further improved by choosing specific training data and reach an accuracy of 83% under specific conditions
Views: 8037 28c3
Introduction to Data Mining: Summary Statistics
 
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In this Data Mining Fundamentals tutorial, we continue our discussion on data exploration and visualization. We discuss summary statistics and the frequency and mode of an attribute. Summary statistics are numbers that summarize properties of data, and the frequency of an attribute value is a percentage measuring how often the value occurs in the data set. We will also describe percentiles, and provide examples of each. -- Learn more about Data Science Dojo here: https://hubs.ly/H0hCsvM0 Watch the latest video tutorials here: https://hubs.ly/H0hCswl0 See what our past attendees are saying here: https://hubs.ly/H0hCsMH0 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 830 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.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: 4483 Data Science Dojo
What is Data Visualization in 3 minutes ?
 
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What is Data Visualization? Data is an increasingly potent tool at the negotiating table. The expectations have moved beyond manipulating headline figures to detailed correlation analysis. Travel managers and buyers are looking at data on total spend, compliance with policy and preferred booking channels, missed savings, advance booking windows, and online adoption across several dimensions. It’s time for an overhaul of the phrase, a picture is worth a thousand words, because today’s travel manager will tell you that data visualization is worth millions of dollars. Egencia Business Travel Academy Learn the basics of Business Travel with Egencia. We travel on business for all sorts of reasons: to close the deal, to meet the team or to meet the customer. Sometimes, it means a suite with a fantastic view, more often it doesn't. In the end, no matter where you're going, whether it's exotic or ordinary, business travel is work. And it's our job to make it easier for you. We do that by making it faster through our innovative technology. Whether you're arranging travel for others; or overseeing budgets, policy, compliance and traveler safety. And above all else, we strive to give travelers solutions that solve their problems, surprise them with efficiency and delight them in experience. As the line between our business and personal lives blur, shouldn't we demand the same innovation we see in our homes, at our workplace? Shouldn't your travel partner ask every day if it's possible there, why not here? As part of Expedia Group, the world's largest online travel company, Egencia has the most relevant inventory for your business travel needs. We combine this with first class customer service. We are here when your travelers need us the most. At Egencia, our promise to you is simple. It is all about you and always will be. Check us out on http://www.egencia.com and get in touch.
Views: 12624 Egencia
What is 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 preprocessing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term is a buzzword, and is frequently misused to mean any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) but is also generalized to any kind of computer decision support system, including artificial intelligence, machine learning, and business intelligence. In the proper use of the word, the key term is discovery[citation needed], commonly defined as "detecting something new". Even 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. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection) and dependencies (association rule mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting are part of the data mining step, but do belong to the overall KDD process as additional steps.
Views: 52606 John Paul
Quick Data Analysis with Google Sheets | Part 1
 
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Spreadsheet software like Excel or Google Sheets are still a very widely used toolset for analyzing data. Sheets has some built-in Quick analysis features that can help you to get a overview on your data and very fast get to insights. #DataAnalysis #GoogleSheet #measure 🔗 Links mentioned in the video: Supermetrics: http://supermetrics.com/?aff=1014 GA Demo account: https://support.google.com/analytics/answer/6367342?hl=en 🎓 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 Our tracking stack: Google Analytics: https://analytics.google.com/analytics/web/ Google Tag Manager: https://tagmanager.google.com/ Supermetrics: http://supermetrics.com/?aff=1014 ActiveCampaign: https://www.activecampaign.com/?_r=K93ZWF56 👍 FOLLOW US Facebook: http://www.facebook.com/measureschool Twitter: http://www.twitter.com/measureschool
Views: 20726 Measureschool
iFeedback® | Big Data | Englisch
 
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Grow your business with data driven decisions. With the Data Mining Tool "iFeedback® Big Data" it is possible to immediately see, which words are often used by the customers in the Feedbacks.
Views: 26 iFeedback
How To Scrape Google For 1000s Of Leads. Web Scraping Tools No Code.
 
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Want to scrape Google for 1000s of leads or want to collect information from literally any website for your business development or maybe for competition research. Growth hackers often use web scraping to automate and expedite business processes. By the end of this video, you will learn how to collect targeted email from Goolge then I will show you a sophisticated web scraping tool and scrape Google Maps. If you understand the concepts in this video and then research more and practice you will be able to literally scrape any website in-fact you will actually learn how to make a bot and then put it to work for anything this can do wonders for your business and all this for free. ---------------------------------------------------------- Get the search query from this link - https://goo.gl/LBVZy9 Link for the tool to extract emails - http://eel.surf7.net.my/ Web scraping tool - https://www.parsehub.com/ --------------------------------------------
Views: 44711 Arpit Khurana
Natural Language Processing (NLP) & Text Mining Tutorial Using NLTK | NLP Training | Edureka
 
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** NLP Using Python: - https://www.edureka.co/python-natural-language-processing-course ** This Edureka video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and much more along with a demo on each one of the topics. The following topics covered in this video : 1. The Evolution of Human Language 2. What is Text Mining? 3. What is Natural Language Processing? 4. Applications of NLP 5. NLP Components and Demo Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV --------------------------------------------------------------------------------------------------------- Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ --------------------------------------------------------------------------------------------------------- - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learnt content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed. This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience. -------------------------- Who Should go for this course ? Edureka’s NLP Training is a good fit for the below professionals: From a college student having exposure to programming to a technical architect/lead in an organisation Developers aspiring to be a ‘Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Text Mining Techniques 'Python' professionals who want to design automatic predictive models on text data "This is apt for everyone” --------------------------------- Why Learn Natural Language Processing or NLP? Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data. --------------------------------- For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 61282 edureka!
Predicting Football Matches Using Data With Jordan Tigani - Strata Europe 2014
 
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A keynote address from Strata + Hadoop World Europe 2014 in Barcelona, "Predictive Analytics in the Cloud: Predicting Football." Watch more from Strata Europe 2014: http://goo.gl/uqw6WS Visit the Strata website to learn more: http://strataconf.com/strataeu2014/ Subscribe for more from the conference! http://goo.gl/szEauh How can you turn raw data into predictions? How can you take advantage of both cloud scalability and state-of-the-art Open Source Software? This talk shows how we built a model that correctly predicted the outcome of 14 of 16 games in the World Cup using Google’s Cloud Platform and tools like iPython and StatsModels. I’ll also demonstrate new tools to integrate iPython with Google’s cloud and how you can use the same tools to make your own predictions. About Jordan Tigani (Google): Jordan Tigani has more than 15 years of professional software development experience, the last 4 of which have been spent building BigQuery. Prior to joining Google, Jordan worked at a number of star-crossed startups, where he learned to make data-based predictions. He is a co-author of Google BigQuery Analytics. When not analyzing soccer matches, he can often be found playing in one. Stay Connected to O'Reilly Media by Email - http://goo.gl/YZSWbO Follow O'Reilly Media: http://plus.google.com/+oreillymedia https://www.facebook.com/OReilly https://twitter.com/OReillyMedia
Views: 96350 O'Reilly
The best stats you've ever seen | Hans Rosling
 
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http://www.ted.com With the drama and urgency of a sportscaster, statistics guru Hans Rosling uses an amazing new presentation tool, Gapminder, to present data that debunks several myths about world development. Rosling is professor of international health at Sweden's Karolinska Institute, and founder of Gapminder, a nonprofit that brings vital global data to life. (Recorded February 2006 in Monterey, CA.) 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. TED stands for Technology, Entertainment, Design, and TEDTalks cover these topics as well as science, business, development and the arts. Closed captions and translated subtitles in a variety of languages are now available on TED.com, at http://www.ted.com/translate. Follow us on Twitter http://www.twitter.com/tednews Checkout our Facebook page for TED exclusives https://www.facebook.com/TED
Views: 2938074 TED
R tutorial: Introduction to cleaning data with R
 
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Learn more about cleaning data with R: https://www.datacamp.com/courses/cleaning-data-in-r Hi, I'm Nick. I'm a data scientist at DataCamp and I'll be your instructor for this course on Cleaning Data in R. Let's kick things off by looking at an example of dirty data. You're looking at the top and bottom, or head and tail, of a dataset containing various weather metrics recorded in the city of Boston over a 12 month period of time. At first glance these data may not appear very dirty. The information is already organized into rows and columns, which is not always the case. The rows are numbered and the columns have names. In other words, it's already in table format, similar to what you might find in a spreadsheet document. We wouldn't be this lucky if, for example, we were scraping a webpage, but we have to start somewhere. Despite the dataset's deceivingly neat appearance, a closer look reveals many issues that should be dealt with prior to, say, attempting to build a statistical model to predict weather patterns in the future. For starters, the first column X (all the way on the left) appears be meaningless; it's not clear what the columns X1, X2, and so forth represent (and if they represent days of the month, then we have time represented in both rows and columns); the different types of measurements contained in the measure column should probably each have their own column; there are a bunch of NAs at the bottom of the data; and the list goes on. Don't worry if these things are not immediately obvious to you -- they will be by the end of the course. In fact, in the last chapter of this course, you will clean this exact same dataset from start to finish using all of the amazing new things you've learned. Dirty data are everywhere. In fact, most real-world datasets start off dirty in one way or another, but by the time they make their way into textbooks and courses, most have already been cleaned and prepared for analysis. This is convenient when all you want to talk about is how to analyze or model the data, but it can leave you at a loss when you're faced with cleaning your own data. With the rise of so-called "big data", data cleaning is more important than ever before. Every industry - finance, health care, retail, hospitality, and even education - is now doggy-paddling in a large sea of data. And as the data get bigger, the number of things that can go wrong do too. Each imperfection becomes harder to find when you can't simply look at the entire dataset in a spreadsheet on your computer. In fact, data cleaning is an essential part of the data science process. In simple terms, you might break this process down into four steps: collecting or acquiring your data, cleaning your data, analyzing or modeling your data, and reporting your results to the appropriate audience. If you try to skip the second step, you'll often run into problems getting the raw data to work with traditional tools for analysis in, say, R or Python. This could be true for a variety of reasons. For example, many common algorithms require variables to be arranged into columns and for missing values to be either removed or replaced with non-missing values, neither of which was the case with the weather data you just saw. Not only is data cleaning an essential part of the data science process - it's also often the most time-consuming part. As the New York Times reported in a 2014 article called "For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights", "Data scientists ... spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets." Unfortunately, data cleaning is not as sexy as training a neural network to identify images of cats on the internet, so it's generally not talked about in the media nor is it taught in most intro data science and statistics courses. No worries, we're here to help. In this course, we'll break data cleaning down into a three step process: exploring your raw data, tidying your data, and preparing your data for analysis. Each of the first three chapters of this course will cover one of these steps in depth, then the fourth chapter will require you to use everything you've learned to take the weather data from raw to ready for analysis. Let's jump right in!
Views: 37791 DataCamp
DEFCON 17: Dangerous Minds: The Art of Guerrilla Data Mining
 
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Speaker: Mark Ryan Del Moral Talabis Senior Consultant, Secure-DNA Consulting It is not a secret that in today's world, information is as valuable or maybe even more valuable that any security tool that we have out there. Information is the key. That is why the US Information Awareness Office's (IAO) motto is "scientia est potential", which means "knowledge is power". The IAO just like the CIA, FBI and others make information their business. Aside from these there are multiple military related projects like TALON,ECHELON, ADVISE, and MATRIX that are concerned with information gathering and analysis. The goal of the Veritas Project is to model itself in the same general threat intelligence premise as the organization above but primarily based on community sharing approach and using tools, technologies, and techniques that are freely available. Often, concepts that are part of artificial intelligence, data mining, and text mining are thought to be highly complex and difficult. Don't mistake me, these concepts are indeed difficult, but there are tools out there that would facilitate the use of these techniques without having to learn all the concepts and math behind these topics. And as sir Isaac Newton once said, "If I have seen further it is by standing on the shoulders of giants". The combination of all the techniques presented in this site is what we call "Guerrilla Data Mining". It's supposed to be fast, easy, and accessible to anyone. The techniques provides more emphasis on practicality than theory. For example, these tools and techniques presented can be used to visualize trends (e.g. security trends over time), summarize large and diverse data sets (forums, blogs, irc), find commonalities (e.g. profiles of computer criminals) gather a high level understanding of a topic (e.g. the US economy, military activities), and automatically categorize different topics to assist research (e.g. malware taxonomy). Aside from the framework and techniques themselves, the Veritas Project hopes to present a number of current ongoing studies that uses "guerilla data mining". Ultimately, our goal is to provide as much information in how each study was done so other people can generate their own studies and share them through the project. The following studies are currently available and will be presented: For more information visit: http://bit.ly/defcon17_information To download the video visit: http://bit.ly/defcon17_videos
Views: 3932 Christiaan008
DATA PRE-PROCESSING using rapid miner 7.2.003
 
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Data preprocessing is a data mining technique that involves transforming raw data into an understandable format. Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is likely to contain many errors
Views: 4351 NIRMAL JOSE
Understanding Wavelets, Part 1: What Are Wavelets
 
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This introductory video covers what wavelets are and how you can use them to explore your data in MATLAB®. •Try Wavelet Toolbox: https://goo.gl/m0ms9d •Ready to Buy: https://goo.gl/sMfoDr The video focuses on two important wavelet transform concepts: scaling and shifting. The concepts can be applied to 2D data such as images. Video Transcript: Hello, everyone. In this introductory session, I will cover some basic wavelet concepts. I will be primarily using a 1-D example, but the same concepts can be applied to images, as well. First, let's review what a wavelet is. Real world data or signals frequently exhibit slowly changing trends or oscillations punctuated with transients. On the other hand, images have smooth regions interrupted by edges or abrupt changes in contrast. These abrupt changes are often the most interesting parts of the data, both perceptually and in terms of the information they provide. The Fourier transform is a powerful tool for data analysis. However, it does not represent abrupt changes efficiently. The reason for this is that the Fourier transform represents data as sum of sine waves, which are not localized in time or space. These sine waves oscillate forever. Therefore, to accurately analyze signals and images that have abrupt changes, we need to use a new class of functions that are well localized in time and frequency: This brings us to the topic of Wavelets. A wavelet is a rapidly decaying, wave-like oscillation that has zero mean. Unlike sinusoids, which extend to infinity, a wavelet exists for a finite duration. Wavelets come in different sizes and shapes. Here are some of the well-known ones. The availability of a wide range of wavelets is a key strength of wavelet analysis. To choose the right wavelet, you'll need to consider the application you'll use it for. We will discuss this in more detail in a subsequent session. For now, let's focus on two important wavelet transform concepts: scaling and shifting. Let' start with scaling. Say you have a signal PSI(t). Scaling refers to the process of stretching or shrinking the signal in time, which can be expressed using this equation [on screen]. S is the scaling factor, which is a positive value and corresponds to how much a signal is scaled in time. The scale factor is inversely proportional to frequency. For example, scaling a sine wave by 2 results in reducing its original frequency by half or by an octave. For a wavelet, there is a reciprocal relationship between scale and frequency with a constant of proportionality. This constant of proportionality is called the "center frequency" of the wavelet. This is because, unlike the sinewave, the wavelet has a band pass characteristic in the frequency domain. Mathematically, the equivalent frequency is defined using this equation [on screen], where Cf is center frequency of the wavelet, s is the wavelet scale, and delta t is the sampling interval. Therefore when you scale a wavelet by a factor of 2, it results in reducing the equivalent frequency by an octave. For instance, here is how a sym4 wavelet with center frequency 0.71 Hz corresponds to a sine wave of same frequency. A larger scale factor results in a stretched wavelet, which corresponds to a lower frequency. A smaller scale factor results in a shrunken wavelet, which corresponds to a high frequency. A stretched wavelet helps in capturing the slowly varying changes in a signal while a compressed wavelet helps in capturing abrupt changes. You can construct different scales that inversely correspond the equivalent frequencies, as mentioned earlier. Next, we'll discuss shifting. Shifting a wavelet simply means delaying or advancing the onset of the wavelet along the length of the signal. A shifted wavelet represented using this notation [on screen] means that the wavelet is shifted and centered at k. We need to shift the wavelet to align with the feature we are looking for in a signal.The two major transforms in wavelet analysis are Continuous and Discrete Wavelet Transforms. These transforms differ based on how the wavelets are scaled and shifted. More on this in the next session. But for now, you've got the basic concepts behind wavelets.
Views: 197971 MATLAB
Getting Started with Orange 03: Widgets and Channels
 
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Orange data mining widgets and communication channels. License: GNU GPL + CC Music by: http://www.bensound.com/ Website: http://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 64795 Orange Data Mining
Intelligent Heart Disease Prediction System Using Data Mining Techniques || in Bangalore
 
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The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information for effective decision making. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining techniques can help remedy this situation. This research has developed a prototype Intelligent Heart Disease Prediction System (IHDPS) using data mining techniques, namely, Decision Trees, Naïve Bayes and Neural Network. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. IHDPS can answer complex “what if” queries which traditional decision support systems cannot. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease, to be established. IHDPS is Web-based, user-friendly, scalable, reliable and expandable. It is implemented on the .NET platform.
Alexey Zinoviev - Java in production for Data Mining Research projects (Ru)
 
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Speaker: Alexey Zinoviev Topic: Java in production for Data Mining Research projects Abstract: Java is often criticized for hard parsing CSV datasets, poor matrix and vectors manipulations. This makes it hard to easy and efficiently implement certain types of machine learning algorithms. In many cases data scientists choose R or Python languages for modeling and problem solution and you as a Java developer should rewrite R algorithms in Java or integrate many small Python scripts in Java application. But why so many highload tools like Cassandra, Hadoop, Giraph, Spark are written in Java or executed on JVM? What the secret of successful implementation and running? Maybe we should forget old manufacturing approach of dividing on developers and research engineers in production projects? During the report, we will discuss how to build full Java-stack Data Mining application, deploy it, make charts, integrate with databases, how to improve performance with JVM tuning and etc. Attendees of my talk will become familiar with the development and deploy of an research Java projects, Hadoop/Spark basics and will get useful tips about possible integration ways.
Views: 174 jetconf
José Manuel Ortega - Python tools for webscraping
 
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PyData Madrid 2016 Most of the talks and workshop tutorials can be found here: https://github.com/PyDataMadrid2016/Conference-Info If we want to extract the contents of a website automating information extraction, often we find that the website does not offer any API to get the data you need and It is necessary use scraping techniques to recover data from a Web automatically. Some of the most powerful tools for extracting the data in web pages can be found in the python ecosystem. Introduction to webscraping WebScraping is the process of collecting or extracting data from web pages automatically. Nowdays is a very active field and developing shared goals with the semantic web field, natural language processing,artificial intelligence and human computer interaction. Python tools for webscraping Some of the most powerful tools to extract data can be found in the python ecosystem, among which we highlight Beautiful soup, Webscraping, PyQuery and Scrapy. Comparison between webscraping tools A comparison of the mentioned tools will be made, showing advantages and disadvantages of each one,highlighting the elements of each one to perform data extraction as regular expressions,css selectors and xpath expressions. Project example with scrapy Scrapy is a framework written in python for extraction automated data that can be used for a wide range of applications such as data mining processing. When using Scrapy we have to create a project, and each project consists of: 1.Items: We define the elements to be extracted. 2.Spiders: The heart of the project, here we define the extract data procedure. 3.Pipelines: Are the proceeds to analyze elements: data validation, cleansing html code Outline Introduction to webscraping(5 min) I will mention the main scraping techniques 1.1.WebScraping 1.2.Screen scraping 1.3.Report mining 1.4.Spiders Python tools for webscraping(10 min) For each library I will make and introduction with a basic example. In some examples I will use requests library for sending HTTP requests 2.1. BeautifulSoup 2.2. Webscraping 2.2. PyQuery Comparing scraping tools(5 min) 3.1.Introduction to techniques for obtain data from web pages like regular expressions,css selectors, xpath expressions 3.2.Comparative table comparing main features of each tool Project example with scrapy(10 min) 4.1.Project structure with scrapy 4.2.Components(Scheduler,Spider,Pipeline,Middlewares) 4.3.Generating reports in json,csv and xml formats
Views: 1498 PyData
John P Overington (Medicines Discovery Catapult): Data Mining Small Molecule Drug Discovery
 
56:35
Despite having more information and technology than at any point in history, drug discovery is becoming harder. It is tempting to believe that there was ‘low hanging fruit’ in the past, and that previous generations had easier to treat diseases, simpler biology and a large number of drug-like leads to optimize. Regardless of the cause, there is now a pressing need to understand fundamental complex biological systems, especially those linked to disease pathology. The most definitive tools for illuminating biology for this are often small molecules, and there is now intense interest in developing, in a cost effective way, potent, well distributed and selective chemical probes, then applying these to understand the role of novel genes, potentially leading to a new medicine. Underlying the development of chemical probes and drug leads, is what is known from the past, and what general rules can be learnt that are useful in the future. The presentation will detail the background and development of two large, now public domain, chemical biology databases – ChEMBL and SureChEMBL. These databases, in particular ChEMBL have led to the development of many new algorithms for target prediction, chemical library design, etc. Next four examples of data mining of ChEMBL and other public domain data will be described. 1) A framework to anticipate and integrate into compound design processes the effect of mutations in the target – this is of special importance in the area of anti-infective and anti-cancer drugs where resistance is a significant healthcare issue. 2) An analysis of drug properties according to target class for the antibiotics, where differences in physicochemical properties can be correlated in target properties. 3) Addressing the problem of target validation using genetics, which could de-risk the development of chemical tools and leads, and place novel targets into an appropriate therapeutic setting. 4) Is the concept of ‘Druggability’ real, or has it led to restriction in the number of systems that the community is prepared to work on?
Views: 469 ChemAxon
Weather Forecasting Using Data Mining In C# | Final Year Project
 
03:19
Weather forecasting is the application of science and technology to predict the state of the atmosphere for a given location. Ancient weather forecasting methods usually relied on observed patterns of events, also termed pattern recognition. For example, it might be observed that if the sunset was particularly red, the following day often brought fair weather. However, not all of these predictions prove reliable. Here this system will predict weather based on parameters such as temperature, humidity and wind. This system is a web application with effective graphical user interface. User will login to the system using his user ID and password. User will enter current temperature; humidity and wind, System will take this parameter and will predict weather from previous data in database. The role of the admin is to add previous weather data in database, so that system will calculate weather based on these data. Weather forecasting system takes parameters such as temperature, humidity, and wind and will forecast weather based on previous record therefore this prediction will prove reliable. This system can be used in Air Traffic, Marine, Agriculture, Forestry, Military, and Navy etc. Advantages User can easily find out Weather condition by using this system. The primary advantage of forecasting is that it provides the business with valuable information that the business can use to make decisions about the future of the organization. Disadvantages Weather forecast by the system is not very accurate. Previous data is required by the system to forecast weather. contact no.:- +91-9860380594 Email: [email protected] /[email protected] LIKE us on FACEBOOK: - https://www.facebook.com/rjdeveloper2015/ JOIN our on Instagram: - https://www.instagram.com/rj_developer/?hl=en
Views: 943 RJ DEVELOPER
What is OLAP?
 
05:05
This video explores some of OLAP's history, and where this solution might be applicable. We also look at situations where OLAP might not be a fit. Additionally, we investigate an alternative/complement called a Relational Dimensional Model. To Talk with a Specialist go to: http://www.intricity.com/intricity101/ www.intricity.com
Views: 382572 Intricity101
Technical Seminar: "Data Mining for Air Safety"
 
01:24:48
Anonymous collections of data from the aviation community can sometimes be "mined" to reveal patterns that can lead to improvements, most often in safety of operations or the aircraft itself. Two of NASA's best data experts discuss advances in extracting information efficiently and reliably from large, distributed, multiple, heterogeneous sources of aviation safety data. Aired September 22, 2006.
Views: 240 NASA Video
Living the Promise: Eamonn Keogh
 
03:31
Cloud storage, mobile computing, super powerful processing and other revolutionary changes in technology leave us awash in massive seas of data. In order to thoughtfully analyze this growing glut of information, researchers are racing to develop innovative data-mining tools. By designing sophisticated algorithms that identify patterns in widely diverse datasets, Prof. Keogh creates unique tools that detect specific shapes and characteristics.
Rattle Logistic and Support Vector Machine
 
04:21
Demonstration of Togaware rattle as a "rapid prototyping" tool for the data sciences. Often, rattle can be used to get a project up and running; and their excellent logging feature allows you to move from quick prototype to hands on R-coding to implement the full featured project.
Views: 972 Math4IQB
The PMML Path towards True Interoperability in Data Mining
 
03:15
As the de facto standard for data mining models, the Predictive Model Markup Language (PMML) provides tremendous benefits for business, IT, and the data mining industry in general, since it allows for predictive models to be easily moved between applications. Due to the cross-platform and vendor-independent nature of such an open-standard, auto-generated PMML code is often represented in different versions of PMML. A tool may export PMML 2.1 and another import PMML 4.0. This problem raises the issue of conversion. For true interoperability, PMML needs to be easily converted from one version to another. This presentation, given at the KDD 2011 PMML Workshop, describes the capabilities associated with the "PMML Converter". This application represents a great step in the PMML path towards true interoperability in data mining. Besides converting older versions of PMML to its latest, the PMML converter checks PMML files for syntax issues and, if issues are encountered, automatically corrects them. Finally, auto-generated PMML code can omit important data pre-processing steps which are an integral part of a predictive solution. The "Transformations Generator" aims to bridge this gap by providing a graphical interface for the development and expression of data pre-processing steps in PMML. Both tools: PMML Converter and the Transformations Generator, can be found at http://www.zementis.com/pmml_tools.htm
Views: 494 aguazzel
How To: Connecting and Visualizing Data with Maltego (Ignite 2015)
 
42:15
A picture is worth a thousand words, but crafting a picture from your threat data is often a major challenge. Maltego is an inexpensive data visualization tool that allows analysts to connect multiple data sources to each other in a visual way. This presentation will demonstrate the power of Maltego and give an introduction how you can write "transforms" that will let you visually explore your data and discover new connections between attacks.
Introduction to Data Mining: Similarity & Dissimilarity
 
03:43
In this Data Mining Fundamentals tutorial, we introduce you to similarity and dissimilarity. Similarity is a numerical measure of how alike two data objects are, and dissimilarity is a numerical measure of how different two data objects are. We also discuss similarity and dissimilarity for single attributes. -- Learn more about Data Science Dojo here: https://hubs.ly/H0hCsmV0 Watch the latest video tutorials here: https://hubs.ly/H0hCr-80 See what our past attendees are saying here: https://hubs.ly/H0hCsmW0 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 830 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.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: 21651 Data Science Dojo
Alice's Adventures in Factorland
 
07:03
To access the paper: https://ssrn.com/abstract=3331680 Factor investing has failed to live up to its many promises. Its success is compromised by three problems that are often underappreciated by investors. First, many investors develop exaggerated expectations about factor performance as a result of data mining, crowding, unrealistic trading cost expectations, and other concerns. Second, for investors using naive risk management tools, factor returns can experience downside shocks far larger than would be expected. Finally, investors are often led to believe their factor portfolio is diversified. Diversification can vanish, however, in certain economic conditions, when factor returns become much more correlated. Factor investing is a powerful tool, but understanding the risks involved is essential before adopting this investment framework. Robert D. Arnott Research Affiliates, LLC Campbell R. Harvey Duke University - Fuqua School of Business; National Bureau of Economic Research (NBER); Duke Innovation & Entrepreneurship Initiative Vitali Kalesnik Research Affiliates LLC Juhani T. Linnainmaa USC Marshall School of Business; National Bureau of Economic Research (NBER)
Views: 542 Campbell Harvey
Finding What to Read: Visual Text Analytics Tools and Techniques to Guide Investigation
 
01:03:57
Text is one of the most prominent forms of open data available, from social media to legal cases. Text visualizations are often critiqued for not being useful, for being unstructured and presenting data out of context (think: word clouds). I argue that we should not expect them to be a replacement for reading. In this talk I will briefly discuss the close/distant reading debate then focus on where I think text visualization can be useful: hypothesis generation and guiding investigation. Text visualization can help someone form questions about a large text collection, then drill down to investigate through targeted reading of the underlying source texts. Over the past 10 years my research focus has been primarily on creating techniques and systems for text analytics using visualization, across domains as diverse as legal studies, poetics, social media, and automotive safety. I will review several of my past projects with particular attention to the capabilities and limitations of the technologies and tools we used, how we use semantics to structure visualizations, and the importance of providing interactive links to the source materials. In addition, I will discuss the design challenges which, while common across visualization, are particularly important with text (legibility, label fitting, finding appropriate levels of 'zoom').
Views: 425 Microsoft Research
Pingar: turning unstructured data into knowledge
 
15:53
Large enterprises, and even smaller organizations in a document intensive industry, often have millions of documents stored on their servers. Finding any sort of meaningful relationship among the documents or gleaning any value from them can seem impossible. Pingar has developed technology to make sense of all this information and allow its owners to put unstructured data to good use. "What we're trying to do is provide technologies that enable those enterprises to begin to understand what content sits within their data sets," explains Peter Wren-Hilton, CEO and Founder of Pingar. "Typically the entity extraction and content analysis components that we have developed really are designed to enable enterprises to be able to identify relationships between documents [and] begin to, for instance, generate automatic meta data. If redaction is a key point, then our entity extraction components allow companies to redact documents through algorithms rather than through the black marker pen. So we've got a range of components that collectively enable enterprises to make more sense from the millions of documents that they have stored away." Generating the meta data is a key difference between Pingar and other enterprise solutions on the market. Most enterprise search engines rely on meta data to help them identify documents; however, users often find a way around entering the meta data when the document is created. Pingar removes the need to humanly tag documents and replaces that task with algorithms. This is just one of many tasks that are automated with Pingar. "It's not just the extraction," says Wren-Hilton, "it's what you can then do with that extracted entity. We then move from entity extraction to content analysis, and with content analysis, we've got redaction, sanitization and summarization. So we're able to take a 40-page .pdf and create a six or seven paragraph executive summary on the fly simply through content analysis." The technology is platform agnostic, so it works with any document management system, and it was released as an API to allow developers to access it. "We were going to go to market at the end of last year, and we actually did a fairly significant pivot when we realized that the amount of technology that we had would make it far better to release it as an API. In March of this year, we released an API with 18 specific components that developers are able to access. There's the standard, free developer sandbox account, so they can start building applications." Currently, Pingar is available in English and Chinese language versions; however, the company has plans to release French, German, Spanish and Arabic versions of the API over the next 6 to 9 months. Other innovations are sure to follow as well. "Although we're commercializing the product," explains Wren-Hilton, "there's still a strong focus on research, and I think one of the areas that will create the most excitement for those people interested in enterprise is the ability to start developing custom taxonomies. A company will be able to build a custom taxonomy using some of our technology. So rather than having to use a digital librarian to physically build a taxonomy, our entity extraction tools will identify the most commonly used terms and phrases and build a taxonomy on the fly." More info: Pingar web site: http://pingar.com/ Pingar blog: http://www.pingar.com/blog/ Pingar profile on CrunchBase: http://www.crunchbase.com/company/pingar Pinger on Twitter: http://twitter.com/PingarHQ
Introduction to BI
 
06:25
In this tutorial, you'll learn what is BI In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons. The data stored in the warehouse is uploaded from the operational systems (such as marketing, sales, etc., shown in the figure to the right). The data may pass through an operational data store for additional operations before it is used in the DW for reporting. The typical extract-transform-load (ETL)-based data warehouse uses staging, data integration, and access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store (ODS) database. The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups often called dimensions and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data.[4] This definition of the data warehouse focuses on data storage. The main source of the data is cleaned, transformed, cataloged and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support.[5] However, the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform and load data into the repository, and tools to manage and retrieve metadata.
Views: 1135 radhikaravikumar
Machine learning and data science for medicine: a vision, some progress and opportunities
 
46:09
Professor van der Schaar is Man Professor in the Oxford–Man Institute of Quantitative Finance (OMI) and the Department of Engineering Science at Oxford, Fellow of Christ Church College and Fellow of the Alan Turing Institute. Mihaela’s research interests and expertise are in machine learning, data science and decisions for a better planet. In particular, she is interested in developing machine learning, data science and AI theory, methods and systems for personalised medicine and personalised education. Talk title: Machine learning and data science for medicine: a vision, some progress and opportunities Synopsis: Mihaela’s work uses data science and machine learning to create models that assist diagnosis and prognosis. Existing models suffer from two kinds of problems. Statistical models that are driven by theory/hypotheses are easy to apply and interpret but they make many assumptions and often have inferior predictive accuracy. Machine learning models can be crafted to the data and often have superior predictive accuracy but they are often hard to interpret and must be crafted for each disease … and there are a lot of diseases. In this talk I present a method (AutoPrognosis) that makes machine learning itself do both the crafting and interpreting. For medicine, this is a complicated problem because missing data must be imputed, relevant features/covariates must be selected, and the most appropriate classifier(s) must be chosen. Moreover, there is no one “best” imputation algorithm or feature processing algorithm or classification algorithm; some imputation algorithms will work better with a particular feature processing algorithm and a particular classifier in a particular setting. To deal with these complications, we need an entire pipeline. Because there are many pipelines we need a machine learning method for this purpose, and this is exactly what AutoPrognosis is: an automated process for creating a particular pipeline for each particular setting. Using a variety of medical datasets, we show that AutoPrognosis achieves performance that is significantly superior to existing clinical approaches and statistical and machine learning methods. #aiattheturing
2012 JUC NY: Noah Sussman - Jenkins Data Mining on the Command Line
 
41:59
Questions arise in the course of running a CI system. Is this test flaky? How often does that message come up in the console log? Which change sets were in the builds that ran between 8:00pm and midnight? To find correlations between arbitrary events, it becomes necessary to look beyond the information provided by the Jenkins UI. This session will show how to use command line tools to discover, analyze and graph patterns in Jenkins data.
Views: 641 CloudBeesTV
Difference between DB & DWH testing
 
04:19
In this tutorial, you 'll learn what is the difference between DB & DWH testing. In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons. The data stored in the warehouse is uploaded from the operational systems (such as marketing, sales, etc., shown in the figure to the right). The data may pass through an operational data store for additional operations before it is used in the DW for reporting. The typical extract-transform-load (ETL)-based data warehouse uses staging, data integration, and access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store (ODS) database. The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups often called dimensions and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data.[4] This definition of the data warehouse focuses on data storage. The main source of the data is cleaned, transformed, cataloged and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support.[5] However, the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform and load data into the repository, and tools to manage and retrieve metadata.
Views: 3042 radhikaravikumar
Scrape Websites with Python + Beautiful Soup 4 + Requests -- Coding with Python
 
34:35
Coding with Python -- Scrape Websites with Python + Beautiful Soup + Python Requests Scraping websites for data is often a great way to do research on any given idea. This tutorial takes you through the steps of using the Python libraries Beautiful Soup 4 (http://www.crummy.com/software/BeautifulSoup/bs4/doc/#) and Python Requests (http://docs.python-requests.org/en/latest/). Reference code available under "Actions" here: https://codingforentrepreneurs.com/projects/coding-python/scrape-beautiful-soup/ Coding for Python is a series of videos designed to help you better understand how to use python. Assumes basic knowledge of python. View all my videos: http://bit.ly/1a4Ienh Join our Newsletter: http://eepurl.com/NmMcr A few ways to learn Django, Python, Jquery, and more: 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 (80k+ 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: 419494 CodingEntrepreneurs
Trade Miner Review By Mike At Orderflows Market Analysis Data Mining Software
 
21:28
http://www.orderflows.com/2017/02/12/find-high-percentage-trades-quickly/ I just wanted to tell you about this market software research tool you might be interested in. Everyone talks about finding high percentage trades, the trades with the 4-1 reward to risk ratio and so on then just leave you to it. What this software does is it finds you high percentage trades and show you the success rate in the past, the average profit per trade and much more. At a click of a button you will know what futures contract, stock or forex pair to buy (or sell), when to buy (or sell), the trade's winning percentage and average profit. Recently I did a webinar about it explaining how a trader can use it to research and find very profitable trades. You can watch the replay here and learn more about the software. One of the best part is the price. All too often software comes on the market for traders and it is priced at $500, $1000 and more, but this software you can get for as low as $97, but honestly there is a better deal which I tell you about to save you a little more money! It is very rare that I promote another trading software product but I have to be honest and tell you that this software can really open your eyes to some amazing trading opportunities. CFTC Rules 4.41: Hypothetical or Simulated performance results have certain limitations, unlike an actual performance record, simulated results do not represent actual trading. Also, since the trades have not been executed, the results may have under-or-over compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profit or losses similar to those shown. Disclaimer: This presentation is for educational and informational purposes only and should not be considered a solicitation to buy or sell a futures contract or make any other type of investment decision. Futures trading contains substantial risk and is not for every investor. An investor could potentially lose all or more than the initial investment. Risk capital is money that can be lost without jeopardizing ones financial security or life style. Only risk capital should be used for trading and only those with sufficient risk capital should consider trading. Past performance is not necessarily indicative of future results.  Risk Disclosure: Futures and forex trading contains substantial risk and is not for every investor. An investor could potentially lose all or more than the initial investment. Risk capital is money that can be lost without jeopardizing ones’ financial security or life style. Only risk capital should be used for trading and only those with sufficient risk capital should consider trading. Past performance is not necessarily indicative of future results. Hypothetical Performance Disclosure: Hypothetical performance results have many inherent limitations, some of which are described below. no representation is being made that any account will or is likely to achieve profits or losses similar to those shown; in fact, there are frequently sharp differences between hypothetical performance results and the actual results subsequently achieved by any particular trading program. One of the limitations of hypothetical performance results is that they are generally prepared with the benefit of hindsight. In addition, hypothetical trading does not involve financial risk, and no hypothetical trading record can completely account for the impact of financial risk of actual trading. for example, the ability to withstand losses or to adhere to a particular trading program in spite of trading losses are material points which can also adversely affect actual trading results. There are numerous other factors related to the markets in general or to the implementation of any specific trading program which cannot be fully accounted for in the preparation of hypothetical performance results and all which can adversely affect trading results.
Views: 2661 Order Flows
R Tidyverse Reporting and Analytics for Excel Users - Preview
 
01:11
https://www.datastrategywithjonathan.com Free YouTube Playlist https://www.youtube.com/playlist?list=PL8ncIDIP_e6vQ0uQofezvKv3yPnL5Unxe From Excel To Big Data and Interactive Dashboard Visualizations in 5 Hours If you use Excel for any type of reporting or analytics then this course is for you. There are a lot of great courses teaching R for statistical analysis and data science that can sometimes make R seem a bit too advanced for every day use. Also since there are many different ways of using R that can often add to the confusion. The reality is that R can be used to make your every day reporting analytics that you do in Excel much faster and easier without requiring any complex statistical techniques while at the same time giving you a solid foundation to expand into those areas if you so wish. This course uses the Tidyverse standards for using R which provides a single, comprehensive and easy to understand method for using R without complicating things via multiple methods. It's designed to build upon the the skills you are already familiar with in Excel to shortcut your learning journey. If you're looking to learn Advanced Excel, Excel VBA or Databases then you need to check out this video series. In this videos series, I will show you how to use Microsoft Excel in different ways that will make you far more effective at working with data. I'm also going to expand your knowledge beyond Excel and show you tips, tricks, and tools from other top data analytics tools such as R Tidyverse, Python, Data Visualisation tools such as Tableau, Qlik View, Qlik Sense, Plotly, AWS Quick Sight and others. We'll start to touch on areas such as big data, machine learning, and cloud computing and see how you can develop your data skills to get involved in these exciting areas. Excel Formulas such as vlookup and sumifs are some of the top reasons for slow spreadsheets. Alternatives for vlookup include power query (Excel 2010 and Excel 2013) which has recently been renamed to Get and Transform in Excel 2016. Large and complex vlookup formulas can be also done very efficiently in R. Using the R Tidyverse libraries you can use the join functions to merge millions of records effortlessly. In comparison to Excel Vlookup, R Tidyverse Join can pull on multiple columns all at the same time. Microsoft Excel Power Query and R Tidyverse Joins are similar to the joins that you do in databases / SQL. The benefit that they have over relational databases such as Microsoft Access, Microsoft SQL Server, MySQL, etc is that they work in memory so they are actually much faster than a database. Also since they are part of an analytics tool instead of a database it is much faster and easier to build your analysis and queries all in the same tools. My very first R Tidyverse program was written to replace a Microsoft Access VBA solution which was becoming complicated and slow. Note that Microsoft Access is very limited in analytics functions and is missing things as simple as Median. Even though I had to learn R programming from scratch and completely re-write the Microsoft Access VBA solution it was so much easier and faster. It blew my mind how much easier R programming with R Tidyverse was than Microsoft Access VBA or Microsoft Excel VBA. If you have any VBA skills or are looking to learn VBA you should definitely checkout my videos on R Tidyverse. To understand why R Tidyverse is so much easier to work with than VBA. R Tidyverse is designed to work directly with your data. So If you want to add a calculated column that’s around one line of script. In Excel VBA, the VBA is used to control the DOM (Document Object Model). In Excel that means that you VBA controls things like cells and sheets. This means your VBA is designed to capture the steps that you would normally do manually in Microsoft Excel or Microsoft Access. VBA is not actually designed to work directly with your data. Note the most efficient path is to reduce the data pulled down from the database in the first place. This is referring to the amount of data you are pulling down from your data warehouse or data lake. It makes no sense to pull data from a data warehouse / data lake to pull into another database to query add joins / lookups to then pull it into Excel or other analysis tool. Often analyst build these intermediate databases because they either don’t have control of the data warehouse or they need to join additional information. All of these operations are done significantly faster in a tool such as R Tidyverse or Microsoft Excel Power Query.
Views: 1566 Jonathan Ng
Goksel MISIRLI - Computational Design of Biological Systems
 
38:19
Synthetic biologists’ aim of designing predictable and novel genetic circuits becomes ever more challenging as the size and complexity of the designs increase. One way to facilitate this process is to use the huge amount of biological data that already exist. However, biological data are often spread in many databases, and are available in different formats and semantics. New computational methods are required to integrate data and to make it available for further mining. Moreover, once integrated, these data can be used to derive designs and mathematical models of biological parts. Computational modelling and simulation will become essential for large-scale synthetic biology. Using model-driven design approaches, biological systems under design can be simulated in silico prior to implementation in vivo. Whilst there are many tools, that demonstrating the usefulness of model-driven design paradigms, these tools often lack access to modular models of biological parts. Here, we show how data integration and mining can be facilitated using Semantic Web technologies. We use an ontology to represent a wealth of biological information for synthetic biology, and to automate the identification of biological parts relevant to the design of biological systems. We th en present an approach to represent modular, reusable and composable models of biological parts. These models, termed standard virtual parts, are created mining biological data and are available from the Virtual Parts Repository. The repository provides data in standard formats, such as the Synthetic Biology Open Language, a standard exchange format for synthetic biology designs. These resources provide computational access to data in standard formats allowing the construction of automated workflows, and facilitate large-scale engineering of biological systems.
Big Data Analysis - tools and methods
 
02:24
A five-day summer course for working professionals. The course will bring you in the forefront of the newest tools and methods based on cutting edge research and experience. Big Data is omnipresent from industries to government and is frequently considered a completely new approach to problem solving. While the possibilities are often exaggerated, Big Data does indeed introduce new opportunities and challenges. Link: http://copenhagensummeruniversity.ku.dk/
What is CRIME ANALYSIS? What does CRIME ANALYSIS mean? CRIME ANALYSIS meaning & explanation
 
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What is CRIME ANALYSIS? What does CRIME ANALYSIS mean? CRIME ANALYSIS meaning - CRIME ANALYSIS definition - CRIME ANALYSIS explanation. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Crime analysis is a law enforcement function that involves systematic analysis for identifying and analyzing patterns and trends in crime and disorder. Information on patterns can help law enforcement agencies deploy resources in a more effective manner, and assist detectives in identifying and apprehending suspects. Crime analysis also plays a role in devising solutions to crime problems, and formulating crime prevention strategies. Quantitative social science data analysis methods are part of the crime analysis process, though qualitative methods such as examining police report narratives also play a role. Crime analysis can occur at various levels, including tactical, operational, and strategic. Crime analysts study crime reports, arrests reports, and police calls for service to identify emerging patterns, series, and trends as quickly as possible. They analyze these phenomena for all relevant factors, sometimes predict or forecast future occurrences, and issue bulletins, reports, and alerts to their agencies. They then work with their police agencies to develop effective strategies and tactics to address crime and disorder. Other duties of crime analysts may include preparing statistics, data queries, or maps on demand; analyzing beat and shift configurations; preparing information for community or court presentations; answering questions from the public and the press; and providing data and information support for a police department's CompStat process. To see if a crime fits a certain known pattern or a new pattern is often tedious work of crime analysts, detectives or in small departments, police officers or deputies themselves. They must manually sift through piles of paperwork and evidence to predict, anticipate and hopefully prevent crime. The U.S. Department of Justice and the National Institute of Justice recently launched initiatives to support “predictive policing”, which is an empirical, data-driven approach. However this work to detect specific patterns of crime committed by an individual or group (crime series), remains a manual task. MIT doctoral student Tong Wang, Cambridge (Mass.) Police Department CPD Lieutenant Daniel Wagner, CPD crime analyst Rich Sevieri and Assoc. Prof. of Statistics at MIT Sloan School of Management and the co-author of Learning to Detect Patterns of Crime Cynthia Rudin have designed a machine learning method called “Series Finder” that can assist police in discovering crime series in a fraction of the time. Series Finder grows a pattern of crime, starting from a seed of two or more crimes. The Cambridge Police Department has one of the oldest crime analysis units in the world and their historical data was used to train Series Finder to detect housebreak patterns. The algorithm tries to construct a modus operandi (MO). The M.O. is a set of habits of a criminal and is a type of behavior used to characterize a pattern. The data of the burglaries include means of entry (front door, window, etc.), day of the week, characteristics of the property (apartment, house), and geographic proximity to other break-ins. Using nine known crime series of burglaries, Series Finder recovered most of the crimes within these patterns and also identified nine additional crimes. Machine learning is a tremendous tool for predictive policing. If patterns are identified the police can immediately try to stop them. Without such tools it can take weeks and even years of shifting though databases to discover a pattern. Series Finder provides an important data-driven approach to a very difficult problem in predictive policing. It’s the first mathematically principled approach to the automated learning of crime series.....
Views: 1984 The Audiopedia
Business Data Analysis with Excel
 
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Lecture Starts at: 8:25 Business data presents a challenge for the data analyst. Business data is often aggregated, recorded over time, and tends to exhibit autocorrelation. Additionally, and most problematically, the amount of business data is usually quite limited. These characteristics lead to a situation where many of the tools in the analyst's tool belt (e.g., regression) aren't ideal for the task. Despite these challenges, proper analysis of business data represents a fundamental skill required of Business/Data Analysts, Product/Program Managers, and Data Scientists. At this meetup presenter Dave Langer will show how to get started analyzing business data in a robust way using Excel – no programming or statistics required! Dave will cover the following during the presentation: • The types of business data and why business data is a unique analytical challenge. • Requirements for robust business data analysis. • Using histograms, running records, and process behavior charts to analyze business data. • The rules of trend analysis. • How to properly compare business data across time, organizations, geographies, etc.Where you can learn more about the tools and techniques. *Excel spreadsheets can be found here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Business%20Data%20Analysis%20with%20Excel **Find out more about David here: https://www.meetup.com/data-science-dojo/events/236198327/ -- Learn more about Data Science Dojo here: https://hubs.ly/H0hz7sf0 Watch the latest video tutorials here: https://hubs.ly/H0hz8rL0 See what our past attendees are saying here: https://hubs.ly/H0hz7ts0 -- Like Us: https://www.facebook.com/datasciencedojo/ Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data-science-dojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo/ Vimeo: https://vimeo.com/datasciencedojo
Views: 52749 Data Science Dojo
FAT* 2019: Implications Tutorial: Reasoning About (Subtle) Biases in Data
 
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FAT* 2019: Implications Tutorial: Reasoning About (Subtle) Biases in Data to Improve the Reliability of Decision Support Tools Session Chair: Joshua Kroll (UC Berkeley) Suchi Saria, Adarsh Subbaswamy Data-driven decision support tools are increasingly being deployed in a variety of applications such as predicting crime, assessing recidivism risk, and automated medical screening. However, common assumptions used in training such models often do not hold in practice, yielding models that make dangerous predictions (as we will demonstrate in our case studies). Specifically, modelers typically assume that training data is representative of the target population or environment where the model will be deployed. Yet commonly there is bias specific to the training dataset which causes learned models to be unreliable: they do not generalize beyond the training population and, more subtly, are not robust to shifts in practice or policy in the training environment. This bias can arise due to the method of data collection, frequently due to some form of selection bias. The bias may also be caused by differences between the policy or population in the training data and that of the deployment environment. In some instances, the very deployment of the decision support tool can change practice and lead to future shifts in policy in the training environment. The features causing such biases can be difficult to detect compared to the often prespecified protected attributes (e.g., race or gender) typically considered in works concerned with bias as it relates to fairness. In this tutorial, we will show the audience real examples of the challenges associated with deploying machine learning driven decision aids to demonstrate how common they are. We will also introduce concepts and terminology to help them frame issues related to dataset shift and think about how it may occur in their own applications. Finally, we will also give an overview of the types of solutions currently available, their applicability, and their respective pros and cons.
What is CELLOMICS? What does CELLOMICS mean? CELLOMICS meaning, definition & explanation
 
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What is CELLOMICS? What does CELLOMICS mean? CELLOMICS meaning - CELLOMICS pronunciation - CELLOMICS definition - CELLOMICS explanation - How to pronounce CELLOMICS? Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Cellomics is the discipline of quantitative cell analysis using bioimaging methods and informatics with a workflow involving three major components: image acquisition, image analysis, and data visualization and management. These processes are generally automated. All three of these components depend on sophisticated software to acquire qualitative data, quantitative data, and the management of both images and data, respectively. Cellomics is also a trademarked term, which is often used interchangeably with high-content analysis (HCA) or high-content screening (HCS), but cellomics infers HCA/HCS with the addition of sophisticated informatics tools. HCS and the discipline of cellomics was pioneered by a once privately held company named Cellomics Inc. who commercialized instruments, software, and reagents to facilitate the study of cells in culture, and more specifically, their responses to potentially therapeutic drug-like molecules. In 2005, Cellomics was acquired by Fisher Scientific International, Inc., now Thermo Fisher Scientific who continues developing cellomics-centered products under its Thermo Scientific™ high content analysis product line. Like many of the -omics, e.g., genomics and proteomics, applications have grown in depth and breadth over time. Currently there are over 40 different application areas that cellomics is used in, including the analysis of 3-D cell models, angiogenesis, and cell-signalling. Originally a tool used by the pharmaceutical industry for screening, cellomics has now expanded into academia to better understand cell function in the context of the cell. Cellomics is used in both academic and industrial life science research in areas such as cancer research, neuroscience research, drug discovery, consumer products safety, and toxicology, however, there are many more areas for which cellomics could provide a much deeper understanding of cellular function. With HCA at its core, cellomics incorporates the flexibility of fluorescence microscopy, the automation and capacity of the plate reader, and flow cytometry’s multi-parametric analysis in order to extract data from single-cells or from a population of cells. Once an image is acquired using high content technology hardware, cell data is extracted from that image using image analysis software. Single cell data or population data may be of interest, but for both, a series of steps is followed with varying degrees of user interaction depending on the application and the software being used. The first step is segmenting the cells in the image which provides the software algorithms with the information it needs for downstream processing of individual cell measurements. Next, a user must define the area(s) of interest based on a multitude of parameters, i.e., the area a user wants to measure. After the area of interest has been defined, measurements are collected. The measurements, oftentimes referred to as features, are dictated by the type of data desired from the sample. There are many mathematical algorithms powering all of these steps, and each image analysis software package provides its own level of openness to the mathematical algorithms being used. Large amounts of images and data need to be managed when doing cellomics research. Data and image volumes can quickly range from 11MB to 1TB in less than a year which is why cellomics uses the power of informatics to collect, organize, and archive all of this information. Secure and effective data mining requires the associated metadata to be captured and integrated into the data management model. Due to the critical nature of cellomics data management, implementing cellomics studies often requires inter-departmental cooperation between information technology and the life science research group leading the study.
Views: 39 The Audiopedia
"Automated Digital Forensics" (CRCS Lunch Seminar)
 
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CRCS Lunch Seminar (Monday, October 18, 2010) Speaker: Simson Garfinkel, Naval Postgraduate School Title: Automated Digital Forensics Abstract: Despite what you may have seen in the movies, today the primary use of digital forensics is to demonstrate the presence of child pornography on the computer systems of suspected criminal perpetrators. Although digital forensics has a great potential for providing criminal leads and assisting in criminal investigations, today's tools are incredibly difficult to use and there is a nationwide shortage of trained forensic investigators. As a result, computer forensics is most often a tool used for security convictions, not for performing investigations. This talk presents research aimed at realizing the goal of Automated Digital Forensics—research that brings the tools of data mining and artificial intelligence to the problems of digital forensics. The ultimate goal of this research is to create automated tools that will be able to ingest a hard drive or flash storage device and produce a high-level reports that be productively used by relatively untrained individuals. This talk will present: * A brief introduction to digital forensics and related privacy issues. * Histogram Analysis -- Using Frequency and Context to understand disks without understanding files. • Instant Drive Analysis, our work which allows the contents of a 1TB hard drive to be inventoried in less than 45 seconds using statistical sampling. • Our efforts to build Standardized Forensic Corpora of files and disk images, so that work different practitioners can be scientifically compared. Many of the tools and much of the data that we will discuss can be downloaded from the author's websites at http://afflib.org/ and http://digitalcorpora.org/ Bio: Simson L. Garfinkel is an Associate Professor at the Naval Postgraduate School in Monterey, California. His research interests include computer forensics, the emerging field of usability and security, personal information management, privacy, information policy and terrorism. He holds six US patents for his computer-related research and has published dozens of journal and conference papers in security and computer forensics. Garfinkel is the author or co-author of fourteen books on computing. He is perhaps best known for his book Database Nation: The Death of Privacy in the 21st Century. Garfinkel's most successful book, Practical UNIX and Internet Security (co-authored with Gene Spafford), has sold more than 250,000 copies and been translated into more than a dozen languages since the first edition was published in 1991. Garfinkel is also a journalist and has written more than a thousand articles about science, technology, and technology policy in the popular press since 1983. He started writing about identity theft in 1988. He has won numerous national journalism awards, including the Jesse H. Neal National Business Journalism Award two years in a row for his "Machine shop" series in CSO magazine. Today he mostly writes for Technology Review Magazine and the technologyreview.com website. As an entrepreneur, Garfinkel founded five companies between 1989 and 2000. Two of the most successful were Vineyard.NET, which provided Internet service on Martha's Vineyard to more than a thousand customers from 1995 through 2005, and Sandstorm Enterprises, an early developer of commercial computer forensic tools. Garfinkel received three Bachelor of Science degrees from MIT in 1987, a Master's of Science in Journalism from Columbia University in 1988, and a Ph.D. in Computer Science from MIT in 2005.
Views: 958 Harvard's CRCS
Data Science for Consultants - Descriptive Analytics & Data Mining using SAP
 
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This video illustrates different data mining techniques commonly used to build deer understanding of the data set, loosely termed this activity is also called "slice & dice". It is a key process to identify the relvant attributes and also to identify egenral patterns in data set. This understanding helps in buidling accurate model. These techiques are very often are also used in conducting Descriptive Analytics.
Views: 116 Rajeev Sinha
Data Mining in Genomics & Proteomics Journals | OMICS Publishing Group
 
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This video is about automatic extraction of useful, often previously unknown information from large databases. Data mining, now-a-days, is an important tool to transform data into information. Study of the genomes of organisms is nothing but Genomics. It deals with the organized use of genome information to ply new biological knowledge. Proteomics is the study of proteins, especially their structures and functions.It is an Open Access, peer reviewed, online journal by OMICS Publishing Group which encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomics, genomic, proteomic, and metabolomics data. There are many applications of data mining in real world. To access more information about Journal of Data Mining in Genomics & Proteomics please follow OMICS Publishing Group's official page http://www.omicsonline.org/jdmgphome.php
Creating Association Rules using the SQL Server Data Mining Addin for Excel
 
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Association Rules are a quick and simple technique to identify groupings of products that are often sold together. This makes them useful for identifying products that could be grouped together in cross-sell campaigns. Association rules are also known as Market Basket Analysis, as they used to analyse a virtual shopping baskets. In this tutorial I will demonstrate how to create association rules with the Excel data mining addin that allows you to leverage the predictive modelling algorithms within SQL Server Analysis Services. Sample files that allow you follow along with the tutorial are available from my website at http://www.analyticsinaction.com/associationrules/ I also have a comprehensive 60 minute T-SQL course available at Udemy : https://www.udemy.com/t-sql-for-data-analysts/?couponCode=ANALYTICS50%25OFF
Views: 8036 Steve Fox
Intelligent Heart Disease Prediction System using Data Mining Techniques
 
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Intelligent Heart Disease Prediction System using Data Mining Techniques To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9. Mobile: (0)9952649690, Email: [email protected], Website: https://www.jpinfotech.org The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not ";mined"; to discover hidden information for effective decision making. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining techniques can help remedy this situation. This research has developed a prototype Intelligent Heart Disease Prediction System (IHDPS) using data mining techniques, namely, Decision Trees, Naive Bayes and Neural Network. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. IHDPS can answer complex ";what if"; queries which traditional decision support systems cannot. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease, to be established. IHDPS is Web-based, user-friendly, scalable, reliable and expandable. It is implemented on the .NET platform.
Views: 247 jpinfotechprojects
Mining Software Repository Made Easy - Boa Language and its Data Store
 
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Software repositories, e.g. SourceForge, GitHub, etc. contain an enormous corpus of software and information about software. Scientists and engineers alike are interested in analyzing this wealth of information both for curiosity as well as for testing important research hypotheses. However, the current barrier to entry is prohibitive and the cost of such scientific experiments great. Furthermore, these experiments are often irreproducible. This talk will describe our work on the Boa language and its data-intensive infrastructure. In a nutshell, Boa aims to be for open source-related research as Mathematica is to numerical computing, R is for statistical computing, and Verilog and VHDL is for hardware description. Our evaluation shows that Boa significantly decreases the burden of the scientists and engineers analyzing human and technical aspects of open source software development allowing them to focus on the essential tasks of scientific research. This is a collaborative work with Robert Dyer, Hoan Nguyen and Tien Nguyen all at Iowa State University.
Views: 697 Microsoft Research