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UML Use Case Diagram Tutorial
 
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Learn how to make Use Case Diagrams in this tutorial. Both beginners and intermediate UML diagrammers will find all the necessary training and examples on systems, actors, use cases, and include and extend relationships. UML Use Case Diagrams show a system or application; then they show the people, organizations, or other systems that interact with it; and finally, they show a basic flow of what the system or application does. This tutorial explains the four main characteristics of Use Case Diagrams: systems, actors, use cases, and relationships. A system is whatever you’re developing. It could be a website, a software component, a business process, an app, or any number of other things. You represent a system with a rectangle. The next aspect of Use Case Diagrams are actors. An actor is going to be someone or something that uses our system to achieve a goal, and they're represented by a stick figure. Use Cases are elements that really start to describe what the system does. They're depicted with an oval shape and they represent an action that accomplishes some sort of task within the system. The final element in Use Case Diagrams are relationships, which show how actors and use cases interact with each other. There are different types of relationships (like association, include, extend, and generalization) that are represented by varying types of lines and arrows. —— Learn more and sign up: http://www.lucidchart.com Follow us: Facebook: https://www.facebook.com/lucidchart Twitter: https://twitter.com/lucidchart Instagram: https://www.instagram.com/lucidchart LinkedIn: https://www.linkedin.com/company/lucidsoftware
Views: 679373 Lucidchart
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: 51552 edureka!
Amazing Things NLP Can Do!
 
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In this video I want to highlight a few of the awesome things that we can do with Natural Language Processing or NLP. NLP basically means getting a computer to understand text and help you with analysis. Some of the major tasks that are a part of NLP include: · Automatic summarization · Coreference resolution · Discourse analysis · Machine translation · Morphological segmentation · Named entity recognition (NER) · Natural language generation · Natural language understanding · Optical character recognition (OCR) · Part-of-speech tagging · Parsing · Question answering · Relationship extraction · Sentence breaking (also known as sentence boundary disambiguation) · Sentiment analysis · Speech recognition · Speech segmentation · Topic segmentation and recognition · Word segmentation · Word sense disambiguation · Lemmatization · Native-language identification · Stemming · Text simplification · Text-to-speech · Text-proofing · Natural language search · Query expansion · Automated essay scoring · Truecasing Let’s discuss some of the cool things NLP helps us with in life 1. Spam Filters – nobody wants to receive spam emails, NLP is here to help fight span and reduce the number of spam emails you receive. No it is not yet perfect and I’m sure we still all still receive some spam emails but imagine how many you’d get without NLP! 2. Bridging Language Barriers – when you come across a phrase or even an entire website in another language, NLP is there to help you translate it into something you can understand. 3. Investment Decisions – NLP has the power to help you make decisions for financial investing. It can read large amounts of text (such as news articles, press releases, etc) and can pull in the key data that will help make buy/hold/sell decisions. For example, it can let you know if there is an acquisition that is planned or has happened – which has large implications on the value of your investment 4. Insights – humans simply can’t read everything that is available to us. NLP helps us summarize the data we have and pull out meaningful information. An example of this is a computer reading through thousands of customer reviews to identify issues or conduct sentiment analysis. I’ve personally used NLP for getting insights from data. At work, we conducted an in depth interview which included several open ended response type questions. As a result we received thousands of paragraphs of data to analyze. It is very time consuming to read through every single answer so I created an algorithm that will categorize the responses into one of 6 categories using key terms for each category. This is a great time saver and turned out to be very accurate. Please subscribe to the YouTube channel to be notified of future content! Thanks! https://en.wikipedia.org/wiki/Natural_language_processing https://www.lifewire.com/applications-of-natural-language-processing-technology-2495544
Views: 7014 Story by Data
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: 45629 DeepLearning.TV
Introduction to Text Analysis with NVivo 11 for Windows
 
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It’s easy to get lost in a lot of text-based data. NVivo is qualitative data analysis software that provides structure to text, helping you quickly unlock insights and make something beautiful to share. http://www.qsrinternational.com
Views: 147217 NVivo by QSR
UCAnalyzer: A Tool to Analyze Use Case Textual Descriptions
 
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Use Case Analyzer, is a tool analyzes the use case textual description for the natural language issues such as incompleteness, inconsistency, incorrectness, redundancy, and ambiguity that are inevitably introduced in the specification.
Views: 190 Saurabh Tiwari
Analysis
 
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This video introduces an example program of a bank and goes through a little analysis work with a UML use case diagram.
Views: 1026 Mark Lewis
Real Time Project - Data Extraction Process - Automation Anywhere 10.7 | Artificial Intelligence
 
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NLP(Text Mining)
 
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NLP
Views: 167 Amarnadh paritala
Agile User Stories
 
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Learn how to write good user stories for Agile teams. If you'd like a free book on this topic, please see below... I've published a book called "Starting Agile" that is designed to help you start your team's Agile journey out right. You can buy a copy from Amazon, but I'm giving free copies away to my subscribers from YouTube. You can signup for a copy at this link: https://mailchi.mp/326ba47ba2e8/agile-list
Views: 132190 Mark Shead
Lecture - 39 Natural Language Processing - I
 
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Lecture Series on Artificial Intelligence by Prof.Sudeshna Sarkar and Prof.Anupam Basu, Department of Computer Science and Engineering,I.I.T, Kharagpur . For more details on NPTEL visit http://nptel.iitm.ac.in.
Views: 93258 nptelhrd
Text Analytics - (Natural Language Processing) Using RPA
 
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This video is all about doing text analytics (NLP) using RPA. Connect with me in Linkedin: https://www.linkedin.com/in/vishalraghav10/ Connect with me in FaceBook: https://www.facebook.com/vishal.raghav1 Connect with me in Instagram: https://www.instagram.com/lash_raghav/ Connect with me in Quora: https://www.quora.com/profile/Vishal-Raghav-6
Views: 877 Vishal Raghav
Final Year Projects | Topic Mining over Asynchronous Text Sequences
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 150 Clickmyproject
How to Make a Text Summarizer - Intro to Deep Learning #10
 
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I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 165378 Siraj Raval
Introduction to KPMG Process Mining with Microsoft Power BI
 
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KPMG Process Mining visualizes actual business processes with Microsoft Power BI. The processes are created from transactional data and do not need any user modeling. This ensures process analysis without bias and provides full end-to-end process transparency. It aims to help businesses find and present better alternatives to real-life processes, to make them more efficient and controls more effective. The demonstration shows a procurement process from the creation of a purchase request to a payment release that has been analyzed in three specific use cases. Learn more: https://powerbi.microsoft.com/en-us/
Views: 9686 KPMG
SAS TextMining Introduction
 
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Introduction to Text Mining
Views: 1612 Dothang Truong
Support Vector Machine (SVM) - Fun and Easy Machine Learning
 
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Support Vector Machine (SVM) - Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS COURSE - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML ►MACHINE LEARNING COURSES -http://augmentedstartups.info/machine-learning-courses ------------------------------------------------------------------------ A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. To understand SVM’s a bit better, Lets first take a look at why they are called support vector machines. So say we got some sample data over here of features that classify whether a observed picture is a dog or a cat, so we can for example look at snout length or and ear geometry if we assume that dogs generally have longer snouts and cat have much more pointy ear shapes. So how do we decide where to draw our decision boundary? Well we can draw it over here or here or like this. Any of these would be fine, but what would be the best? If we do not have the optimal decision boundary we could incorrectly mis-classify a dog with a cat. So if we draw an arbitrary separation line and we use intuition to draw it somewhere between this data point for the dog class and this data point of the cat class. These points are known as support Vectors – Which are defined as data points that the margin pushes up against or points that are closest to the opposing class. So the algorithm basically implies that only support vector are important whereas other training examples are ‘ignorable’. An example of this is so that if you have our case of a dog that looks like a cat or cat that is groomed like a dog, we want our classifier to look at these extremes and set our margins based on these support vectors. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 212832 Augmented Startups
Modeling Techniques in Business Analytics with R
 
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( R Training : https://www.edureka.co/r-for-analytics ) There are different techniques of modelling that are applied in R. This video explains different modelling techniques, their functionality and use cases, along with the objective of using each of the techniques. Related Blogs: http://www.edureka.co/blog/sentiment-analysis-methodology/?utm_source=youtube&utm_medium=referral&utm_campaign=modeling-techniques http://www.edureka.co/blog/importingspss-data-r/?utm_source=youtube&utm_medium=referral&utm_campaign=modeling-techniques http://www.edureka.co/blog/data-mining-and-r/?utm_source=youtube&utm_medium=referral&utm_campaign=modeling-techniques Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world. The topics related to ‘Modelling Techniques’ have been covered in our course ‘Business Analytics with R’. For more information, please write back to us at [email protected]
Views: 4247 edureka!
K-Means Clustering Algorithm - Cluster Analysis | Machine Learning Algorithm | Data Science |Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4 Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #kmeans #clusteranalysis #clustering #datascience #machinelearning How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 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. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 72576 edureka!
EASI-CLOUDS demo: opinion mining use case
 
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This demo shows the use of cloud computing and big data to analyze opinions in social networks. The work was developed by ITWorx, an Egyptian partner of the EASI-CLOUDS consortium (www.easi-clouds.eu).
Views: 312 EASI-CLOUDS
Aaron Katz shows the use cases for Elasticsearch
 
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http://ibm.biz/ibmdev-newsletter Get the Developer Webcast Calendar newsletter to learn about new videos and upcoming webcasts from IBM Developer. Aaron Katz (SVP, WW Field Operations, Elastic) starts by asking who in the audience has searched on Facebook, Wikipedia, Uber, or Tinder in the past 24 hours because all of those companies use Elasticsearch technology. Aaron notes that IBM is using Elasticsearch on its Bluemix platform and the Watson Developer site, etc. --WikiMedia - "Elasticsearch is the backbone across all Wikimedia's sites, powering billions of real-time user prefix and full text searches every day." - Chad Horohoe --Mozilla - "Elasticsearch, logstash, and Kibana allow for real time indexing, search, and analytics for over 300 million events per day. This protects our network, services, and systems from security threats. " -- Jeff Bryner --Verizon - "Using Elasticsearch, we index more than 500 billion documents for real-time logging and analytics for our mission critical applications." - Bhaskar Karambelkar --Goldman Sachs - "Elasticsearch is one of the top 5 strategic technologies for the Bank - it is literally saving us hundreds of thousands of hours of people time." - Don Duet --Facebook - "We process 60 million queries a day enabling search and analytics for critical internal applications - and using Shield, all of this data is protected from interception and corruption." - Peter Vulgaris --NASA - "With the ELK Stack, we log more than 30,000 messages and 100,000 documents four times every day from the Mars Rover to optimize our space missions." - Dan Isla Aaron notes that Elasticsearch is one of the most popular open source projects today: URL: https://developer.ibm.com/tv/videos/aaron-katz-shows-the-use-cases-for-elasticsearch/ IBM Owner: Calvin Powers
Views: 26518 IBM Developer
Analyzing Text using BigInsights Text Analytics Web Tooling Skill Builder
 
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This video will demonstrate how to use the text analytics module in BigInsights to extract contextual information from text documents. Course Links: BD085EN - Text Analytics with AQL programming (Big Data University): http://bigdatauniversity.com/bdu-wp/bdu-course/text-analytics-essentials/ DW653 - BigInsights Analytics for Programmers: http://www.ibm.com/services/learning/ites.wss/zz/en?pageType=course_description&courseCode=DW653G&cc= Other Links: Training Paths: http://www.ibm.com/services/learning/ites.wss/zz/en?pageType=page&c=a0003096 BigInsights Training Path: http://www.ibm.com/services/learning/ites.wss/zz/en?pageType=page&c=P869090H78414O98
Views: 1574 IBM Analytics Skills
How to perform text analytics using IBM SPSS Modeler Premium (Part 3 of 3)
 
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For those interested in how they can analyse customer comments or indeed any fee-text data, Jarlath Quinn illustrates how to exploit the power of the text analytics engine contained in SPSS Modeler Premium. This short three-part series of videos shows how to get the best from the in-built text mining resources of Modeler Premium in order to accurately classify text data and capture customer sentiment.
Views: 3365 Smart Vision Europe
Excel Tutorial - Cleaning Up Data with TRIM, PROPER, and Text to Columns
 
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In this Excel tutorial, you'll learn how to clean up data using the TRIM, PROPER, and Text to Columns functions (and more). By http://breakingintowallstreet.com/ "Financial Modeling Training And Career Resources For Aspiring Investment Bankers" Why Do You Need to "Clean Up" Data? Often you've pasted in data from websites or PDFs or other sources, and you get lots of ugly formatting and other problems, such as extra spaces, non-printable characters, etc. Also, data may be grouped together in cases where it's better to be separated (as in the address data here). This happens all the time on the job, and cleaning up the data makes your life easier and makes it 100x easier to manipulate and analyze it. You COULD go in and manually fix it, but you might want to jump off the roof of a tall building after doing that. Instead, we'll use these functions to automate the process: Text Manipulation Formulas (Across all PC and Mac versions): =TRIM Remove extra spaces =PROPER Makes first letter in each word uppercase =CLEAN Removes all non-printable characters from text =UPPER Capitalizes all letters in all words =LOWER Turns all letters in all words to lowercase Alt + A + E / Alt + D + E Text to Columns Ctrl + C Copy (CMD + C on the Mac) Alt + E + S + F Paste Formulas (Ctrl + CMD + V, CMD + F on the Mac) Alt + E + S + V Paste Values (Ctrl + CMD + V, CMD + V on the Mac) Alt + O + C + A Auto-Fit Column Width Alt + H + C + A Center Text How to Clean Up This Data in 5 Steps: 1. First, remove all the extra spaces and capitalize each individual word with TRIM and PROPER - could throw in CLEAN for good measure. 2. Then, separate everything into separate columns with the "Text to Column" function. May have to apply this several times if different characters separate each type of data (commas vs. spaces). 3. Fix anything that still requires fixing in these separate columns - capitalize all state abbreviations, make sure ZIP codes with trailing 0 still work properly (change format to text), and so on. May also need to apply additional TRIMs here. Must be really careful with copying and pasting data as values - have to do that to avoid errors! 4. Add column headers at the top, based on copy and paste of original header. 5. Delete extra rows/columns and shift everything over or up properly. What Next? Go apply this to real data that you're working with... depends a bit on the specific problems with the data, but you can never go wrong with TRIM, PROPER, and Text to Columns! If you're more advanced, you could try automating this entire process with VBA and macros, but that also gets complicated and may not save you much time since you need to know what the data looks like before writing code for that.
The Text and Data mining functionalities of the PoolParty Semantic Suite
 
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This webinar introduces PoolParty Semantic Suite, the main software product of Semantic Web Company (SWC), one of the leading providers of graph-based metadata, search, and analytic solutions. PoolParty is a world-class semantic technology suite that offers sharply focused solutions to your knowledge organization and content business. PoolParty is the most complete semantic middleware on the global market. You can use it to enrich your information with valuable metadata to link your business and content assets automatically. This webinar focuses on the text-mining and entity- / text extraction capability of PoolParty Semantic Suite that is used for: • support of the continuous modelling of industrial knowledge graphs (as a supervised learning system) • for entity linking and data integration • for classification and semantic annotation mechanisms • and thereby for downstream applications like semantic search, recommender systems or intelligent agents The webinar presents and explains these features in the PoolParty software environment, shows demos based on real world use cases and finally showcases 3rd party integrations (e.g. into Drupal CMS).
Views: 174 AIMS CIARD
Data Scientist Resume | Data Scientist Jobs, Salary & Skills | Data Science Training | Edureka
 
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** Data Scientist Master Program: https://www.edureka.co/masters-program/data-scientist-certification ** This session on Data Scientist Resume will help you understand the demand and the growth of a Data Scientist and their impact on the business world. The following topics are covered in this session: 1. Who is a Data Scientist? 2. Data Scientist Job Trends 3. Data Scientist Salary Trends 4. Job Description 5. Skills required 6. Data Scientist Resume Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist ------------------------------------- Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV Edureka! organizes live instructor-led webinars on the latest technologies, to stay updated, join our Meetup community: http://meetu.ps/c/4glvl/JzH2K/f Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Slideshare: https://www.slideshare.net/EdurekaIN/ #edureka #datascienceedureka #datascientistresume #datascientistcareer -------------------------------------- How it Works? 1. This is a 30-hour Instructor-led Online Course. 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 be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate! ------------------------------------- About the Course Edureka's Data Science Training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. Data Science Training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR. ------------------------------------- Who should go for this course? The market for Data Analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Our Data Science Training helps you to grab this opportunity and accelerate your career by applying the techniques on different types of Data. It is best suited for: Developers aspiring to be a 'Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Machine Learning (ML) Techniques Information Architects who want to gain expertise in Predictive Analytics 'R' professionals who wish to work Big Data Analysts wanting to understand Data Science methodologies ------------------------------------- Why learn Data Science? Data science is an evolutionary step in interdisciplinary fields like the business analysis that incorporate computer science, modeling, statistics, and analytics. To take complete benefit of these opportunities, you need structured training with an updated curriculum as per current industry requirements and best practices. Besides strong theoretical understanding, you need to work on various real-life projects using different tools from multiple disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes. Additionally, you need the advice of an expert who is currently working in the industry tackling real-life data-related challenges. ------------------------------------- Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For more information, Please write back to us at [email protected]dureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free).
Views: 5713 edureka!
Comprehensive Power BI Desktop Example: Visualize Excel Data & Build Dynamic Dashboard (EMT 1360)
 
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Download File: http://people.highline.edu/mgirvin/excelisfun.htm See how to use Power BI Desktop to import, clean and transform Sales Tables from Multiple Excel Files and consolidate into a Single Proper Data Set that can be linked in a Relationship to other tables, and then build DAX Calculated Columns & Measures for Gross Profit that can be used in a Dynamic Dashboard with Map, Column Chart, Line Chart, Card and Slicer visualizations. During the whole process we will compare and contrast how the process is similar and different from Excel’s Power Query and Power Pivot DAX. The steps we will see in this video are: 1. (00:17) Introduction to entire process for Power BI Desktop, including looking at the finished Dashboard 2. (04:50) Import Multiple Excel Files From Folder 3. (05:44) Name Query 4. (06:02) Transform extension column to lowercase 5. (06:34) Filter Files to only include “.xlsx” file extensions 6. (07:05) Remove Columns 7. (07:18) November 2016 Power Query Update Problem 8. (08:05) Add Custom Column with Excel.Workbook Function to extract the Excel Objects from each File. 9. (09:40) Delete Content Column 10. (10:41) Filter to only include Excel Sheet Objects 11. (11:06) Filter to exclude sheets that contain the word “Sheet” 12. (11:40) Remove Columns 13. (11:51) Expand Data and Sheet Name Columns 14. (12:06) Change Field Names 15. (12:22) Change Data Types 16. (14:05) Add Custom Column to calculate Net Revenue Column then round Number.Round function. Then Add Fixed Decimal Data Type. 17. (15:59) Remove columns for Amount and Revenue Discount 18. (16:10) Close and Apply to add to Data Model 19. (17:05) Import Excel Manager Table. Change Data Types to Text. Close and Apply 20. (18:10) Create Relationship between Zip Code Columns 21. (19:03) Create DAX Calculated Column with the IF Function to Categorize Retail Data. Change Data Type. 22. (21:53) Create DAX Measures for: Total Revenue, Total COGS and Gross Profit. Add Currency Number Formatting with No Decimals Showing. 23. (24:28) Create DAX Measures for: Gross Profit Percentage. Add Percentage Number Formatting with Two Decimals Showing. 24. (25:35) Create Map Visualization for Zip Code & Gross Profit Data (Zip Code with relationship to Managers) 25. (26:20) Create Clustered Bar for Manager Names & Gross Profit Data (Zip Code with relationship to Managers) 26. (27:15) Create Clustered Column for Product & Gross Profit Data, with a Line Chart for Gross Profit Percentage 27. (28:19) Create Clustered Column for Payment Method & Gross Profit Data, with a Line Chart for Gross Profit Percentage 28. (28:45) Create Slicer for States. 29. (29:00) Create Card Visualization for Total Revenue, Total COGS, Gross Profit and Gross Profit Percentage. 30. (29:57) Summary Learn Power BI Desktop Basics. Introduction to Power BI Desktop. Getting Started with Power BI Desktop. Create Impactful Reports With Power BI Desktop. Microsoft Power BI.
Views: 137208 ExcelIsFun
2  Data Science Practical Use Case
 
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Session 2 - Egyptian Big Data Geeks 2nd Meetup - ITI - Cairo Join us: https://www.facebook.com/groups/big.data.egypt/ Speaker: Mohamed Aref, https://www.linkedin.com/in/maref/ https://www.facebook.com/mohamed.aref.355
Predictive Maintenance, Part 2: Feature Extraction for Identifying Condition Indicators
 
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Learn about different maintenance strategies and predictive maintenance workflow. - MATLAB and Simulink for Predictive Maintenance: http://bit.ly/2E5LRgh - Designing Algorithms for Condition Monitoring and Predictive Maintenance: http://bit.ly/2GsiGae - Using Simulink to Generate Fault Data: http://bit.ly/2Gnb7Bw This video explains different maintenance strategies and walks you through a workflow for developing a predictive maintenance algorithm. Predictive maintenance lets you find the optimum time to schedule maintenance by estimating time to failure. It also pinpoints problems in your machinery and helps you identify the parts that need to be fixed. Using predictive maintenance, you can minimize downtime and maximize equipment lifetime. This video uses a triplex pump example to walk you through the predictive maintenance algorithm steps. To develop an algorithm, you need a large set of sensor data collected under different operating conditions. In cases, where sensor data is not enough, you can use simulation data that is representative of failures by creating a model of your machine and simulating faulty operating conditions. For more information on generating failure data using Simulink®, please check out the links given below. Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Views: 4267 MATLAB
Introduction To Text Analytics - Webinar by Ellicium
 
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This webinar gives you brief idea about what text analytics is. It includes brief history of text analytics, current use cases and future applications. You will also get to see the demo of Gadfly, Ellicium's own text analytics platform.
Symmetric Key and Public Key Encryption
 
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Modern day encryption is performed in two different ways. Check out http://YouTube.com/ITFreeTraining or http://itfreetraining.com for more of our always free training videos. Using the same key or using a pair of keys called the public and private keys. This video looks at how these systems work and how they can be used together to perform encryption. Download the PDF handout http://itfreetraining.com/Handouts/Ce... Encryption Types Encryption is the process of scrambling data so it cannot be read without a decryption key. Encryption prevents data being read by a 3rd party if it is intercepted by a 3rd party. The two encryption methods that are used today are symmetric and public key encryption. Symmetric Key Symmetric key encryption uses the same key to encrypt data as decrypt data. This is generally quite fast when compared with public key encryption. In order to protect the data, the key needs to be secured. If a 3rd party was able to gain access to the key, they could decrypt any data that was encrypt with that data. For this reason, a secure channel is required to transfer the key if you need to transfer data between two points. For example, if you encrypted data on a CD and mail it to another party, the key must also be transferred to the second party so that they can decrypt the data. This is often done using e-mail or the telephone. In a lot of cases, sending the data using one method and the key using another method is enough to protect the data as an attacker would need to get both in order to decrypt the data. Public Key Encryption This method of encryption uses two keys. One key is used to encrypt data and the other key is used to decrypt data. The advantage of this is that the public key can be downloaded by anyone. Anyone with the public key can encrypt data that can only be decrypted using a private key. This means the public key does not need to be secured. The private key does need to be keep in a safe place. The advantage of using such a system is the private key is not required by the other party to perform encryption. Since the private key does not need to be transferred to the second party there is no risk of the private key being intercepted by a 3rd party. Public Key encryption is slower when compared with symmetric key so it is not always suitable for every application. The math used is complex but to put it simply it uses the modulus or remainder operator. For example, if you wanted to solve X mod 5 = 2, the possible solutions would be 2, 7, 12 and so on. The private key provides additional information which allows the problem to be solved easily. The math is more complex and uses much larger numbers than this but basically public and private key encryption rely on the modulus operator to work. Combing The Two There are two reasons you want to combine the two. The first is that often communication will be broken into two steps. Key exchange and data exchange. For key exchange, to protect the key used in data exchange it is often encrypted using public key encryption. Although slower than symmetric key encryption, this method ensures the key cannot accessed by a 3rd party while being transferred. Since the key has been transferred using a secure channel, a symmetric key can be used for data exchange. In some cases, data exchange may be done using public key encryption. If this is the case, often the data exchange will be done using a small key size to reduce the processing time. The second reason that both may be used is when a symmetric key is used and the key needs to be provided to multiple users. For example, if you are using encryption file system (EFS) this allows multiple users to access the same file, which includes recovery users. In order to make this possible, multiple copies of the same key are stored in the file and protected from being read by encrypting it with the public key of each user that requires access. References "Public-key cryptography" http://en.wikipedia.org/wiki/Public-k... "Encryption" http://en.wikipedia.org/wiki/Encryption
Views: 496993 itfreetraining
Data Processing in Google Cloud: Hadoop, Spark, and Dataflow  (Cloud Next '19)
 
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Google Cloud is committed to making your infrastructure as easy-to-use as possible. Naturally, ""easy"" means different things to different people and organizations. In data processing, sometimes that means migrating your existing Hadoop and Spark environment to Cloud Dataproc, which delivers a familiar feel. For others, easy will mean putting Cloud Dataflow's serverless, unified batch and stream data processing into production. In this session, we'll explore the ins and ours of making this decision, with real-world experience form Qubit, who has used both Dataproc and Dataflow in production. Big data analytics → https://bit.ly/2U1EY4g Watch more: Next '19 Data Analytics Sessions here → https://bit.ly/Next19DataAnalytics Next ‘19 All Sessions playlist → https://bit.ly/Next19AllSessions Subscribe to the G Suite Channel → https://bit.ly/G-Suite1 Speaker(s): Sergei Sokolenko, Christopher Crosbie, Ravi Upreti Session ID: DA203 product:Cloud Dataflow,Cloud Dataproc,BigQuery; fullname:Christopher Crosbie,Sergei Sokolenko;
Views: 1161 G Suite
Loch Ness Monster Text Mining and Power BI
 
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Use RapidMiner to extract words from the Loch Ness Monster sightings and visualize the data with Power BI. Use cases for text mining include call centers notes, claim descriptions, and project notes.
Views: 307 CDO Advisors
Text Mining the Contributors to Rail Accidents||ieee 2017 java projects at bangalore
 
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We are providing a Final year IEEE project solution & Implementation with in short time. If anyone need a Details Please Contact us Mail: [email protected] or [email protected] Phone: 09842339884, 09688177392 Watch this also: https://www.youtube.com/channel/UCDv0caOoT8VJjnrb4WC22aw ieee projects, ieee java projects , ieee dotnet projects, ieee android projects, ieee matlab projects, ieee embedded projects,ieee robotics projects,ieee ece projects, ieee power electronics projects, ieee mtech projects, ieee btech projects, ieee be projects,ieee cse projects, ieee eee projects,ieee it projects, ieee mech projects ,ieee e&I projects, ieee IC projects, ieee VLSI projects, ieee front end projects, ieee back end projects , ieee cloud computing projects, ieee system and circuits projects, ieee data mining projects, ieee image processing projects, ieee matlab projects, ieee simulink projects, matlab projects, vlsi project, PHD projects,ieee latest MTECH title list,ieee eee title list,ieee download papers,ieee latest idea,ieee papers,ieee recent papers,ieee latest BE projects,ieee B tech projects| Engineering Project Consultants bangalore, Engineering projects jobs Bangalore, Academic Project Guidance for Electronics, Free Synopsis, Latest project synopsiss ,recent ieee projects ,recent engineering projects ,innovative projects| Computer Software Project Management Consultants, Project Consultants For Electrical, Project Report Science, Project Consultants For Computer, ME Project Education Consultants, Computer Programming Consultants, Project Consultants For Bsc, Computer Consultants, Mechanical Consultants, BCA live projects institutes in Bangalore, B.Tech live projects institutes in Bangalore,MCA Live Final Year Projects Institutes in Bangalore,M.Tech Final Year Projects Institutes in Bangalore,B.E Final Year Projects Institutes in Bangalore , M.E Final Year Projects Institutes in Bangalore,Live Projects,Academic Projects, IEEE Projects, Final year Diploma, B.E, M.Tech,M.S BCA, MCA Do it yourself projects, project assistance with project report and PPT, Real time projects, Academic project guidance Bengaluru| Image Processing ieee projects with source code,VLSI projects source code,ieee online projects.best projects center in Chennai, best projects center in trichy, best projects center in bangalore,ieee abstract, project source code, documentation ,ppt ,UML Diagrams,Online Demo and Training Sessions|Rail safety, safety engineering, latent Dirichlet allocation, partial least squares, random forests|PLS predictor|RMSE from cross-validation with different numbers of components.
CAREERS IN DATA ANALYTICS - Salary , Job Positions , Top Recruiters
 
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CAREERS IN DATA ANALYTICS - Salary , Job Positions , Top Recruiters What IS DATA ANALYTICS? Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses. As a term, data analytics predominantly refers to an assortment of applications, from basic business intelligence (BI), reporting and online analytical processing (OLAP) to various forms of advanced analytics. In that sense, it's similar in nature to business analytics, another umbrella term for approaches to analyzing data -- with the difference that the latter is oriented to business uses, while data analytics has a broader focus. The expansive view of the term isn't universal, though: In some cases, people use data analytics specifically to mean advanced analytics, treating BI as a separate category. Data analytics initiatives can help businesses increase revenues, improve operational efficiency, optimize marketing campaigns and customer service efforts, respond more quickly to emerging market trends and gain a competitive edge over rivals -- all with the ultimate goal of boosting business performance. Depending on the particular application, the data that's analyzed can consist of either historical records or new information that has been processed for real-time analytics uses. In addition, it can come from a mix of internal systems and external data sources. Types of data analytics applications : At a high level, data analytics methodologies include exploratory data analysis (EDA), which aims to find patterns and relationships in data, and confirmatory data analysis (CDA), which applies statistical techniques to determine whether hypotheses about a data set are true or false. EDA is often compared to detective work, while CDA is akin to the work of a judge or jury during a court trial -- a distinction first drawn by statistician John W. Tukey in his 1977 book Exploratory Data Analysis. Data analytics can also be separated into quantitative data analysis and qualitative data analysis. The former involves analysis of numerical data with quantifiable variables that can be compared or measured statistically. The qualitative approach is more interpretive -- it focuses on understanding the content of non-numerical data like text, images, audio and video, including common phrases, themes and points of view. At the application level, BI and reporting provides business executives and other corporate workers with actionable information about key performance indicators, business operations, customers and more. In the past, data queries and reports typically were created for end users by BI developers working in IT or for a centralized BI team; now, organizations increasingly use self-service BI tools that let execs, business analysts and operational workers run their own ad hoc queries and build reports themselves. Keywords: being a data analyst, big data analyst, business analyst data warehouse, data analyst, data analyst accenture, data analyst accenture philippines, data analyst and data scientist, data analyst aptitude questions, data analyst at cognizant, data analyst at google, data analyst at&t, data analyst australia, data analyst basics, data analyst behavioral interview questions, data analyst business, data analyst career, data analyst career path, data analyst career progression, data analyst case study interview, data analyst certification, data analyst course, data analyst in hindi, data analyst in india, data analyst interview, data analyst interview questions, data analyst job, data analyst resume, data analyst roles and responsibilities, data analyst salary, data analyst skills, data analyst training, data analyst tutorial, data analyst vs business analyst, data mapping business analyst, global data analyst bloomberg, market data analyst bloomberg
Views: 29199 THE MIND HEALING
Building with Watson: Introduction to Watson Natural Language Understanding
 
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In this Building with Watson webinar, IBM Watson developer Joshua Elliott demonstrates how Watson's Natural Language Understanding service works by analyzing text to extract meta-data from content and provides some common use cases. For more information on NLU, visit https://ibm.co/2oLMVLV
Views: 8576 IBM Watson
Legal tech - How to Use Case Report? | Premonition Analytics
 
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Legal tech: How to Open Case Report? The World's Largest Litigation Database Analytics https://www.Premonition.ai Premonition Analytics Get an insider look into Premonition. Get A Free Demo: https://www.premonition.ai/take-a-tour/ Video Transcript: This is premonitions open case report. here we put in the name of the court and then the name of the company .that we wish to look at and search we have under the Advanced Settings .the criteria that we're looking for basically it concerns us if someone has a win rate. Over seventy-five percent or below five percent and we're ideally looking for a minimum of five occurrences in the last three years. like that and we can go to see the number of cases. basically, this is really really simple. it shows all the open cases for that company and whether the company needs to worry about the case .so there are currently 65 open cases for Discover Bank in the County Florida 28 of them have been marked red 19 yellow and 18 green .so for red you have a problem with the judge or you have a problem with the opposing counsel or your counsel sample here to the point turmoil that only has a 25 percent success rate encountered these are all plaintiff cases for them less than half the time here that appointed lawyer who's had two appearances and hasn't had any wins. So it gives you a very quick heads up of which cases you need to be concerned about you can either place counsel or settle or look to change venue with green these are cases you really don't need to worry about at all there's decent judge the opposing counsel was not too much of a threat and they have chosen could counsel so here they've chosen lawyer that has six straight wins yellow cases are average there are no major issues but the representation is average see if they were taking premonition Counsel. they wouldn't have any yellow cases at all that basically would be yellow or neither basically green or red because they would always be pointing good counsel though obviously, you are still going to get issues with tough judge sort of opposing counsel from time to time so who might use this what the claims managers and insurance companies this is a very fast very cheap method of reviewing a large number of cases at one time you might also want to check out our claims report for claims software Providers. we can API this right into your system or you can also embed this JavaScript function here straight into your website for general counsel attorneys and law firms .this is again a very quick and easy way to do open case review for stock and insurance analysts obviously good litigation is more profitable than bad.so it's a good idea to know in advance whether the block of litigation is likely to be successful or not and compare it to peers .click on the form below to get a demo of this and please check out our reports especially our new claims report thanks ----------------------------------------------------------------------------------------------------------- Premonition is an Artificial Intelligence system that mines Big Data to find out which Attorneys usually win before which Judges. It is a very, very unfair advantage in Litigation. ----------------------------------------------------------------------------------------------------------- Follow us others Contract: Facebook: https://www.facebook.com/premonitionanalytics/ Twitter: https://twitter.com/Premonition_AI Linkedin: https://www.linkedin.com/company/premonition-analytics Google +: https://plus.google.com/102440054506128693572 Subscribe: https://goo.gl/u4ZNdA Analytics for insider legal news and visit us at https://www.Premonition.ai Thank You for Watching!
Using Decisions In Framing Analytics Problems: A Consulting Perspective - DataEDGE 2015
 
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Using Decisions In Framing Analytics Problems: A Consulting Perspective Friday, May 8, 2015 http://dataedge.ischool.berkeley.edu/2015/schedule/using-decisions-framing-analytics-problems-consulting-perspective In data science applications, a key determinant of success is how the analytical problem is framed, even before any data sources or algorithms are selected. This talk discusses a framework helpful where the goal of analytics is to help organizations use data to make better decisions. Application of the framework begins by asking three key questions related to decision-making, and uses the answers to these questions to guide selection of data sources, algorithms, data visualizations as well as how the organization will use the analytic results: What is the decision being improved by the use of analytics? Who is deciding? What is the value of an improved decision? We’ve found that often analytics project sponsors cannot articulate the answers to these questions, at least at the outset of the project, and that a critical role for us as consultants is to help clients refine the answers, thereby better understanding the problems they are trying to solve refine the answers. Sometimes answering these questions yield results that may be surprising to data scientists, such as that the most technically accurate model may not be the best for a given project or that adding big data to a project may be counterproductive. This talk expands on these questions and illustrates with examples taken from consulting practice. (Note: Some of this talk previews concepts to be covered in the course INFO 290: Managing Analytics Projects, to be taught at the iSchool in the fall of 2015.) David Steier Director, Advanced Analytics and Modeling Deloitte Consulting LLP David Steier is a Director in Deloitte Analytics for Deloitte Consulting LLP’s U.S. Human Capital Practice and is Deloitte’s Technology Black Belt for Unstructured Analytics. Using advanced analytic and visualization techniques, including predictive modeling, social network analysis, and text mining, David and his team of quantitative specialists help clients across a variety of industries to solve some of their most complex technical problems. Prior to joining Deloitte, David was Director of Research at the Center for Advanced Research at PwC, and was a Managing Director at Scient, an e-business services consultancy. David has also authored numerous publications and presentations in applications of advanced technology, including two books and a variety of journal papers, conference papers and workshop presentations. David received his PhD in Computer Science from Carnegie Mellon University, where he is currently an adjunct faculty member teaching a course on Managing Analytics Projects.
Intro to Hypothesis Testing in Statistics - Hypothesis Testing Statistics Problems & Examples
 
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Get the full course at: http://www.MathTutorDVD.com The student will learn the big picture of what a hypothesis test is in statistics. We will discuss terms such as the null hypothesis, the alternate hypothesis, statistical significance of a hypothesis test, and more. In this step-by-step statistics tutorial, the student will learn how to perform hypothesis testing in statistics by working examples and solved problems.
Views: 1464367 mathtutordvd
Fundamentals of Qualitative Research Methods: Data Analysis (Module 5)
 
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Qualitative research is a strategy for systematic collection, organization, and interpretation of phenomena that are difficult to measure quantitatively. Dr. Leslie Curry leads us through six modules covering essential topics in qualitative research, including what it is qualitative research and how to use the most common methods, in-depth interviews and focus groups. These videos are intended to enhance participants' capacity to conceptualize, design, and conduct qualitative research in the health sciences. Welcome to Module 5. Bradley EH, Curry LA, Devers K. Qualitative data analysis for health services research: Developing taxonomy, themes, and theory. Health Services Research, 2007; 42(4):1758-1772. Learn more about Dr. Leslie Curry http://publichealth.yale.edu/people/leslie_curry.profile Learn more about the Yale Global Health Leadership Institute http://ghli.yale.edu
Views: 172320 YaleUniversity
Text Analytics With R | How to Connect Facebook with R | Analyzing Facebook in R
 
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In this text analytics with R tutorial, I have talked about how you can connect Facebook with R and then analyze the data related to your facebook account in R or analyze facebook page data in R. Facebook has millions of pages and getting emotions and text from these pages in R can help you understand the mood of people as a marketer. Text analytics with R,how to connect facebook with R,analyzing facebook in R,analyzing facebook with R,facebook text analytics in R,R facebook,facebook data in R,how to connect R with Facebook pages,facebook pages in R,facebook analytics in R,creating facebook dataset in R,process to connect facebook with R,facebook text mining in R,R connection with facebook,r tutorial for facebook connection,r tutorial for beginners,learn R online,R beginner tutorials,Rprg
Mining Competitors from Large Unstructured datasets(D6)
 
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Guide: K.Sowmya Priya Team Members: 14241A05M8-S. Savitha 14241A05H8-A. Vaishnavi 14241A05K7-M.Prathibha
Word 2016 - Create Citation and Bibliography How to Tutorial in Microsoft Office 365 with Windows 10
 
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This tutorial shows you how to insert and create citations and bibliography sections in your Word 2016 document. I demo how to manage your sources, use Office 365, and the newest and most updated ways to use your references. I also have demos on APA and MLA style formatting, so make sure to check those out if you're writing a research paper. This training is created for beginners to Office who are trying to learn the different programs, and I encourage you to take a look at my other videos and playlists, so that you can learn those programs as well. Best of luck! My goal is to provide you with the best learning experience possible, for all beginners of technology. Please see a list of topics below that my various playlists cover, and don't forget to like and subscribe! Windows 10: Perform Basic Mouse Operations Create Folders Explore the Windows 10 Desktop, Taskbar, and Start Menu Select Multiple Files and Folders Download a File From a Website Word 2016: Create a New Document and Insert Text Insert and Format Graphics Insert and Modify Text Boxes Create a Table Format a Table Present a Word Document Online Create a Research Paper in MLA Format Insert Footnotes in a Research Paper Create Citations and a Bibliography Save a Document Correct Errors as You Type How to Format a Document in APA Format Convert Word Document to a PDF File Microsoft Office Specialist Certification Exam Practice Study Guide APA Format from Default Formatting Table of Contents Tutorial Format Paragraphs Create a Custom Word Template Excel 2016: Create, Save, and Navigate an Excel Workbook Enter Data in a Worksheet How do you Export Access to Excel and Apply Conditional Formatting Use Flash Fill, SUM, Average, Median, and MAX Functions and Formulas Move Data and Rotate Text Graph Data with a Pie Chart Format a Pie Chart MOS Prep - Basic Certification Exam Practice Study Guide Change Fonts, Font Style, and Font Color The NOW Function Export Excel Spreadsheet to Access Table The VLookup Function The MIN or MINIMUM Function Histogram Charts Use the Sum Button to Sum a Range of Cells Enter Formulas Using the Keyboard Access 2016: Identify Good Database Design Create a Table and Define Fields in a Blank Desktop Database The Primary Key Import Excel Spreadsheet into Access Create a Table in Design View Modify the Structure of a Table Create a Subform MOS Prep - Basic Certification Exam Practice Study Guide Add Existing Fields to a Form PowerPoint 2016: Create a New Presentation Edit a Presentation in Normal View Add Pictures to a Presentation Format Numbered and Bulleted Lists Customize Slide Backgrounds and Themes Animate a Slide Show Apply a Theme Used in Another Presentation Outlook 2016 Basic Tutorial YouTube Analytics: 100 Subscribers 200 Subscribers 300 Subscribers Computer Fundamentals: Computer Case Types - Dell Inspiron AMD 3656 Printer Ports and Types The Boot Up Process How to Get Your Computer Questions Answered Undo Your Mistakes on Windows 10 or Apple Mac Routers vs. Modems What is the Cloud? Storage as a Service Types of Internet Services on Google Android or Apple iPhone Browsing the Web Why Use the Cloud? Microsoft OneDrive - Creating Uploading Downloading and Syncing Explain the Importance of File Management Troubleshoot Common Computer Problems Job Search Skills: Values, Attitude, and Goals Top 5 Job Search Websites Prepare For Your Interview Negotiating Your Salary Video Requests: Download GMetrix Test Preparation Software Remember, the goal of my channel is for you to learn. You can request a video at any time in the comment section, and I will make the video for you. I will make tutorials and simulations and demos for whatever you'd like to learn in our class. So, I encourage you to make a request. I also YouTube Live Stream once a week to answer your questions! Instructor A Morgan
Views: 185886 Professor Adam Morgan
Video Analysis in Hadoop - A Case Study
 
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Our secure remote connectivity tool provides full video recording of all work our engineers perform on client systems. We have requirements to analyze the video log to detect suspicious activity, provide forensic and root cause analysis capabilities. Some of the obvious use cases include detection of credit card patterns or personally identifiable information (PII) as well as malicious activity like dropping database objects. We need to process hundreds of gigabytes per day representing thousands of hours of video. Our solution leverages a variety of Hadoop components to perform optical text recognition and indexing, keyboard and mouse movement analysis as well as integration with variety of other data sources such as our monitoring, documentation, ticketing and communication systems. We will present our complete architecture starting from multi-source data ingestion through data processing and analysis up to the end user interface, reporting and integration layer.
Views: 2212 DataWorks Summit
MAC17 Attendee: Angela Lauryssen, Husky Injection Molding Systems
 
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Angela Lauryssen is the Warranty Manager for Husky and in this video she shares her experience from the conference and workshops. The 2017 Megaputer Analytics Conference (MAC) is held annually and attracts users of Polyanalyst and individuals interested in data & text analytics. The conference highlights successful use cases for multiple industries including specialized tracks in Pharma, Insurance, and Healthcare.
SPSS Questionnaire/Survey Data Entry - Part 1
 
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How to enter and analyze questionnaire (survey) data in SPSS is illustrated in this video. Lots more Questionnaire/Survey & SPSS Videos here: https://www.udemy.com/survey-data/?couponCode=SurveyLikertVideosYT Check out our next text, 'SPSS Cheat Sheet,' here: http://goo.gl/b8sRHa. Prime and ‘Unlimited’ members, get our text for free. (Only 4.99 otherwise, but likely to increase soon.) Survey data Survey data entry Questionnaire data entry Channel Description: https://www.youtube.com/user/statisticsinstructor For step by step help with statistics, with a focus on SPSS. Both descriptive and inferential statistics covered. For descriptive statistics, topics covered include: mean, median, and mode in spss, standard deviation and variance in spss, bar charts in spss, histograms in spss, bivariate scatterplots in spss, stem and leaf plots in spss, frequency distribution tables in spss, creating labels in spss, sorting variables in spss, inserting variables in spss, inserting rows in spss, and modifying default options in spss. For inferential statistics, topics covered include: t tests in spss, anova in spss, correlation in spss, regression in spss, chi square in spss, and MANOVA in spss. New videos regularly posted. Subscribe today! YouTube Channel: https://www.youtube.com/user/statisticsinstructor Video Transcript: In this video we'll take a look at how to enter questionnaire or survey data into SPSS and this is something that a lot of people have questions with so it's important to make sure when you're working with SPSS in particular when you're entering data from a survey that you know how to do. Let's go ahead and take a few moments to look at that. And here you see on the right-hand side of your screen I have a questionnaire, a very short sample questionnaire that I want to enter into SPSS so we're going to create a data file and in this questionnaire here I've made a few modifications. I've underlined some variable names here and I'll talk about that more in a minute and I also put numbers in parentheses to the right of these different names and I'll also explain that as well. Now normally when someone sees this survey we wouldn't have gender underlined for example nor would we have these numbers to the right of male and female. So that's just for us, to help better understand how to enter these data. So let's go ahead and get started here. In SPSS the first thing we need to do is every time we have a possible answer such as male or female we need to create a variable in SPSS that will hold those different answers. So our first variable needs to be gender and that's why that's underlined there just to assist us as we're doing this. So we want to make sure we're in the Variable View tab and then in the first row here under Name we want to type gender and then press ENTER and that creates the variable gender. Now notice here I have two options: male and female. So when people respond or circle or check here that they're male, I need to enter into SPSS some number to indicate that. So we always want to enter numbers whenever possible into SPSS because SPSS for the vast majority of analyses performs statistical analyses on numbers not on words. So I wouldn't want and enter male, female, and so forth. I want to enter one's, two's and so on. So notice here I just arbitrarily decided males get a 1 and females get a 2. It could have been the other way around but since male was the first name listed I went and gave that 1 and then for females I gave a 2. So what we want to do in our data file here is go head and go to Values, this column, click on the None cell, notice these three dots appear they're called an ellipsis, click on that and then our first value notice here 1 is male so Value of 1 and then type Label Male and then click Add. And then our second value of 2 is for females so go ahead and enter 2 for Value and then Female, click Add and then we're done with that you want to see both of them down here and that looks good so click OK. Now those labels are in here and I'll show you how that works when we enter some numbers in a minute. OK next we have ethnicity so I'm going to call this variable ethnicity. So go ahead and type that in press ENTER and then we're going to the same thing we're going to create value labels here so 1 is African-American, 2 is Asian-American, and so on. And I'll just do that very quickly so going to Values column, click on the ellipsis. For 1 we have African American, for 2 Asian American, 3 is Caucasian, and just so you can see that here 3 is Caucasian, 4 is Hispanic, and other is 5, so let's go ahead and finish that. Four is Hispanic, 5 is other, so let's go to do that 5 is other. OK and that's it for that variable. Now we do have it says please state I'll talk about that next that's important when they can enter text we have to handle that differently.
Views: 634054 Quantitative Specialists
Qualitative Data Analysis - Coding & Developing Themes
 
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This is a short practical guide to Qualitative Data Analysis
Views: 138551 James Woodall