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Data Mining (Introduction for Business Students)
 
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This short revision video introduces the concept of data mining. Data mining is the process of analysing data from different perspectives and summarising it into useful information, including discovery of previously unknown interesting patterns, unusual records or dependencies. There are many potential business benefits from effective data mining, including: Identifying previously unseen relationships between business data sets Better predicting future trends & behaviours Extract commercial (e.g. performance insights) from big data sets Generating actionable strategies built on data insights (e.g. positioning and targeting for market segments) Data mining is a particularly powerful series of techniques to support marketing competitiveness. Examples include: Sales forecasting: analysing when customers bought to predict when they will buy again Database marketing: examining customer purchasing patterns and looking at the demographics and psychographics of customers to build predictive profiles Market segmentation: a classic use of data mining, using data to break down a market into meaningful segments like age, income, occupation or gender E-commerce basket analysis: using mined data to predict future customer behavior by past performance, including purchases and preferences
Views: 5764 tutor2u
Data Mining
 
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Technology students give presentation on about Data Mining including the advantages/disadvantages, how to and more.
Views: 238 Wafeek Wahby
NCUA Webinar:  Data Mining - Golden Nuggets for Your Marketing Campaign (10/21/2015)
 
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This webinar discussed benefits of data mining for marketing in credit unions. Topics covered included capturing and using key member and profitability data; calculating a marketing campaign’s return on investment; top trends in credit union marketing today; and maximizing success when working with a marketing consultant.
Views: 254 NCUAchannel
Data Mining
 
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Technology students give presentation on about Data Mining including the advantages/disadvantages, how to and more.
Views: 17141 techEIU
O'Reilly Webcast: An Introduction to Ethics of Big Data
 
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Ethics of Big Data is about finding alignment between an organization's core values and their day-to-day actions in a way that balances risk and innovation. As Big Data brings business operations and practices deeper and more fully into individual lives, it is creating a forcing function that raises ethical questions about our values around concepts like identity, privacy, ownership, and reputation. How we understand those values and align them with our actions when innovating products and services using Big Data technologies benefits from a framework that provides a common vocabulary and encourages explicit discussion. The material will address the intersection of ethics and Big Data; what it is and what it isn't. Specifically, how to approach and generate dialog about an abstract subject with direct, real-world implications. A general framework for talking about ethics in the context of Big Data will be introduced. Aspects include: -Direct relevance to your data handling practices -How Big Data is influencing important concepts including identity, privacy, ownership, and reputation -Ethical Decision Points -Value Personas as a tool for encouraging discussion and generating agreement and alignment between values and actions -Balancing the benefits of Big Data innovation and the risks of harm The webcast will present key concepts from the forthcoming book Ethics of Big Data About Kord Davis: Kord Davis is a former Principal Consultant with Cap Gemini and has spent nearly 20 years providing business strategy, analysis, and technical consulting to over 100 organizations of all sizes including: Autotask, Microsoft, Intel, Sisters of Mercy Healthcare, Nike, Bonneville Power Administration (BPA), Northwest Energy Alliance (NEEA), Bill & Melinda Gates Foundation, Western Digital, Fluke, Merix, Roadway Express, and Gardenburger. Integrating a professional background in telecommunications and an academic background in philosophy, he brings passionate curiosity, the rigor of analysis, and a love of how technology can help us do the things we really want to do better, faster, and easier. A formally trained workgroup facilitator, he holds a BA in Philosophy from Reed College and professional certifications in communication, systems modeling, and enterprise transformation. Produced by: Yasmina Greco
Views: 5477 O'Reilly
Data Mining in Finance - How is Data Mining Affecting Society?
 
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Title of Project/Presentation: Data Mining in Finance - How is Data Mining Affecting Society? Individual Subtopic: Finance Abstract of Presentation/Paper: In today’s society a vast amount of information is being collected daily. The collection of data has been deemed useful and is utilized by many sectors to include finance, health, government, and social media. The finance sector is vast and is implemented in things such as: financial distress prediction, bankruptcy prediction, and fraud detection. This paper will discuss data mining in finance and its association with globalization and ethical ideologies. Description of tools and techniques used to create the presentation: Power Point http://screencast-o-matic.com/
Views: 1412 Gregory Rice
Data Mining for Privacy | Data Dialogs 2014
 
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Data Mining for Privacy Jessica Staddon, Google Data Dialogs Conference 2014 UC Berkeley School of Information http://datadialogs.ischool.berkeley.edu/ The privacy dangers of data mining are serious and much discussed. Data mining also can help us understand privacy attitudes and behaviors. This talk will cover some recent efforts to leverage public data to better support anonymity and understand topic sensitivity. Use cases include anonymous blogging, document sanitization and more user-friendly sharing and advertising. I will also talk about challenges in moving forward with this area of research and open problems.
The European Copyright reform and Text and Data Mining: what should be done?
 
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Caroline De Cock, Coordinator of the Copyright for Creativity Coalition, explains how the very narrow text and data mining (TDM) exception included in the European Commission's copyright proposal would cause the EU to miss on a lot of potential innovation.
Views: 130 CCIA
Naive Bayes Classifier | Naive Bayes Algorithm | Naive Bayes Classifier With Example | Simplilearn
 
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This Naive Bayes Classifier tutorial video will introduce you to the basic concepts of Naive Bayes classifier, what is Naive Bayes and Bayes theorem, conditional probability concepts used in Bayes theorem, where is Naive Bayes classifier used, how Naive Bayes algorithm works with solved examples, advantages of Naive Bayes. By the end of this video, you will also implement Naive Bayes algorithm for text classification in Python. The topics covered in this Naive Bayes video are as follows: 1. What is Naive Bayes? ( 01:06 ) 2. Naive Bayes and Machine Learning ( 05:45 ) 3. Why do we need Naive Bayes? ( 05:46 ) 4. Understanding Naive Bayes Classifier ( 06:30 ) 5. Advantages of Naive Bayes Classifier ( 20:17 ) 6. Demo - Text Classification using Naive Bayes ( 22:36 ) To learn more about Machine Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the Slides here: https://goo.gl/Cw9wqy #NaiveBayes #MachineLearningAlgorithms #DataScienceCourse #DataScience #SimplilearnMachineLearning - - - - - - - - Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer Why learn Machine Learning? Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems The Machine Learning Course is recommended for: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Naive-Bayes-Classifier-l3dZ6ZNFjo0&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 46087 Simplilearn
Big Data at Audi: Root Cause Analysis in an Automotive Paint Shop Using MLlib
 
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"The process of painting a car is highly automated, highly complex and depends on various external variables which are, sometimes, difficult to control. Quality standards regarding paint and finish are extremely high at Audi as these are the most visible features of a car to a customer. Today, it takes years of experience to identify the main drivers of paint failures and keep standards accordingly high. For example, different types of paint require different settings regarding process values and application technique. In order to track the level of quality, every single car is inspected by quality assurance and every failure is documented. For documentation, there are more than 200 predefined types of failures available which are used for standardized documentation. While a car is being painted, data is collected from 2,500 sensors. Those parameters include temperatures, humidity, air flow of application robots, energy consumption, state of filters, etc. All of these variables may influence quality positively or negatively. The challenge of supporting process experts with valuable insights into data is solved by storing sensor data in the data lake and processing the data with Apache Spark and Scala on a HDFS cluster. To identify the most important drivers for paint quality for each failure and each layer of paint, 20 random forest models are being trained daily with MLlib. The results are stored in HDFS and visualized with Tableau. This session will give insights into the challenges of big data at an automotive OEM and how the production of Audi benefits from new big data technologies to make their processes more efficient and raise quality standards even higher. To achieve business benefits, Spark is being used along the whole process chain for data ingestion, transformation and training in a productive and completely automated environment. Session hashtag: #SFexp13"
Views: 3005 Databricks
Data Mining using R | R Tutorial for Beginners | Data Mining Tutorial for Beginners 2018 | ExcleR
 
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Data Mining Using R (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. Data Mining Certification Training Course Content : https://www.excelr.com/data-mining/ Introduction to Data Mining Tutorials : https://youtu.be/uNrg8ep_sEI What is Data Mining? Big data!!! Are you demotivated when your peers are discussing about data science and recent advances in big data. Did you ever think how Flip kart and Amazon are suggesting products for their customers? Do you know how financial institutions/retailers are using big data to transform themselves in to next generation enterprises? Do you want to be part of the world class next generation organisations to change the game rules of the strategy making and to zoom your career to newer heights? Here is the power of data science in the form of Data mining concepts which are considered most powerful techniques in big data analytics. Data Mining with R unveils underlying amazing patterns, wonderful insights which go unnoticed otherwise, from the large amounts of data. Data mining tools predict behaviours and future trends, allowing businesses to make proactive, unbiased and scientific-driven decisions. Data mining has powerful tools and techniques that answer business questions in a scientific manner, which traditional methods cannot answer. Adoption of data mining concepts in decision making changed the companies, the way they operate the business and improved revenues significantly. Companies in a wide range of industries such as Information Technology, Retail, Telecommunication, Oil and Gas, Finance, Health care are already using data mining tools and techniques to take advantage of historical data and to create their future business strategies. Data mining can be broadly categorized into two branches i.e. supervised learning and unsupervised learning. Unsupervised learning deals with identifying significant facts, relationships, hidden patterns, trends and anomalies. Clustering, Principle Component Analysis, Association Rules, etc., are considered unsupervised learning. Supervised learning deals with prediction and classification of the data with machine learning algorithms. Weka is most popular tool for supervised learning. Topics You Will Learn… Unsupervised learning: Introduction to datamining Dimension reduction techniques Principal Component Analysis (PCA) Singular Value Decomposition (SVD) Association rules / Market Basket Analysis / Affinity Filtering Recommender Systems / Recommendation Engine / Collaborative Filtering Network Analytics – Degree centrality, Closeness Centrality, Betweenness Centrality, etc. Cluster Analysis Hierarchical clustering K-means clustering Supervised learning: Overview of machine learning / supervised learning Data exploration methods Basic classification algorithms Decision trees classifier Random Forest K-Nearest Neighbours Bayesian classifiers: Naïve Bayes and other discriminant classifiers Perceptron and Logistic regression Neural networks Advanced classification algorithms Bayesian Networks Support Vector machines Model validation and interpretation Multi class classification problem Bagging (Random Forest) and Boosting (Gradient Boosted Decision Trees) Regression analysis Tools You Will Learn… R: R is a programming language to carry out complex statistical computations and data visualization. R is also open source software and backed by large community all over the world who are contributing to enhancing the capability. R has many advantages over other tools available in the market and it has been rated No.1 among the data scientist community. Mode of Trainings : E-Learning Online Training ClassRoom Training --------------------------------------------------------------------------- For More Info Contact :: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com
Introduction to Data Analytics with R, Tableau & Excel | Data Analytics Career in 2019 & Beyond
 
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Introduction to Data Analytics with R, Tableau & Excel | Data Analytics Career in 2019 & Beyond https://acadgild.com/big-data/data-analytics-training-certification?aff_id=6003&source=youtube&account=UgnojgSKQLk&campaign=youtube_channel&utm_source=youtube&utm_medium=intro-DA-R-tableau-excel&utm_campaign=youtube_channel Did you know? by 2020, every human being will create over 1.5 megabytes of data per second on average. In 2025, the sum of digital data will add up to 180 zettabytes, which is over 1600 trillion gigabytes. Considering these numbers, it is an understatement to say that the data is only BIG. So, what is Big Data and how is it related to Data Analytics? Big data is a large volume of data that consists of both structured and unstructured data forms. helps organizations to draw meaningful insights from their data to learn and grow. Thus, it’s the data that matters and not it’s volume. Structured data is organized information that can be accessed with the help of simple search algorithms. While Unstructured data as the name suggests is less uniform and thus difficult to work with. The lack of structure makes compiling data at a time and energy-consuming task. The Relation Between Big Data and Analytics: The process of uncovering hidden patterns, unknown correlations, market trends, customer preferences and other useful information from both structured and unstructured data is called Data analytics. The Benefits of Using Data Analytics. • Analytics help organizations make informed decisions and choices. • It boosts the overall performance of the organization by refining the financial processes, increasing visibility, providing insights and granting control over managerial processes. • It detects fraud and flaws by keeping a close vigil. • It further Improves the IT economy by increasing agility and flexibility of systems. The above mentioned are just a few advantages, however, the list goes on. Despite the growing interest in data analytics, there is an acute shortage of professionals with good data analytical skills. Thus, only 0.5% of the data we produce is analysed. There is a serious shortage of skilled professionals. Thus, the ones who are called proficient data analysts must have certain skills. They must possess a varied skill-set like computer science, data mining and business management to provide from the data they are working on. Their computer science skills should include both programming skills and technical skills • Programming Skills: Python, R, and Java • Technical Skills: Knowledge of platforms like Hadoop, Hive, Spark, etc., Their data skills should include Warehousing Skills, Quantitative & Statistical Skills & Analytical & Interpretation Skills • Warehousing Skills: Data scientist must possess good analytical skills • Quantitative & Statistical Skills: As technology is a key aspect of big data analysis, quantitative and statistical skills are essential • Analytical & Interpretation Skills: knacks to analyses and interpret data The business skills are important to use the data effectively and to improve various aspects such as operations, finance, productivity, etc., These are the skills that make the data analytics professional an invaluable asset to the organization. The lack of skilled data professionals is an opportunity in turn for upcoming data scientists to make their mark in the field of data analytics. As the significance of data grows in the business world, the value of professionals working in analytics also increases. This is creating a variety of job roles amongst organizations and they are. Data Analyst, Analytics Consultant, Business Analyst, Analytics Manager, Data Architect, Metrics and Analytics Specialist, Analytics Associate these are only some of the job titles that data analytics professionals can acquire in business organizations. The list is presumably greater. The Chief Software Platforms are R, Tableau & Excel R is one of the robust statistical computing solutions. Tableau is the foremost business intelligence platform that offers eminent data visualization and exploration capabilities. Coming to Excel, it is used for managing, manipulating and presenting data. When combined, Tableau, R and Excel offer the most powerful and complete data analytics solutions. So, the demand for data analytics and its professionals is augmenting at a great pace. Organizations are interested in analysts to maximize their data potential, while professionals are interested in capitalizing on the analytical crunch in many parts of the world. #DataAnalytics, #Tableau, #R, #Excel, #career Please like share and subscribe the channel for more such video. For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 3971 ACADGILD
Kamanja: An Open Source Real Time System for Scoring Data Mining Models, Greg Makowski 20150727
 
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Greg Makowski, Director of Data Science, LigaDATA This talk will start with a number of complex data real-time use cases, such as a) complex event processing, b) supporting the modeling of a data mining department and c) developing enterprise applications on Apache big-data systems. While Hadoop and big data has been around for a while, banks and healthcare companies tend not to be early IT adopters. What are some of the security or roadblocks in Apache big data systems for such industries with high requirements? Data mining models can be trained in dozens of packages, but what can simplify the deployment of models regardless of where they were trained or with what algorithm? Predictive Modeling Markup Language (PMML), is a type of XML with specific support for 15 families of data mining algorithms. Data mining software such as R, KNIME, Knowledge Studio, SAS Enterprise Miner are PMML producers. The new open-source product, Kamanja, is the first open-source, real-time PMML consumer (scoring system). One advantage of PMML systems is that it can reduce time to deploy production models from 1-2 months to 1-2 days - a pain point that may be less obvious if your data mining exposure is competitions or MOOCs. Kamanja is free on Github, supports Kafka, MQ, Spark, HBase and Cassandra among other things. Being a new open-source product, initially, Kamanja supports rules, trees and regression. I will cover an architecture of a sample application using multiple real-time open source data, such as social network campaigns and tracking sentiment for the bank client and its competitors. Other real-time architectures cover credit card fraud detection. A brief demo will be given of the social network analysis application, with text mining. An overview of products in the space will include popular Apache big data systems, real-time systems and PMML systems. For more details: Slides: http://www.slideshare.net/gregmakowski/kamanja-driving-business-value-through-realtime-decisioning-solutions http://kamanja.org/ http://www.meetup.com/SF-Bay-ACM/events/223615901/ http://www.sfbayacm.org/event/kamanja-new-open-source-real-time-system-scoring-data-mining-models Venue sponsored by eBay, Food and live streaming sponsored by LigaDATA, San Jose, CA, July 27, 2015 Chapter Chair Bill Bruns Data Science SIG Program Chair Greg Makowski Vice Chair Ashish Antal Volunteer Coordinator Liana Ye Volunteers Joan Hoenow, Stephen McInerney, Derek Hao, Vinay Muttineni Camera Tom Moran Production Alex Sokolsky Copyright © 2015 ACM San Francisco Bay Area Professional Chapter
Decision Tree Algorithm With Example | Decision Tree In Machine Learning | Data Science |Simplilearn
 
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This Decision Tree algorithm in Machine Learning tutorial video will help you understand all the basics of Decision Tree along with what is Machine Learning, problems in Machine Learning, what is Decision Tree, advantages and disadvantages of Decision Tree, how Decision Tree algorithm works with solved examples and at the end we will implement a Decision Tree use case/ demo in Python on loan payment prediction. This Decision Tree tutorial is ideal for both beginners as well as professionals who want to learn Machine Learning Algorithms. Below topics are covered in this Decision Tree Algorithm Tutorial: 1. What is Machine Learning? ( 02:25 ) 2. Types of Machine Learning? ( 03:27 ) 3. Problems in Machine Learning ( 04:43 ) 4. What is Decision Tree? ( 06:29 ) 5. What are the problems a Decision Tree Solves? ( 07:11 ) 6. Advantages of Decision Tree ( 07:54 ) 7. How does Decision Tree Work? ( 10:55 ) 8. Use Case - Loan Repayment Prediction ( 14:32 ) What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Decision-Tree-Algorithm-With-Example-RmajweUFKvM&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Decision-Tree-Algorithm-With-Example-RmajweUFKvM&utm_medium=Tutorials&utm_source=youtube #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - - Who should take this Machine Learning Training Course? We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning - - - - - - For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 52173 Simplilearn
Demo: IBM Big Data and Analytics at work in Banking
 
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Visit http://ibmbigdatahub.com for more industry demos. Banks face many challenges as they strive to return to pre-2008 profit margins including reduced interest rates, unstable financial markets, tighter regulations and lower performing assets. Fortunately, banks taking advantage of big data and analytics can generate new revenue streams. Watch this real-life example of how big data and analytics can improve the overall customer experience. To learn more about IBM Big Data, visit http://www.ibm.com/big-data/us/en/ To learn more about IBM Analytics, visit http://www.ibm.com/analytics/us/en/
Views: 99490 IBM Analytics
The Logic of Data Mining in Social Research
 
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This video is a brief introduction for undergraduates to the logic (not the nitty-gritty details) of data mining in social science research. Four orienting tips for getting started and placing data mining in the broader context of social research are included.
Views: 402 James Cook
Data Mining and Business Intelligence for Cyber Security Applications Summer Program at BGU
 
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The purpose of the Summer Program in Data Mining and Business Intelligence is to provide both theoretical and practical knowledge, including tools, on data mining. The program offers two academic courses (each for 3 credits), where students learn the basic tools of data mining and the utilization of machine learning techniques for solving cyber security problems. The program includes a mandatory one week internship at BGU’s Cyber Security Research Center. The internship corresponds with the course materials and contributes the practical experience component. In addition, students will take part in professional fieldtrips to leading companies, in order to enhance their understanding of data mining and cyber security To Apply: https://www.tfaforms.com/399172 For More information: www.bgu.ac.il/global
Views: 1392 BenGurionUniversity
What is machine learning and how to learn it ?
 
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http://www.LearnCodeOnline.in Machine learning is just to give trained data to a program and get better result for complex problems. It is very close to data mining. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with: The heavily hyped, self-driving Google car? The essence of machine learning. Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life. Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation. Fraud detection? One of the more obvious, important uses in our world today. fb: https://www.facebook.com/HiteshChoudharyPage homepage: http://www.hiteshChoudhary.com
Views: 834218 Hitesh Choudhary
Future Medicine: Modern Informatics | Richard Frackowiack | TEDxYouth@Zurich
 
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How can data mining in hospitals help researchers find cures for the most harmful diseases? In [email protected] 2016, Richard talks about his role in the Human Brain Project. He explains how neurologists and psychiatrists will use the results of data mining the masses of data in Europe’s hospital and research databases to develop new diagnostic schemas thus facilitating an era of precision medicine. Richard, a former brain doctor, studied medicine at the University of Cambridge, where he first became interested in neuroscience. His work includes the development of new techniques for MRI, as well as a study drawing a connection between the enlarged hippocampus and heightened navigational skills of London taxi drivers. He now works for the Human Brain Project, where he is in charge of its medical informatics platform. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 16162 TEDx Talks
Data Mining Tool essentials
 
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Basics of the Data Mining Tool. Includes experiment info, quality control, text search and standard search.
Views: 83 QMRIBioinf
Data Collection and Preprocessing | Lecture 6
 
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Deep Learning Crash Course playlist: https://www.youtube.com/playlist?list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07 Highlights: Garbage-in, Garbage-out Dataset Bias Data Collection Web Mining Subjective Studies Data Imputation Feature Scaling Data Imbalance #deeplearning #machinelearning
Views: 2019 Leo Isikdogan
Data Mining using R | R Tutorial for Beginners | Data Mining Tutorial for Beginners 2018 | ExcelR
 
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ExcelR Data Mining Tutorial for Beginners 2018 - Introduction to Data mining using R language. Data Mining Certification Training Course Content : https://www.excelr.com/data-mining/ Introduction to Data Mining Tutorials : https://youtu.be/uNrg8ep_sEI What is Data Mining? Big data!!! Are you demotivated when your peers are discussing about data science and recent advances in big data. Did you ever think how Flip kart and Amazon are suggesting products for their customers? Do you know how financial institutions/retailers are using big data to transform themselves in to next generation enterprises? Do you want to be part of the world class next generation organisations to change the game rules of the strategy making and to zoom your career to newer heights? Here is the power of data science in the form of Data mining concepts which are considered most powerful techniques in big data analytics. Data Mining with R unveils underlying amazing patterns, wonderful insights which go unnoticed otherwise, from the large amounts of data. Data mining tools predict behaviours and future trends, allowing businesses to make proactive, unbiased and scientific-driven decisions. Data mining has powerful tools and techniques that answer business questions in a scientific manner, which traditional methods cannot answer. Adoption of data mining concepts in decision making changed the companies, the way they operate the business and improved revenues significantly. Companies in a wide range of industries such as Information Technology, Retail, Telecommunication, Oil and Gas, Finance, Health care are already using data mining tools and techniques to take advantage of historical data and to create their future business strategies. Data mining can be broadly categorized into two branches i.e. supervised learning and unsupervised learning. Unsupervised learning deals with identifying significant facts, relationships, hidden patterns, trends and anomalies. Clustering, Principle Component Analysis, Association Rules, etc., are considered unsupervised learning. Supervised learning deals with prediction and classification of the data with machine learning algorithms. Weka is most popular tool for supervised learning. Topics You Will Learn… Unsupervised learning: Introduction to datamining Dimension reduction techniques Principal Component Analysis (PCA) Singular Value Decomposition (SVD) Association rules / Market Basket Analysis / Affinity Filtering Recommender Systems / Recommendation Engine / Collaborative Filtering Network Analytics – Degree centrality, Closeness Centrality, Betweenness Centrality, etc. Cluster Analysis Hierarchical clustering K-means clustering Supervised learning: Overview of machine learning / supervised learning Data exploration methods Basic classification algorithms Decision trees classifier Random Forest K-Nearest Neighbours Bayesian classifiers: Naïve Bayes and other discriminant classifiers Perceptron and Logistic regression Neural networks Advanced classification algorithms Bayesian Networks Support Vector machines Model validation and interpretation Multi class classification problem Bagging (Random Forest) and Boosting (Gradient Boosted Decision Trees) Regression analysis Tools You Will Learn… R: R is a programming language to carry out complex statistical computations and data visualization. R is also open source software and backed by large community all over the world who are contributing to enhancing the capability. R has many advantages over other tools available in the market and it has been rated No.1 among the data scientist community. Mode of Trainings : E-Learning Online Training ClassRoom Training --------------------------------------------------------------------------- For More Info Contact :: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com
BigData and Old Data: Embedding Predictive Analytics in Real Applications
 
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Usama Fayyad, Chief Data Officer at Barclays Bank presents at RapidMiner World 2014 on the challenges of making the benefits of advanced analytics fit with the business or target area of application. Topics discussed include embedding data mining insights and models into production processes and live deployments, real-time data streaming and in situ data mining, BigData, unstructured data, and Hadoop. Access Usama's slides here: http://www.slideshare.net/RapidMiner/big-data-vs-classic-data-usama-fayyad
Views: 1440 RapidMiner, Inc.
Advanced analytics in Microsoft Excel: Get deep insights from your data including - BRK3082
 
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Join this session to learn how to explore and analyze your data with the power of new AI capabilities within Microsoft Excel. We’ll review the new "Excel Insights" feature powered by ML based techniques, brand new Excel visualizations, and cloud-connected geography and financial data types. This session will also include a review of best practices using PivotTables, Power Queries, Conditional Formatting and more.
Views: 920 Microsoft Ignite
Building Advanced Analytics Applications with R and Python Integration
 
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At Tableau we help people see and understand data. Seven words that drive everything we do. And they’ve never been more relevant. Tableau is all about making your analytics faster, smarter, and more powerful, so that everyone can get the answers they need. Helping people gain insight into their data to solve unexpected problems is what drives us. Tableau is a visual analytics and reporting solution that connects directly to R, Python, and more. It’s designed for you, the domain expert who understands the data. Its drag-and-drop interface allows you effortlessly connect to libraries and packages, import saved models, or write new ones directly into calculations, visualizing them in seconds. In this webinar, we will explore how various analytics partners are leveraged in Tableau, and how to take advantage of these integrations to move your analysis to the next level. Whether you work with R, Python, or other statistical or data mining environments, Tableau allows you to take advantage of your existing investments and knowledge to compose impactful data stories. Read more at http://forums.bsdinsight.com/forums/tableau.95/
Views: 7603 Tableau
The Artificial Intelligence Application in the Management of Contemporary Organization
 
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Nowadays the artificial intelligence solutions together with data science and business analytics solutions such as Business Intelligence systems, Big data and data mining play crucial role in the management of many contemporary business organizations. The multitude of its benefits include improvement of the whole management process of business organization and especially the process of decision making, allowing for automation of tasks in many areas. The aim of the paper is to present the role of artificial intelligence solutions in the process of contemporary organization’s management, its theoretical assumptions, development and current practices. The paper also presents authors’ research carried out among the group of 12 respondents. The aim of the study was to find how the benefits and drawbacks of artificial intelligence solutions are perceived by respondents. The foreign research review includes analysis of practices in such areas and branches as production management, logistics, retail trade and financial sector.
Views: 33 SAIConference
An integrated data mining approach to real-time clinical monitoring and deterioration.. (KDD 2012)
 
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An integrated data mining approach to real-time clinical monitoring and deterioration warning KDD 2012 Yi Mao Wenlin Chen Yixin Chen Chenyang Lu Marin Kollef Thomas Bailey Clinical study found that early detection and intervention are essential for preventing clinical deterioration in patients, for patients both in intensive care units (ICU) as well as in general wards but under real-time data sensing (RDS). In this paper, we develop an integrated data mining approach to give early deterioration warnings for patients under real-time monitoring in ICU and RDS. Existing work on mining real-time clinical data often focus on certain single vital sign and specific disease. In this paper, we consider an integrated data mining approach for general sudden deterioration warning. We synthesize a large feature set that includes first and second order time-series features, detrended fluctuation analysis (DFA), spectral analysis, approximative entropy, and cross-signal features. We then systematically apply and evaluate a series of established data mining methods, including forward feature selection, linear and nonlinear classification algorithms, and exploratory undersampling for class imbalance. An extensive empirical study is conducted on real patient data collected between 2001 and 2008 from a variety of ICUs. Results show the benefit of each of the proposed techniques, and the final integrated approach significantly improves the prediction quality. The proposed clinical warning system is currently under integration with the electronic medical record system at Barnes-Jewish Hospital in preparation for a clinical trial. This work represents a promising step toward general early clinical warning which has the potential to significantly improve the quality of patient care in hospitals.
Data Model Examples to Help You Predict the Future
 
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In this video, we're going to look at different ways you can track status in your applications. Ultimately what I'd like to show you, is why updating your database is a bad idea in general. Transcript and code: http://www.deegeu.com/data-model-examples/ In this video, we're going to look at different ways you can track status in your applications. Ultimately what I'd like to show you, is why updating your database is a bad idea in general. There are exceptions, but for most cases you want to just append data. Finally we'll look at some of the benefits of storing a state history in your data model, including predicting the future with data mining! Concepts: Programming, data modeling, data structures, data patterns Social Links: Don't hesitate to contact me if you have any further questions. WEBSITE : [email protected] TWITTER : http://www.twitter.com/deege FACEBOOK: https://www.facebook.com/deegeu.programming.tutorials GOOGLE+ : http://google.com/+Deegeu-programming-tutorials About Me: http://www.deegeu.com/about-programming-tutorial-videos/ Related Videos: https://www.youtube.com/playlist?list=PLZlGOBonMjFVXbUCdvYLEZFAkimS27Aor Media credits: All images are owned by DJ Spiess unless listed below Balloons - Creative Commons CC0 License https://download.unsplash.com/photo-1433838552652-f9a46b332c40
Views: 370 Deege U
Prof. Michael Cafarella
 
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Prof. Michael Cafarella speaks about his work in data mining and its applications in mining social content and unlocking legacy data. Prof. Cafarella's research interests include databases, information extraction, data integration, and data mining. He is particularly interested in applying data mining techniques to Web data and scientific applications. His website is at: http://web.eecs.umich.edu/~michjc
Instant Visualization in Every Step of Analysis - O'Reilly Webcast
 
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A webcast led by Karen Hsu of Datameer. Surveys reveal that concerns about data quality can create barriers for companies deploying Analytics and BI initiatives. How can you readily identify and correct data quality issues at every step of your big data analysis to ensure accurate insights into customer behavior? In this webcast, we'll discuss how IT and business users can leverage self-service visualizations to quickly spot and correct data anomalies throughout the analytic process. You will learn how to: - Continuously visualize a profile of your data to identify inconsistencies, incompleteness and duplicates in your data - Visualize machine learning and data mining, including clustering, decision tree analysis, column correlations and recommendations - Create self-service visualizations for business and IT users About Karen Hsu: Karen is Senior Director, Product Marketing at Datameer. With over 15 years of experience in enterprise software, Karen Hsu has co-authored 4 patents and worked in a variety of engineering, marketing and sales roles. Most recently she came from Informatica where she worked with the start-ups Informatica purchased to bring big data, data quality, master data management, B2B and data security solutions to market. Karen has a Bachelors of Science degree in Management Science and Engineering from Stanford University. @Karenhsumar About host Ben Lorica: Ben Lorica is the Chief Data Scientist at O'Reilly Media, Inc. He has applied Business Intelligence, Data Mining, Machine Learning and Statistical Analysis in a variety of settings including Direct Marketing, Consumer and Market Research, Targeted Advertising, Text Mining, and Financial Engineering. His background includes stints with an investment management company, internet startups, and financial services. Don't miss an upload! Subscribe! http://goo.gl/szEauh 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: 887 O'Reilly
Civilian spy gangs, data mining, black ops economy 1of3
 
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The new Stasi is here. As both economy and morals go down, a lot of people are willing to do anything to keep their status and make an extra buck. So, we're rapidly turning into a public/private informant society, where black ops and psyops on 'dangerous' individuals are the norm. Corporations, NGOs and government agencies are going full steam with a unified, militarized program to turn North America and Europe into a full-fledged Stasi society. If you're mandated into one of these programs (I'm not talking to the weak minded people who actually volunteer or try to get contracted into these things, just to the person that finds him/herself coerced), never forget this saying by the man above: «For what shall it profit a man, if he shall gain the whole world, and lose his own soul?» FAIR USE NOTICE: This video contains copyrighted material the use of which has not always been specifically authorized by the copyright owner. We are making such material available in our efforts to advance understanding of issues of ecological and humanitarian significance. We believe this constitutes a 'fair use' of any such copyrighted material as provided for in section 107 of the US Copyright Law. In accordance with Title 17 U.S.C. Section 107, the material in this video is distributed without profit to those who have expressed a prior interest in receiving the included information for research and educational purposes. For more information go to: http://www.law.cornell.edu/uscode/17/107.shtml. lf you wish to use copyrighted material from this site for purposes of your own that go beyond 'fair use', you must obtain permission from the copyright owner.
Views: 819 LibertyTruthJustice
SQL Server Enterprise Data Mining
 
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Microsoft MVP Mark Tabladillo discusses SQL Server Data Mining (SSDM) for SQL Server Professionals. http://marktab.net Mark spoke at SQL Saturday Silicon Valley in March 2012, organized by Mark Ginnebaugh of DesignMind. http://www.designmind.com/ Starting with SQL Server Management Studio (SSMS), the demo includes the interfaces important for professional development, including Business Intelligence Development Studio (BIDS), highlighting Integration Services, and PowerShell.
Views: 317 DesignMind
Personal Information Collection: A Trade-Off Analysis
 
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A PowerPoint presentation examining the advantages and disadvantages of personal information collection. Featured issues include genetic testing and data mining.
International Journal of Data Mining & Knowledge Management Process (IJDKP)
 
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International Journal of Data Mining & Knowledge Management Process (IJDKP) ISSN : 2230 - 9608 [Online] ; 2231 - 007X [Print] http://airccse.org/journal/ijdkp/ijdkp.html Call for papers :- Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the Journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Topics of interest include, but are not limited to, the following: Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining. Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources. Paper Submission Authors are invited to submit papers for this journal through E-mail: [email protected] or [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit : http://airccse.org/journal/ijdkp/ijdkp.html
Views: 94 Sivakumar Arumugam
Panel - Data Mining: Exploring the Ethical Dilemmas
 
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Panel members include: Dr. Tobias Blanke Prof. Claudia Aradau Dr. Ben Waterson Dr. Kieron O'Hara Moderated by Dr. Wendy White This event focused on researchers who employ data mining techniques in their work. In this thematic context we aim to better understand the cross-disciplinary practice of data mining and its associated implications, such as privacy issues, ethics and the interplay with open data. PhD students as well as early career and experienced researchers from around the UK came together to explore how they manage data that they have created when undertaking mining projects, and a panel session helped to identify key questions that researchers face when encountering these implications. For more information visit: www.ses.ac.uk/2018/07/17/data-mining
July 09 2015 Data Mining Webinar: Live Q&A Session
 
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A live recording of the Q&A segment of The Modeling Agency's webinar: "Data Mining: Failure to Launch". Sign up for a full future production at http://www.the-modeling-agency.com/data-mining-webinar. Learn how data mining teams trump "data scientists," and more. In this session we will answer the following questions: 1. What is the biggest suggestion you would give to a person just starting out and looking to have a successful career in the analytics career? 2. What do you think about the role of statistics in the high failure rate of using big data? 3. How do you change the culture to include predictive modeling?
Views: 33 TMA_Analytics
What the Heck Does “Data Science” Really Mean? The Dr. Data Show
 
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In this episode of The Dr. Data Show, Eric Siegel answers the question, "What the heck do 'data science' and 'big data' really mean?" Sign up for future episodes and more info: http://www.TheDoctorDataShow.com Attend Predictive Analytics World: http://www.pawcon.com Read Dr. Data's book: http://www.thepredictionbook.com Welcome to "The Dr. Data Show"! I'm Eric Siegel. “Data science.” “Big data.” What the hell do these buzzwords really, specifically mean? Are they just cockamamie -- intentionally vague jargon that overhypes and overpromises? Or are these terms actually helpful -- do they somehow designate, like, the most profound impact of the Information Age? Well, I’ll start with the vague and overhyping side and then circle back to why these buzzwords may matter after all. It’s time for the Dr. Data buzzword smackdown. There are a lotta problems with these words. First, "data scientist" is redundant. It's like calling a librarian a "book librarian." If you're doing science, it involves data. Duh! Furthermore, don't tell anyone I said this, but real sciences like physics and chemistry don't have "science" in their name. Your science is trying too hard if it has to call itself a science: Social science, political science, data science, and I gotta say -- even though I have three degrees in it and was a professor of it -- computer science is an arbitrarily defined field. It's just the amalgam of everything to do with computers -- as a concept and as an appliance -- from the engineering of how to build them and the deep mathematics about their theoretical limitations to how to make them more user friendly, and even business strategies for managing a team of programmers... Universities might as well also have a "toaster science" department, which covers the engineering of better toasters as well as the culinary arts on how to best cook with them. But I digress. Ok, next buzzword: “Big data.” First of all, it's just grammatically incorrect. It’s like looking at the Pacific Ocean and saying “big water.” It should be “a lotta data” or “plenty of data.” But the real problem with "big data" is that it emphasizes the size. 'Cause what’s exciting about data isn't how much of it there is per se -- it's about how quickly it's growing -- which is amazing by the way. There’s always so much more data today than there was yesterday. So we're gonna run out of adjectives really quickly: “big data,” “bigger data,” “even bigger data,” “the biggest data.” Actually, there’s been a long-running conference called the International Conference on Very Large Databases since 1975. I’m not joking. That's before the first Star Wars movie came out! Now, in some cases, people use the terms data science and big data just to refer to machine learning, i.e., when computers learn from the experience encoded in data. That's the topic of most episodes of this program, The Dr. Data Show. It’s a show about machine learning -- which is a well-defined field and by the way is also often called predictive analytics, especially when you're talking about its deployment in the private or public sector. I would urge folks to use the well-defined terms machine learning or predictive analytics if in fact that's what you’re specifically talking about. But as for data science and big data, in their general usage they suffer from a terrible case of vagueness. The have a wide range of subjective definitions, which compete and conflict. Basically, they're often used to mean nothing more specific than "some clever use of data." The terms don't necessarily refer to any particular technology, method, or value proposition. They're just plain subjective -- you can use them to mean whichever technology you'd like: machine learning, data visualization, or even just basic reporting. But much worse than that, this vagueness often serves to mislead and misrepresent by alluding to capabilities that don't exist. For example, the popular press -- as well certain analytics vendors -- sometimes use "data science" to denote some whole collection of methods that includes machine learning as well as some other advanced methods. The problem is, those other advanced methods are implied but often actually just don't really exist. They're vaporware. This confusion is sometimes inadvertent -- such as when journalists aren’t fully knowledgeable of the topic yet want it to sound as powerful as possible -- but, either way, the end result is souped-up hype that overpromises and circulates misinformation. All these issues, by the way, also apply to the older-school term "data mining," also totally subjective. Besides, calling it "data mining" is like instead of "gold mining," saying “dirt mining.” Malfunction, failed analogy... 'Cause we aren't searching for data, we're searching within data... For the complete transcript and more: http://www.TheDoctorDataShow.com
Views: 914 Eric Siegel
OCPUS18–Innovative Immersion Cooling Approach for Shrinking OCP Data Center Size, Complexity & Costs
 
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Speakers: Gary Testa (Engineered Fluids, Inc., President & CEO) & Darwin Kauffman (LiquidCool Solutions, CEO) Liquid cooling systems for data center servers are attracting increased interest, due to the improved energy efficiency and significant capital savings they offer. This presentation will introduce participants to an innovative liquid immersion cooling solution for data centers developed by LiquidCool Solutions in partnership with Engineered Fluids. For many people, the term “liquid immersion cooling” conjures up images of large tanks with servers bathed in mineral oil or boiling two-phase fluid. LiquidCool Solutions has taken an entirely different approach that achieves all the benefits of other liquid immersion technologies without many of the operational challenges these approaches impose. While all liquid cooling technologies deliver improved cooling energy efficiency compared to air-cooling, LiquidCool systems provide additional benefits that other liquid cooling technologies can’t match. These enhanced benefits include: -Compatibility with standard server racks -Reduced weight compared to other liquid immersion cooling approaches -Modular sealed rack systems that enable servers to be added or removed without exposing cooling fluid -The ability to recover nearly 100% of server waste heat for effective reuse -The ability to cool without chillers or evaporative cooling in almost any climate on earth -The key to achieving these benefits is LiquidCool Solutions’ patented Directed-Flow total immersion cooling technology combined with the use of Engineered Fluids’ ElectroCool Biodegradable Dielectric Coolant. Participants will learn how directed-flow immersion cooling works and how it enables LiquidCool systems to deliver its unique combination of excellent cooling performance, high energy efficiency, and adaptability. Finally, the presentation will include an overview of current LiquidCool pilot data center installations and provide a vision of how data centers can leverage LiquidCool Solutions and Engineered Fluids’ technology to shrink data center size, simplify cooling infrastructure, and dramatically reduce capital expense and operating costs.
Private in the digital age: Who benefits from the data economy?
 
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Keynote from Prof. Alessandro Acquisti Alessandro Acquisti is a Professor of Information Technology and Public Policy at the Heinz College, Carnegie Mellon University (CMU) and an Andrew Carnegie Fellow (inaugural class). He is the director of the Peex (Privacy Economics Experiments) lab at CMU and the co-director of CMU CBDR (Center for Behavioral and Decision Research). His research interests lie at the overlap of information technology, society, and economics. They include, primarily, the economics and behavioral economics of privacy and information security, and privacy in online social networks. Prof. Acquisti interested in the economic impact of privacy protection and privacy intrusions, the relations between privacy and economic rationality, and the dichotomy between expressed privacy attitudes and actual revealed behavior. Abstract of Professor Acquisti’s keynote speech: In the recent policy debate over privacy, the protection of personal information is often set against the benefits society is expected to gain from large scale analytics. We know from big data technology. Or in other words: Is there a trade-off between the protection of individual rights and the welfare of a society? In this talk, I will use results from both empirical and theoretical economic research, as well as from behavioral economics, to scrutinize this notion. In particular, I will highlight how current research findings paint a much more nuanced, and interesting, picture regarding the economic impact of data sharing and data protection on both individual and societal wellbeing. Panel Discussion with Prof. Alessandro Acquisti (Professor for Information Technology and Public Policy, Carnegie Mellon University), Prof. Dr. Franziska Böhm (Professor for Law, Leibniz-Institute for Information Infrastructure in Karlsruhe (FIZ) and the Karlsruhe Institute for Technologies (KIT), Prof. Dr. Sabine Trepte (Professor for Communication Science and Media Psychology, University of Hohenheim) and Stefan Butz (Vice President at BMW Group). Moderation: Prof. Dr. Thomas Hess (Institute for Information Systems and New Media, Ludwig-Maximilians-Universität München)
Views: 321 BIDT München
Using R and Apache Hadoop for Data Mining and Statistical Predictive Analytics
 
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This on-demand webinar, we'll: - Walk you through how Hadoop is being used today - Discuss real-world customer use cases for data mining and statistical predictive analytics in Hadoop - Show a live churn analytics demonstration with Revolution Analytics and Hortonworks Data Platform
Views: 10172 Hortonworks
What is CCC Computer Course|All About CCC Course in Hindi|How to Apply
 
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Learn What is Doeacc NIELIT CCC Course, its Duration, Syllabus,How to Apply Online,step by step registration Process in Hindi.All about how to do ccc computer course and what is ccc computer course, how to apply online for registration of ccc. Visit Our Website: http://www.cpitudaipur.com Visit Our Blog: http://cpitudaipur.blogspot.in/ Visit Our Facebook Page: http://facebook.com/cpitudr Please Subscribe to Our Channel https://www.youtube.com/channel/UCSMsxXvvi-7XvygtsMWRBOg
[LIVE] Kamanja: A New Open Source Real-Time System for Scoring Data Mining Models, Greg Makowski,
 
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[Streamed version. Front & back trimmed. Slide issue in beginning.] An edited version is available: https://www.youtube.com/watch?v=ANqB72b0r38 Slides: http://www.slideshare.net/gregmakowski/kamanja-driving-business-value-through-realtime-decisioning-solutions Greg Makowski, Director of Data Science, LigaDATA This talk will start with a number of complex data real-time use cases, such as a) complex event processing, b) supporting the modeling of a data mining department and c) developing enterprise applications on Apache big-data systems. While Hadoop and big data has been around for a while, banks and healthcare companies tend not to be early IT adopters. What are some of the security or roadblocks in Apache big data systems for such industries with high requirements? Data mining models can be trained in dozens of packages, but what can simplify the deployment of models regardless of where they were trained or with what algorithm? Predictive Modeling Markup Language (PMML), is a type of XML with specific support for 15 families of data mining algorithms. Data mining software such as R, KNIME, Knowledge Studio, SAS Enterprise Miner are PMML producers. The new open-source product, Kamanja, is the first open-source, real-time PMML consumer (scoring system). One advantage of PMML systems is that it can reduce time to deploy production models from 1-2 months to 1-2 days - a pain point that may be less obvious if your data mining exposure is competitions or MOOCs. Kamanja is free on Github, supports Kafka, MQ, Spark, HBase and Cassandra among other things. Being a new open-source product, initially, Kamanja supports rules, trees and regression. I will cover an architecture of a sample application using multiple real-time open source data, such as social network campaigns and tracking sentiment for the bank client and its competitors. Other real-time architectures cover credit card fraud detection. A brief demo will be given of the social network analysis application, with text mining. An overview of products in the space will include popular Apache big data systems, real-time systems and PMML systems. For more details: http://kamanja.org/ http://www.meetup.com/SF-Bay-ACM/events/223615901/ http://www.sfbayacm.org/event/kamanja-new-open-source-real-time-system-scoring-data-mining-models Venue sponsored by eBay, Food and live streaming sponsored by LigaDATA, San Jose, CA, July 27, 2015 Chapter Chair Bill Bruns Data Science SIG Program Chair Greg Makowski Vice Chair Ashish Antal Volunteer Coordinator Liana Ye Volunteers Joan Hoenow, Stephen McInerney, Derek Hao, Vinay Muttineni Camera Tom Moran Production Alex Sokolsky Copyright © 2015 ACM San Francisco Bay Area Professional Chapter
Views: 1190 San Francisco Bay ACM
Victor Henning discusses the value and benefits of text mining to Mendeley
 
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Interview with Dr Victor Henning CEO and Co-founder of Mendeley on the value and benefits of text mining. This includes discussion of new services and business models. For more details see the full JISC- funded report by Intelligent Digital Options - http://www.jisc.ac.uk/publications/reports/2012/value-and-benefits-of-text-mining.aspx.
Views: 248 InDigONetwork
Mining Large Multi-Aspect Data: Algorithms and Applications
 
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Author: Evangelos Papalexakis, Department of Computer Science and Engineering, University of California, Riverside Abstract: What does a person’s brain activity look like when they read the word apple? How does it differ from the activity of the same (or even a different person) when reading about an airplane? How can we identify parts of the human brain that are active for different semantic concepts? On a seemingly unrelated setting, how can we model and mine the knowledge on web (e.g., subject-verb-object triplets), in order to find hidden emerging patterns? Our proposed answer to both problems (and many more) is through bridging signal processing and large-scale multi-aspect data mining. Specifically, language in the brain, along with many other real-word processes and phenomena, have different aspects, such as the various semantic stimuli of the brain activity (apple or airplane), the particular person whose activity we analyze, and the measurement technique. In the above example, the brain regions with high activation for “apple” will likely differ from the ones for “airplane”. Nevertheless, each aspect of the activity is a signal of the same underlying physical phenomenon: language understanding in the human brain. Taking into account all aspects of brain activity results in more accurate models that can drive scientific discovery (e.g, identifying semantically coherent brain regions). In addition to the above Neurosemantics application, multi-aspect data appear in numerous scenarios such as mining knowledge on the web, where different aspects in the data include entities in a knowledge base and the links between them or search engine results for those entities, and multi-aspect graph mining, with the example of multi-view social networks, where we observe social interactions of people under different means of communication, and we use all aspects of the communication to extract communities more accurately. The main thesis of our work is that many real-world problems, such as the aforementioned, benefit from jointly modeling and analyzing the multi-aspect data associated with the underlying phenomenon we seek to uncover. In this thesis we develop scalable and interpretable algorithms for mining big multiaspect data, with emphasis on tensor decomposition. We present algorithmic advances on scaling up and parallelizing tensor decomposition and assessing the quality of its results, that have enabled the analysis of multi-aspect data that the state-of-the-art could not support. Indicatively, our proposed methods speed up the state-of-the-art by up to two orders of magnitude, and are able to assess the quality for 100 times larger tensors. Furthermore, we present results on multi-aspect data applications focusing on Neurosemantics and Social Networks and the Web, demonstrating the effectiveness of multiaspect modeling and mining. We conclude with our future vision on bridging Signal Processing and Data Science for real-world applications. More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 142 KDD2017 video
Evaluating the Benefits of Purpose Driven Data Marts
 
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Join BI and Dynamics AX subject matter expert Jason Weidenbenner for a session that will explore data marts from the perspective of IT project managers, BI architects, IT executives, or any other business partners that looking to plan the creation of a BI platform with a robust delivery process for the entire enterprise. Topics and questions posed will include: * Should you buy, build, or hack together your EDW (enterprise data warehouse)? * Using In-memory solutions for high-performing, drillable datamarts * The Personal to Corporate BI progression * Revisiting BI Center of Excellence concepts * Agile Analytics
Views: 92 MSDynamicsWorld
Re2you- Ecosystem of apps ( Patent)
 
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re2you is a patent intercloud layer which will revolutionize the way people use the Web, announced today its official launch of its personal cloud experience at The Next Web Conference in Amsterdam, running April 24 to 25. The new drag-and-drop interface works across multiple services and devices, eliminating data mining while delivering an online experience never seen before. re2you creates and combines multiple, dynamic tiles within a single browser tab and then gives users the power to drag and drop content from one tile to another instantaneously. The online experience, be it on a computer or smartphone, becomes completely customizable while, at the same time, secure from prying eyes. Key advantages of re2you include:, ● No cookies or data mining. ● No uploading or downloading of unsecured data. ● Stored data accessed via a mirror server, creating a secure level of abstraction. ● All communications and payments are fully encrypted. ● Integrates personal data from multiple platforms into a single, coherent profile. ● Lets marketers provide relevant offerings based on user experience rather than driven by data.
Cryptocurrency Data Mining Webinar - Where are the Opportunities for 2018?
 
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Bitcoin and Cryptocurrencies are a hot topic today. Many wonder if the window of opportunity has already passed them by to take advantage of this new currency. Men who say they struggle to come up with the money to pursue their dreams, may have an option not available until recently. Catch this informational conversation and see where the growth opportunities may still be explosive. If you want more information, please send Mark an email and include a good phone number to reach you: [email protected]
Views: 1469 Dream Connections
What is DATA WAREHOUSE? What does DATA WAREHOUSE mean? DATA WAREHOUSE meaning & explanation
 
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What is DATA WAREHOUSE? What does DATA WAREHOUSE mean? DATA WAREHOUSE meaning - DATA WAREHOUSE definition - DATA WAREHOUSE explanation. 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 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, and is considered a core component of business intelligence. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place and are used for creating analytical reports for knowledge workers throughout the enterprise. The data stored in the warehouse is uploaded from the operational systems (such as marketing or sales). The data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality 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. The main source of the data is cleansed, transformed, catalogued and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support. 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. A data warehouse maintains a copy of information from the source transaction systems. This architectural complexity provides the opportunity to: Integrate data from multiple sources into a single database and data model. Mere congregation of data to single database so a single query engine can be used to present data is an ODS. Mitigate the problem of database isolation level lock contention in transaction processing systems caused by attempts to run large, long running, analysis queries in transaction processing databases. Maintain data history, even if the source transaction systems do not. Integrate data from multiple source systems, enabling a central view across the enterprise. This benefit is always valuable, but particularly so when the organization has grown by merger. Improve data quality, by providing consistent codes and descriptions, flagging or even fixing bad data. Present the organization's information consistently. Provide a single common data model for all data of interest regardless of the data's source. Restructure the data so that it makes sense to the business users. Restructure the data so that it delivers excellent query performance, even for complex analytic queries, without impacting the operational systems. Add value to operational business applications, notably customer relationship management (CRM) systems. Make decision–support queries easier to write. Optimized data warehouse architectures allow data scientists to organize and disambiguate repetitive data. The environment for data warehouses and marts includes the following: Source systems that provide data to the warehouse or mart; Data integration technology and processes that are needed to prepare the data for use; Different architectures for storing data in an organization's data warehouse or data marts; Different tools and applications for the variety of users; Metadata, data quality, and governance processes must be in place to ensure that the warehouse or mart meets its purposes. In regards to source systems listed above, Rainer states, "A common source for the data in data warehouses is the company's operational databases, which can be relational databases"....
Views: 1692 The Audiopedia
Introduction to ETL Testing
 
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ETL Testing, What is ETL Testing?, Benefits of ETL Testing, What is Data warehousing?, ETL Testing process, and ETL Tester Roles & Responsibilities. 1. What is ETL? ETL refers to Extracting, Transforming and Loading of Data from any outside system to the required place. These are the basic 3 steps in the Data Integration process. . Extracting means locating the Data and removing from the source file, . Transforming is the process of transporting it to the required target file and . Loading the file in the target system in the format applicable. 2. Explain what are the ETL Testing Operations Includes? ETL testing includes . Verify whether the data is transforming correctly according to business requirements. . Verify that the projected data is loaded into the data warehouse without any truncation and data loss... . Make sure that ETL application reports invalid data and replaces with default values. . Make sure that data loads at expected time frame to improve scalability and performance 3. What are the various Tools used in ETL? . Cognos Decision Stream . Oracle Warehouse Builder . Business Objects XI . SAS business warehouse . SAS Enterprise ETL server 4) What are ETL Tester Capabilities? . In depth knowledge on the ETL tools and processes. . Needs to write the SQL queries for the various given scenarios during the testing phase. . Should be able to carry out different types of tests such as Primary Key, defaults and keep a check on the other functionality of the ETL process. 5. What are the key Benefits of ETL Testing? . Minimize the risk of the data loss . Data security . Data Accuracy . Reporting efficiency 6. What are the types of Data Warehouse Applications? . Info processing . Analytical processing . Data mining 7. What is a Three-tier Data Warehouse? Most data warehouses are considered to be a three-tier system. This is essential to their structure. . The first layer is where the data lands. This is the collection point where data from outside sources is compiled. . The second layer is known as the ‘integration layer.’ This is where the data that has been stored is transformed to meet company needs. . The third layer is called the ‘dimension layer,’ and is where the transformed information is stored for internal use. 8. What is the difference between Data Mining and Data Warehousing? . Data warehousing comes before the mining process. This is the act of gathering data from various exterior sources and organizing it into one specific location, . Data mining is when that data is analyzed and used as information for making decisions.
Views: 10031 G C Reddy
Big Data Opportunity: Structured vs. Unstructured Data
 
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http://www.patrickschwerdtfeger.com/sbi/ Where's the opportunity in Big Data? Is it with structured data or unstructured data? Experts estimate that over 95% of the data in the world today is unstructured and only 5% is structured, so there's definitely a lot MORE unstructured data to be mined. The case histories so far suggest that the biggest opportunities lie in the messy unstructured data; the data the INCLUDES the outliers rather than marginalize them. The outliers add the most interesting insights to the process and allow the algorithms to calculate probabilities using the entire sample size, rather than relying on sampling inferences based on a small subset of the population. So research your unstructured data. Look at all those machine logs and metadata and see what insights you might be able to glean. Those are the building blocks for predictive analytics and algorithms that value.
Views: 17058 Patrick Schwerdtfeger