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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: 2445 tutor2u
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: 221 NCUAchannel
How can Machine Learning benefit your organization?
 
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Blueocean Market Intelligence believes in providing comprehensive, detailed and customized end-to-end solutions. We bring in a high level of technical proficiencies, combined with business expertise, to identify and address the areas that matter the most. We seek to help you improve your marketing performance, efficiently trade-off risks against different available options, maximize customer lifetime value, and increase operational efficiency. Our solutions include predictive capabilities, where we extrapolate historical data to identify and chart out future behavior. To know more, click here http://www.blueoceanmi.com/
Views: 2342 Course5i
Data Mining
 
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Technology students give presentation on about Data Mining including the advantages/disadvantages, how to and more.
Views: 16960 techEIU
Data Mining
 
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Technology students give presentation on about Data Mining including the advantages/disadvantages, how to and more.
Views: 235 Wafeek Wahby
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: 700 Gregory Rice
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
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: 14812 Patrick Schwerdtfeger
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: 330 Deege U
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.
Data Mining Software Memory Requirements in SPM
 
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www.salford-systems.com data mining software memory requirements in the SPM Salford Predictive Modeler software suite. The SPM software suite components include CART, MARS, TreeNet and RandomForests.
Views: 192 Salford Systems
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: 1158 BenGurionUniversity
Text and Data Mining in the Humanities and Social Sciences—Strategies and Tools
 
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Peter Leonard and Lindsay King of Yale University discuss reasons for current interest in TDM, what makes a good project, and implications for libraries. They also demonstrate Yale’s Robots Reading Vogue platform, showing projects based on the ProQuest database.
Views: 1925 CRLdotEDU
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: 1439 RapidMiner, Inc.
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: 10042 Hortonworks
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: 93259 IBM Analytics
What is Bitcoin Mining?
 
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For more information: https://www.bitcoinmining.com and https://www.weusecoins.com What is Bitcoin Mining? Have you ever wondered how Bitcoin is generated? This short video is an animated introduction to Bitcoin Mining. Credits: Voice - Chris Rice (www.ricevoice.com) Motion Graphics - Fabian Rühle (www.fabianruehle.de) Music/Sound Design - Christian Barth (www.akkord-arbeiter.de) Andrew Mottl (www.andrewmottl.com)
Views: 6669146 BitcoinMiningCom
Turning Data Integration Challenges into Collateral Benefits
 
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Presentation from Salesforce.org Higher Ed Summit 2018 by: Shelley Hou, Stanford University, and Janet Hall, Stanford University. Since every approach to integration with a campus SIS has its pros and cons, selecting the "right" one is a decision often fraught with competing interests and multiple stakeholders. Come learn how a Salesforce implementation evolved from a single integration plan to an ETL, custom views, and API calls. Stanford’s Undergraduate Advising & Research (UAR) and Office of Accessible Education (OAE) will discuss the types of data coming from each integration method and demo how they are leveraging Community Cloud, Service Cloud, HEDA, and FormAssembly to better advise and accommodate their students. Find out some of the unexpected benefits of not getting everything on their data wish list, such as smoother user adoption and streamlined processes. View Slides: https://www.slideshare.net/salesforcefoundation/turning-data-integration-challenges-into-collateral-benefits
Views: 206 Salesforce.org
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
Data Science For Beginners | Who Is A Data Scientist? | Data Science Tutorial Using R | Edureka
 
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** Data Science Certification using R: https://www.edureka.co/data-science ** This Edureka "Data Science for Beginners" video talks about the basic concepts of Data Science, which includes machine learning algorithms as well as the roles & responsibilities of a Data Scientist. It also includes a demo using R Studio, that attempts to make sense of all the Data generated in the real world. This video talks about the most crucial aspects of data science and covers the following topics: 1:07 - Why Data Science? 3:06 - What is Data Science? 5:44 - Who is a Data Scientist? 6:36 - What does a Data Scientist do? 6:59 - How to solve a problem in Data Science? 17:11 - Data Science Tools 19:53 - Demo 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 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 #datascience #statisticsfordatascience #rstatistics #datascienceessentials #datasciencewithr -------------------------------------- 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, modelling, statistics and analytics. To take complete benefit of these opportunities, you need a 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 Data Science Training and Certification, Call us at US: +18336900808 (Toll-Free) or India: +918861301699 Or, write back to us at [email protected]
Views: 2438 edureka!
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: 82 QMRIBioinf
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: 1309 ACADGILD
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.
Random Forest in R - Classification and Prediction Example with Definition & Steps
 
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Provides steps for applying random forest to do classification and prediction. R code file: https://goo.gl/AP3LeZ Data: https://goo.gl/C9emgB Machine Learning videos: https://goo.gl/WHHqWP Includes, - random forest model - why and when it is used - benefits & steps - number of trees, ntree - number of variables tried at each step, mtry - data partitioning - prediction and confusion matrix - accuracy and sensitivity - randomForest & caret packages - bootstrap samples and out of bag (oob) error - oob error rate - tune random forest using mtry - no. of nodes for the trees in the forest - variable importance - mean decrease accuracy & gini - variables used - partial dependence plot - extract single tree from the forest - multi-dimensional scaling plot of proximity matrix - detailed example with cardiotocographic or ctg data random forest is an important tool related to analyzing big data or working in data science field. Deep Learning: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 49538 Bharatendra Rai
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: 331 James Cook
Databite No. 106: Virginia Eubanks
 
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Virginia Eubanks speaks about her most recent book Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. Eubanks systematically shows the impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America. The book is full of heart-wrenching and eye-opening stories, from a woman in Indiana whose benefits are literally cut off as she lays dying to a family in Pennsylvania in daily fear of losing their daughter because they fit a certain statistical profile. The U.S. has always used its most cutting-edge science and technology to contain, investigate, discipline and punish the destitute. Like the county poorhouse and scientific charity before them, digital tracking and automated decision-making hide poverty from the middle-class public and give the nation the ethical distance it needs to make inhuman choices: which families get food and which starve, who has housing and who remains homeless, and which families are broken up by the state. In the process, they weaken democracy and betray our most cherished national values. Virginia Eubanks is an Associate Professor of Political Science at the University at Albany, SUNY. In addition to her latest book, Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor, she is the author of Digital Dead End: Fighting for Social Justice in the Information Age and co-editor, with Alethia Jones, of Ain’t Gonna Let Nobody Turn Me Around: Forty Years of Movement Building with Barbara Smith. For two decades, Eubanks has worked in community technology and economic justice movements. Today, she is a founding member of the Our Data Bodies Project and a Fellow at New America. Joining her to discuss data-based discrimination are powerhouses Alondra Nelson and Julia Angwin. Alondra Nelson is president of the Social Science Research Council and professor of sociology at Columbia University. A scholar of science, technology, and social inequality, she is the author most recently of The Social Life of DNA: Race, Reparations, and Reconciliation after the Genome. Her publications also include Body and Soul: The Black Panther Party and the Fight against Medical Discrimination; Genetics and the Unsettled Past: The Collision of DNA, Race, and History; and Technicolor: Race, Technology, and Everyday Life. Julia Angwin is an award-winning investigative journalist at the independent news organization ProPublica. From 2000 to 2013, she was a reporter at The Wall Street Journal, where she led a privacy investigative team that was a Finalist for a Pulitzer Prize in Explanatory Reporting in 2011 and won a Gerald Loeb Award in 2010. Her book, Dragnet Nation: A Quest for Privacy, Security and Freedom in a World of Relentless Surveillance, was published by Times Books in 2014. In 2003, she was on a team of reporters at The Wall Street Journal that was awarded the Pulitzer Prize in Explanatory Reporting for coverage of corporate corruption. She is also the author of “Stealing MySpace: The Battle to Control the Most Popular Website in America” (Random House, March 2009).
2nd International Conference on Big Data Analysis and Data Mining
 
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Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations.
The future will be decentralized | Charles Hoskinson | TEDxBermuda
 
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This talk was given at a local TEDx event, produced independently of the TED Conferences. Tech entrepreneur and mathematician Charles Hoskinson says Bitcoin-related technology is about to revolutionise property rights, banking, remote education, private law and crowd-funding for the developing world. Charles Hoskinson is Chief Executive Officer at Thanatos Holdings, Director at The Bitcoin Education Project, and President at the Hoskinson Content Group LLC. Charles is a Colorado based technology entrepreneur and mathematician. He attended University of Colorado, Boulder to study analytic number theory in graduate school before moving into cryptography and social network theory. His professional experience includes work with NoSQL and Bigdata using MongoDB and Hadoop for several data mining projects involving crowdsource research and also development of web spiders. He is the author of several white papers on the design and deployment of low bandwidth prolog based semantical web scraping bots as well as analysis of metamorphic computer viruses through a case study on Zmist. His current projects focus on evangelism and education for Bitcoin and fully homomorphic encryption schemes. About TEDx, x = independently organized event In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)
Views: 298752 TEDx Talks
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: 243 InDigONetwork
Principal Component Analysis in R: Example with Predictive Model & Biplot Interpretation
 
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Provides steps for carrying out principal component analysis in r and use of principal components for developing a predictive model. Link to code file: https://goo.gl/SfdXYz Includes, - Data partitioning - Scatter Plot & Correlations - Principal Component Analysis - Orthogonality of PCs - Bi-Plot interpretation - Prediction with Principal Components - Multinomial Logistic regression with First Two PCs - Confusion Matrix & Misclassification Error - training & testing data - Advantages and disadvantages principal component analysis is an important statistical tool related to analyzing big data or working in data science field. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 25843 Bharatendra Rai
Definiens Corporate Video
 
06:03
The Definiens 2012 corporate video provides an overview of the advantages of Quantitative Digital Pathology and the benefits from using Definiens leading solutions for image and data analysis. It includes statements from Thomas Heydler (CEO Definiens), Mark Lloyd (Moffitt Cancer Center), Gerd Binnig (Definiens founder and Nobel Laureate for Physics) and Heinz Hoeffler (Technical University Munich).
Views: 3725 DefiniensLifeTV
Measures of Dispersion | Range, Sample Variance and Standard Deviation | Statistics Tutorial 2018
 
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Measures of Dispersion: Range, Sample Variance, and Standard Deviation | Statistics Tutorial 2018 https://acadgild.com/big-data/data-science-training-certification?aff_id=6003&source=youtube&account=YvGeUSeQGYU&campaign=youtube_channel&utm_source=youtube&utm_medium=measures-of-dispersion-sumit&utm_campaign=youtube_channel Hello and Welcome to Statistics Tutorial conducted by Acadgild. In this tutorial, you will be able to learn Range, Sample Variance, and Standard Deviation as Measures of Dispersion. Measures of Dispersion: The concept measures of dispersion characterize how to spread out the distribution, i.e., how variable the data are. The commonly used dispersion measures include, • Range • Sample Variance • Standard Deviation Range: The Range is the difference between the largest and the smallest observations in the sample. For example, the minimum and maximum blood pressure is 113 and 170 respectively. Hence the range is 57 mmHg • Easy to calculate • Implemented for both “best” or “worst” case scenarios • Too sensitive for extreme values Sample Variance, s2, is the arithmetic mean of the squared deviations from the sample mean: Standard Deviation: The sample standard deviation (s) is the square root of the variance. The sample standard deviation has an advantage of being in the same units as the original variable (x). Go through the entire video on statistics tutorial which is the best tutorial with key points and easy to learn. Please like and share the video and kindly give your feedbacks and subscribe the channel for more tutorial videos. 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: 2637 ACADGILD
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: 23459 Simplilearn
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: 107 KDD2017 video
SQL Server Enterprise Data Mining
 
01:58
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: 313 DesignMind
Talks at Twelve: Irwin Epstein, "Rediscovering Context: Clinical Data-Mining Findings ..."
 
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"Rediscovering Context: Clinical Data-Mining Findings and the Future of Evidence-informed Practice" Audio and powerpoint recording from the BCTR Talks at Twelve series. Recorded April 25, 2013 To arrive at best practices, academic proponents of Evidence-based Practice in social work place emphasis on seeking the most robust findings concerning intervention effectiveness. Their strategy in seeking the "best available evidence" involves employing randomized controlled experiments and meta-analyses to maximize effect size. In so doing, contextual effects —i.e., differences in client, worker and organizational characteristics—are systematically negated. Dr. Epstein will review findings derived from quasi-experimental, practitioner-initiated Clinical Data-Mining dissertation studies that suggest that the search for best practices must also take context into account. For this, a more methodologically pluralist Evidence-informed Practice that includes practitioners and available practice data in knowledge production is required. Irwin Epstein occupies the Helen Rehr Chair in Applied Social Work Research (Health & Mental Health) at the Silberman School of Social Work at Hunter College of the City University of New York, where he directs, teaches and supervises dissertation research in the Ph.D. program. In addition, he is Adjunct Professor at the Mount Sinai Medical Center, Department of Community Medicine where he provides research consultation and seminars to participants in the International Leadership Enhancement and Exchange Program.
Advantage of Entrepreneur, Profile Page, and Associates leads From Rotator
 
50:00
Creating a "Universal Income" for entrepreneurs. Using our state-of-the-art integrated inbound marketing platform, social network, artificial intelligence, business services, e-wallet, coin exchange, mining data center, incubator and block chain income platforms for success in the crypto-preneurial and entrepreneurial markets. Markethive Platform Includes: HomePage Augmented Webinars Apps Events Blog Casting Business Services Survey Systems Groups Markethive Platform Includes: HomePage Apps Events Blog Casting Business Services Survey Systems Groups Support system News Feed Profile Hub Micro payments Training Automation Campaigns 10Traffic Portals Mining Hive Capture Pages Friends More to come.. Louis Harvey http://hive.pe/zv
Views: 20 Louis Harvey
Database Lesson #8 of 8 - Big Data, Data Warehouses, and Business Intelligence Systems
 
01:03:13
Dr. Soper gives a lecture on big data, data warehouses, and business intelligence systems. Topics covered include big data, the NoSQL movement, structured storage, the MapReduce process, the Apache Cassandra data model, data warehouse concepts, multidimensional databases, business intelligence (BI) concepts, and data mining,
Views: 77202 Dr. Daniel Soper
Naive Bayes Classifier | Naive Bayes Algorithm | Naive Bayes Classifier With Example | Simplilearn
 
43:45
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: 23100 Simplilearn
Predictive Analytics, Machine Learning, and Recommendation Systems on Hadoop
 
01:05:01
Originally recorded January 30, 2014. In the world of ever growing data volumes, how do you extract insight, trends and meaning from all that data in Hadoop? Do you need help transforming your big data into big knowledge? Organizations know that the key to competitive advantage is in using advanced analytics to discover trends and use them to your advantage faster than the competition. Getting relevant information from big data requires a different approach. Churning out a couple of analytical models a week isn't going to cut it. If you're using big data to identify trends, spot weaknesses and predict outcomes, you need proven analytical software that's a lot faster, more efficient, accurate, and easy to use. Learn more about how to reveal insights in your Big data and redefine how your organization solves complex problems. You will learn how to: Use sophisticated analytics in both a visual interface and a coding interface. Prepare, explore and model multiple scenarios using data volumes never before possible to generate accurate and rapid insights. Interact with the data to add or drop variables into the model and instantly see how their influence provides increased predictive power. Easily perform modeling tasks interactively and on-the-fly Quickly understand your model fit with model diagnostics - interactively and in real time (typically in seconds instead of hours or days). Ask what-if questions on all the data. Use a scalable recommendation system to help improve customer experience through profiling users and items and finding how to relate them About Wayne Thompson Wayne Thompson is the Manager of SAS Predictive Analytics Product Management at SAS.Over the course of his 20-year tenure at SAS he has been credited with bringing to market landmark SAS analytics technologies (SAS Text Miner, SAS Credit Scoring for Enterprise Miner, SAS Model Manager, SAS Rapid Predictive Modeler, SAS Scoring Accelerator for Teradata, and SAS Analytics Accelerator for Teradata). Current focus initiatives include easy to use self-service data mining tools for business analysts, decision management and massively parallel high performance analytics. Wayne received his Ph.D. and M.S from the University of Tennessee in 1992 and 1987, respectively. During his PhD program, he was also a visiting scientist at the Institut Superieur d'Agriculture de Lille, Lille, France. Georgia Mariani is Principal Product Marketing Manager for Statistics at SAS. She drives marketing direction for SAS' statistics software initiatives. Georgia began her career at SAS as a systems engineer, consulting with sales prospects in the government and education industries regarding their analytical business questions and implementing SAS software and solutions. Georgia received her M.S. degree in Mathematics with a concentration in Statistics in 1996 and her B.S. degree in Mathematics in 1992 from the University of New Orleans. During her Master's program she was awarded a fellowship with NASA. Produced by: Yasmina Greco Don't miss an upload! Subscribe! http://goo.gl/szEauh - Stay Connected to O'Reilly Media. Visit http://oreillymedia.com Sign up to one of our newsletters - http://goo.gl/YZSWbO Follow O'Reilly Media: http://plus.google.com/+oreillymedia https://www.facebook.com/OReilly https://twitter.com/OReillyMedia
Views: 3316 O'Reilly
Applications of Predictive Analytics in Legal | Litigation Analytics, Data Mining & AI | Great Lakes
 
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#PredictiveAnalytics | Learn the prediction of outcome or treatment of a case by legal courts of Appeals based on historical data using predictive analytics. Watch the video to understand analytics in legal using case study on real-life data set. How litigation analytics can flourish with the use of data mining and AI. Know more about our analytics Program: PGP- Business Analytics: https://goo.gl/V9RzVD PGP- Big Data Analytics: https://goo.gl/rRyjj4 Business Analytics Certification Program: https://goo.gl/7HPoUY #LegalTech #LegalAnalytics #GreatLearning #GreatLakes About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/ - Follow our Blog: https://www.greatlearning.in/blog/?utm_source=Youtube
Views: 728 Great Learning
AutoKazanova42 - Marios Michailidis, Research Data Scientist, H2O.ai
 
36:16
This presentation was recorded at #H2OWorld 2017 in Mountain View, CA. Learn more about H2O.ai: https://www.h2o.ai/. Follow @h2oai: https://twitter.com/h2oai. - - - Recent world no.1 Kaggle Grandmaster, Marios Michailidis, is now a Research Data Scientist at H2O.ai. He is finishing his PhD in machine learning at the University College London (UCL) with a focus on ensemble modeling and his previous education entails a B.Sc in Accounting Finance from the University of Macedonia in Greece and an M.Sc. in Risk Management from the University of Southampton. He has gained exposure in marketing and credit sectors in the UK market and has successfully led multiple analytics’ projects based on a wide array of themes including: acquisition, retention, recommenders, uplift, fraud detection, portfolio optimization and more. Before H2O.ai, Marios held the position of Senior Personalization Data Scientist at dunnhumby where his main role was to improve existing algorithms, research benefits of advanced machine learning methods, and provide data insights. He created a matrix factorization library in Java along with a demo version of personalized search capability. Prior to dunnhumby, Marios has held positions of importance at iQor, Capita, British Pearl, and Ey-Zein. At a personal level, he is the creator and administrator of KazAnova, a freeware GUI for quick credit scoring and data mining which is made absolutely in Java. In addition, he is also the creator of StackNet Meta-Modelling Framework. His hobbies include competing in predictive modeling competitions and was recently ranked 1st out of 465,000 data scientists on the popular data competition platform, Kaggle.
Views: 619 H2O.ai
HMDA: 2018 Changes and Data Analytics
 
01:21:09
In November 2017, Richey May hosted a webinar for independent mortgage banking companies on the important changes forthcoming, best practices for HMDA data analysis, and how to use the data to your company’s advantage. Topics during the webinar included: changes to lender coverage, new data fields, expansion of loans reported, how and when to report, making the data public, enforcement, regression analysis and Fair Lending compliance, and nationwide HMDA data analytics.
Views: 475 Richey May & Co
Using JReview to Analyze Clinical and Pharmacovigilance Data in Disparate Systems
 
57:31
Learn how JReview can be used to analyze clinical and pharmacovigilance data. -- Sponsors and CROs naturally rely on various clinical and safety systems from a multitude of software vendors. However, continuously accessing disparate sources for the reporting, analysis, and monitoring of data can be a treacherous undertaking, if you don't have a solution that connects to them right out of the box. That's where JReview comes in. For almost two decades, life sciences companies, research organizations, in addition to the government, have relied on JReview for the comprehensive analysis and monitoring of clinical and pharmacovigilance data. In this webinar, we discussed: • The features and benefits of JReview, including the new functionality in v10.0 (e.g., risk-based monitoring analytics reporting on the clinical data itself, etc.) • Benefits of using JReview for: o Reporting and query of your clinical data o Supplying internal and/or external users/sponsors information o Providing a secure way for your internal users and/or sponsor users to access the clinical data • Examples of how customers use JReview with OC/RDC • The implementation process and options • Your own questions and challenges To view this webinar in its entirety, please visit: http://www.perficient.com/Thought-Leadership/On-Demand-Webinars/2014/Using-JReview-to-Analyze-Clinical-and-Pharmacovigilance-Data-in-Disparate-Systems Stay on top of Life Sciences technologies by following us here: Twitter: http://www.twitter.com/Perficient_LS Facebook: http://www.facebook.com/Perficient LinkedIn: http://www.linkedin.com/company/165444 Google+: https://plus.google.com/+Perficient SlideShare: http://www.slideshare.net/PerficientInc
Views: 7528 Perficient, Inc.
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
CERIAS - 2015-10-21 - Anonymized Data
 
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Anonymized Data Koray Mancuhan - Purdue University Oct 21, 2015 Abstract Privacy has been a hot issue since early 2000s, in particular with the rise of social network and data outsourcing. Data privacy is a big concern in data outsourcing because it involves sharing personal data with third parties. In this talk, I will give an introduction to data privacy on topics such as privacy standards, data anonymization techniques, and data anonymization usage in data outsourcing and data mining. Then, I will present our work in data mining using anonymized data. We propose a data publisher-third party decision tree learning method for outsourced private data. The privacy model is anatomization/fragmentation: the third party sees data values, but the link between sensitive and identifying information is encrypted with a key known only to data publisher. Data publishers have limited processing and storage capability. Both sensitive and identifying information thus are stored on the third parties. The approach presented also retains most processing at the third parties, and data publisher-side processing is amortized over predictions made by the data publishers. Experiments on various datasets show that the method produces decision trees approaching the accuracy of a non-private decision tree, while substantially reducing the data publisher's computing resource requirements. About the Speaker Koray is a PhD student in the Department of Computer Science at Purdue University. He is currently a member of the privacy preserving data mining lab under the supervision of Chris Clifton. His research elaborates the data mining models from the anonymized data. The challenge in his research is the injected uncertainty into data because of anonymization methods. In most cases, uncertainty slows down the data mining models and require special mechanisms to exploit noisy data. His work includes learning algorithms such as k-NN classification, SVM classification, decision tree classification and frequent itemset mining. Koray received his masters degree in Computer Science from Purdue University and his undergraduate degree in Computer Engineering from Galatasaray University. Throughout his masters degree, he studied on data mining and social fairness, and authored papers in this topic. Before joining to Purdue CS, he did his research in semantic web area. He was a former member of Complex Networks lab in Galatasaray University where he worked in developing a new automatic web service annotation tool. http://www.cerias.purdue.edu
Views: 425 ceriaspurdue
telefoongidsboek.nl
 
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About Nederlands business directory developed in 2015 and launched public 2015 September. Benefits of owning this business directory Huge amount of data that is already indexed in Google No need for new content to keep website alive Content is updated by companies if they find it outdated New content is being added when someone registers Project doesn't need much maintenance Using existing data and some data modeling you can easily increase number of pages in website Monetization Website earns from Adsense, last moth it was about 60 USD. See Attached documents for screenshot prof. Includes Over 1 300 000 business contacts with emails, website address, business address,state,phone,fax and other data . Includes data mining methods for data extraction from business websites like HTML data (keywords, heading tags, links and different RSS data. Technology Directory is based on custom PHP cakePHP Framework and MySQL database so you can easily find developers for it. There is simple administration interface created for new registrations and updating existing data. Maintenance Project doesn't need much maintenance only few times a month overview of new subscriptions and approve of data modifications by registered companies. If you are not familar wih Dutch language it is not a problem. We was running this project for more than years and Google Translate was enough to answer support questions Traffic 90% traffic is organic. DATA SOURCE: commercial databases, registrations. Server Requirements Website needs MYSQL server, shared hosting is enough. Post Sale Support (INCLUDES in to sale) We will move website to your hosting We will transfer domain to your account We will replace Adsense code with yours We will replace Analytics, Webmaster tools code with yours Reason for selling Money Our company specializes in business directories for more then 7 years. During that period we started more then 50 different business catalogues all over the world. Most successful projects are bizin.eu and bizin.asia
Views: 70 Bizin.eu
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: 85 MSDynamicsWorld
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: 1127 The Audiopedia
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: 876 O'Reilly

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