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الـ Data Mining | #عتاد_صلب
 
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في هذه الحلفة نتحدث عن Data Mining وكيف تعمل وماهي خطوات عملها ؟ شُكر خاص لـ فراس الطويل - عتاد صلب ، نُثري المحتوى العربي في مجال الهاردوير خصوصاً والحاسب بشكل عام. _ عبدالرحمن البلوي https://twitter.com/@abdulrhmanb - https://twitter.com/3tad_slb http://www.facebook.com/3tad.Slb - مراجع https://en.wikipedia.org/wiki/Data_mining#cite_note-Fayyad-4 https://en.wikipedia.org/wiki/Data_pre-processing http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/datamining.htm https://www.youtube.com/watch?v=W44q6qszdqY&t=4s https://www.tutorialspoint.com/data_mining/dm_knowledge_discovery.htm http://www.kdnuggets.com/2016/01/businesses-need-one-million-data-scientists-2018.html https://www2.deloitte.com/content/dam/html/us/analytics-trends/2016-analytics-trends/pdf/analytics-trends.pdf - تواصل معنا على : [email protected]
Views: 6623 عتاد صلب
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 84981 edureka!
Exploratory Data Analysis (EDA) Using Python (Jupyter Notebook)
 
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In this video you will learn how to perform Exploratory Data Analysis using Python. We will see how to slice data using Pandas, how to perform computing summary statistics using Numpy and how to vizualise data using Matplotlib and Seaborn. Exploratory data analysis is very usefull while building Statistical/Machine Learning models. It helps to understand the structure of the data in order to be able to build a good predictive model ANalytics Study Pack : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 15553 Analytics University
Data Mining Lecture - - Data Analytics life cycle (Eng-Hindi)
 
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Business Intelligence Big data issues are solved using data analytics life cycle The key roles of data analytics are explained -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 23336 Well Academy
28c3 - Data Mining the Israeli Census
 
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This video is part of the Infosec Video Collection at SecurityTube.net: http://www.securitytube.net 28c3 - Data Mining the Israeli Census http://events.ccc.de/congress/2011/Fahrplan/attachments/2010_28c3-dmic.pdf Data Mining the Israeli Census Insights into a publicly available registry The entire Israeli civil registry database has been leaked to the internet several times over the past decade. In this talk, we examine interesting data that can be mined and extracted from such database. Additionally, we will review the implications of such data being publicly available in light of the upcoming biometric database. The Israeli census database has been freely available on the Internet since 2001. The database has been illegally leaked due to incompetent data security policies in the Ministry of Interior of Israel, which is responsible for the management of the Israeli census. The data available includes all personal data of every Israeli citizen: name, ID number, date and location of birth, address, phone number and marital status, as well as linkage to parents and spouses. In this talk we discuss various statistics, trends and anomalies that such data provides us with insight to. Personal details will obviously be left out of the talk, though it is important to note that any person who wishes to retrieve such details can easily do so. We will end the talk with a discussion about upcoming and relevant privacy issues in light of Israel's soon-to-be biometric database.
Views: 90 SecurityTubeCons
KDD ( knowledge data discovery )  in data mining in hindi
 
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#kdd #datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 92760 Last moment tuitions
MATLAB Tools for Scientists: Introduction to Statistical Analysis
 
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Free MATLAB Trial: https://goo.gl/yXuXnS Request a Quote: https://goo.gl/wNKDSg Contact Us: https://goo.gl/RjJAkE Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe ------------------------------------------------------------------------- Researchers and scientists have to commonly process, visualize and analyze large amounts of data to extract patterns, identify trends and relationships between variables, prove hypothesis, etc. A variety of statistical techniques are used in this data mining and analysis process. Using a realistic data from a clinical study, we will provide an overview of the statistical analysis and visualization capabilities in the MATLAB product family. Highlights include: • Data management and organization • Data filtering and visualization • Descriptive statistics • Hypothesis testing and ANOVA • Regression analysis
Views: 21113 MATLAB
RapidMiner Tutorial - Evaluation  (Data Mining and Predictive Analytics System)
 
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A tutorial discussing analytics evaluation with RapidMiner, an open source system for data mining, predictive analytics, machine learning, and artificial intelligence applications. For more information: http://rapid-i.com/ Brought to you by Rapid Progress Marketing and Modeling, LLC (RPM Squared) http://www.RPMSquared.com/
Views: 5478 Predictive Analytics
Basic Data Analysis with Java : Business Intelligence | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2w2lQqE]. The aim of this video is to deal with Business Intelligence. It will use Apache POI for creating and reading spreadsheets, as well as show what users will do in MS Excel o Understand why as a data analyst, you need to save time using MS Excel o Perform some reads and writes of existing MS Excel spreadsheets For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 3443 Packt Video
Data Analytics for Beginners | Introduction to Data Analytics | Data Analytics Tutorial
 
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Data Analytics for Beginners -Introduction to Data Analytics https://acadgild.com/big-data/data-analytics-training-certification?utm_campaign=enrol-data-analytics-beginners-THODdNXOjRw&utm_medium=VM&utm_source=youtube Hello and Welcome to data analytics tutorial conducted by ACADGILD. It’s an interactive online tutorial. Here are the topics covered in this training video: • Data Analysis and Interpretation • Why do I need an Analysis Plan? • Key components of a Data Analysis Plan • Analyzing and Interpreting Quantitative Data • Analyzing Survey Data • What is Business Analytics? • Application and Industry facts • Importance of Business analytics • Types of Analytics & examples • Data for Business Analytics • Understanding Data Types • Categorical Variables • Data Coding • Coding Systems • Coding, coding tip • Data Cleaning • Univariate Data Analysis • Statistics Describing a continuous variable distribution • Standard deviation • Distribution and percentiles • Analysis of categorical data • Observed Vs Expected Distribution • Identifying and solving business use cases • Recognizing, defining, structuring and analyzing the problem • Interpreting results and making the decision • Case Study Get started with Data Analytics with this tutorial. Happy Learning 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: 287741 ACADGILD
Types of Data: Nominal, Ordinal, Interval/Ratio - Statistics Help
 
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The kind of graph and analysis we can do with specific data is related to the type of data it is. In this video we explain the different levels of data, with examples. Subtitles in English and Spanish.
Views: 961521 Dr Nic's Maths and Stats
An Introduction to Linear Regression Analysis
 
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Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 831222 statisticsfun
More Data Mining with Weka (1.3: Comparing classifiers)
 
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More Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 3: Comparing classifiers http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/Le602g https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 17883 WekaMOOC
Advanced Data Mining with Weka (1.4: Looking at forecasts)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 4: Looking at forecasts http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/JyCK84 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 5688 WekaMOOC
Basic Data Analysis in RStudio
 
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This clip explains how to produce some basic descrptive statistics in R(Studio). Details on http://eclr.humanities.manchester.ac.uk/index.php/R_Analysis. You may also be interested in how to use tidyverse functionality for basic data analysis: https://youtu.be/xngavnPBDO4
Views: 147431 Ralf Becker
Getting Started with SAS Enterprise Miner: Building Decision Trees
 
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http://support.sas.com/software/products/miner/index.html Chip Robie of SAS presents the third in a series of six "Getting Started with SAS Enterprise Miner 13.2" videos. This third video demonstrates building decision trees in SAS Enterprise Miner. For more information regarding SAS Enterprise Miner, please visit http://support.sas.com/software/products/miner/index.html SAS ENTERPRISE MINER SAS Enterprise Miner streamlines the data mining process so you can create accurate predictive and descriptive analytical models using vast amounts of data. Our customers use this software to detect fraud, minimize risk, anticipate resource demands, reduce asset downtime, increase response rates for marketing campaigns and curb customer attrition. LEARN MORE ABOUT SAS ENTERPRISE MINER http://www.sas.com/en_us/software/analytics/enterprise-miner.html SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 75,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world The Power to Know.® VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 56947 SAS Software
SAS Enterprise Miner: Impute, Transform, Regression & Neural Models
 
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http://support.sas.com/software/products/miner/index.html Chip Robie of SAS presents the fourth in a series of six "Getting Started with SAS Enterprise Miner 13.2" videos. This fourth video demonstrates imputing and transforming data, building a neural network, and building a regression model with SAS Enterprise Miner. For more information regarding SAS Enterprise Miner, please visit http://support.sas.com/software/products/miner/index.html SAS ENTERPRISE MINER SAS Enterprise Miner streamlines the data mining process so you can create accurate predictive and descriptive analytical models using vast amounts of data. Our customers use this software to detect fraud, minimize risk, anticipate resource demands, reduce asset downtime, increase response rates for marketing campaigns and curb customer attrition. LEARN MORE ABOUT SAS ENTERPRISE MINER http://www.sas.com/en_us/software/analytics/enterprise-miner.html SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 75,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world The Power to Know.® VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 45799 SAS Software
Big Data: To Explain or To Predict?: Galit Shmueli
 
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Speaker: Galit Shmueli, Tsing Hua Distinguished Professor, Institute of Service Science and Director - Center for Service Innovation & Analytics, College of Technology Management, National Tsing Hua University, Taiwan Topic: Big Data – To Explain or To Predict? Big Data Experts Speaker Series @ Rotman March 4, 2016 Located in downtown Toronto and part of the University of Toronto, the Rotman School of Management (http://www.rotman.utoronto.ca) is the top business school in Canada. Rotman offers a Full-Time MBA program, and several programs for working professionals, including the Morning and Evening MBA, Master of Finance, One-Year Executive MBA and Omnium Global Executive MBA. Whichever degree or program you choose, Rotman will give an edge in your career and help you make the most of your potential. #anewwaytothink #newwaytothink #Rotman #RotmanSchool
How Random Forest algorithm works
 
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In this video I explain very briefly how the Random Forest algorithm works with a simple example composed by 4 decision trees. The presentation is available at: https://prezi.com/905bwnaa7dva/?utm_campaign=share&utm_medium=copy
Views: 325758 Thales Sehn Körting
Short Story Presentation CMPE-255
 
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This presentation video is created as part of CMPE 255 - Data Mining Short Story class assignment. Content is inspired by this research paper Link : http://www.cs.newpaltz.edu/~lik/publications/Jianguo-Chen-IS-2018.pdf
Views: 74 shashwat jain
R Programming For Beginners | Data Science Tutorial | Simplilearn
 
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Become an expert in the various data analytics techniques using R. Master the data exploration, data visualization, predictive analytics, and descriptive analytics techniques. Get hands-on practice on R CloudLabs by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music Industry, and on unemployment. The course is best suited for beginners as well as experienced professionals who want to use R for data analytics. Data Science Certification Training - R Programming: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Data-Programming-Uenf8DbOjz0&utm_medium=SC&utm_source=youtube For a new-comer to the analytics field, this course provides the best required foundation. The training also delves into statistical concepts which are important to derive the best insights from available data and to present the same using executive level dashboards. Finally we introduce Power BI, which is the latest and the best tool provided by Microsoft for analytics and data visualization. What are the course objectives? This course will enable you to: 1. Gain a foundational understanding of business analytics 2. Install R, R-studio, and workspace setup. You will also learn about the various R packages 3. Master the R programming and understand how various statements are executed in R 4. Gain an in-depth understanding of data structure used in R and learn to import/export data in R 5. Define, understand and use the various apply functions and DPLYP functions 6. Understand and use the various graphics in R for data visualization 7. Gain a basic understanding of the various statistical concepts 8. Understand and use hypothesis testing method to drive business decisions 9. Understand and use linear, non-linear regression models, and classification techniques for data analysis 10. Learn and use the various association rules and Apriori algorithm 11. Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: IT professionals looking for a career switch into data science and analytics Software developers looking for a career switch into data science and analytics Professionals working in data and business analytics Graduates looking to build a career in analytics and data science Anyone with a genuine interest in the data science field Experienced professionals who would like to harness data science in their fields Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 16726 Simplilearn
Datawatch and Angoss - fast data prep and analytics
 
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In today’s high speed analytics marketplace it is no surprise that data volumes and sources are expanding at an accelerating rate. On a daily basis, analysts spend up to 80 percent of their time collecting data from numerous sources such as the web, pdf’s, text reports, log files and many more to prepare it for analysis. Analysts are further challenged to make this data actionable with the use of predictive modeling. The alliance between Datawatch and Angoss offers businesses the fastest and most easy-to-use applications which significantly reduce time spent on data extraction, data preparation, and predictive modeling. Datawatch Monarch works with a wide range of report formats including PDF, XML, HTML, text, spool and ASCII files. Analysts can easily access data from invoices, sales reports, balance sheets, customer lists, inventory, logs and more. Data is then cleansed and consolidated into a single file for immediate consumption into any of the Angoss software applications. Analysts can now focus on translating their data into business value, without having to code, using the most easy-to-use and analyst recognized data mining and modeling techniques, such as Angoss’ best in class Decision Trees and Strategy Trees, to uncover important patterns within a dataset, identify good predictors, and produce accurate, stable and actionable predictions. Let us help you provide your business with the fastest and easiest tools for data acquisition, preparation, and business analytics.
Predictive Analytics using R with Drupal
 
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By Eric Weinberg I have gotten a lot of interest recently on my use of R, google analytics and Drupal to create a website that has dynamic content that is predictive based on a users previous browsing history of the site and behavior relative to site goals. The project uses R, and an additional server, and can be used to switch or modify content on a page to help guide users. This is a big topic and a bit off the the traditional path so it would be an introduction to some the concepts and tools used to build predictive analytics into a drupal website. Learning Objectives & Outcomes: Understand infrastructure and tools needed for predictive analytics Understand basics of R and how it (and/or Python) can be used to aid the process Understand how to integrate back-end prediction server with your Drupal front-end https://2018.tcdrupal.org/session/predictive-analytics-using-r-drupal
Views: 114 Twin Cities Drupal
Big Data تعالوا نتكلم عن ال
 
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الـ big data كلمة مشهورة قوي اليومين دول, هنحاول نوضح إيه هي بالظبط و ناخد فكرة عامة عن إزاي بتشتغل. نرحب بأي تعليقات أو feedback! ال PDF: https://drive.google.com/open?id=0B4eh4yvSGv6KN1Mtb0MwMmR1OFk
Busines Intelligence Role of Data Warehousing in BI
 
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Project Name: e-Content generation and delivery management for student –Centric learning Project Investigator:Prof. D V L N Somayajulu
Views: 130 Vidya-mitra
Finding mean, median, and mode | Descriptive statistics | Probability and Statistics | Khan Academy
 
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Here we give you a set of numbers and then ask you to find the mean, median, and mode. It's your first opportunity to practice with us! Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/probability/descriptive-statistics/central_tendency/e/mean_median_and_mode?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Watch the next lesson: https://www.khanacademy.org/math/probability/descriptive-statistics/central_tendency/v/exploring-mean-and-median-module?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/descriptive-statistics/central_tendency/v/statistics-intro-mean-median-and-mode?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it! About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 2111538 Khan Academy
Ph.D. Defense - Dr. Jose A. Ruiperez Valiente
 
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Ph.D. in Telematics Engineering Defense - 31st of May, 2017 Title: Analyzing the Behavior of Students Regarding Learning Activities, Badges, and Academic Dishonesty in MOOC Environments Supervisor: Dr. Pedro J. Muñoz Merino URL Thesis: http://eprints.networks.imdea.org/1582/1/ThesisJoseRuiperez_IMDEA.pdf Slides: https://www.slideshare.net/JoseAntonioRuiprezVa/phd-defense-dr-jose-a-ruiperez-valiente Abstract: The 'big data' scene has brought new improvement opportunities to most products and services, including education. Web-based learning has become very widespread over the last decade, which in conjunction with the MOOC phenomenon, it has enabled the collection of large and rich data samples regarding the interaction of students with these educational online environments. We have detected different areas in the literature that still need improvement and more research studies. Particularly, in the context of MOOC and SPOC, where we focus our data analysis on the platforms Khan Academy, Open edX and Coursera. More specifically, we are going to work towards learning analytics visualization dashboards, carrying out an evaluation of these visual analytics tools. Additionally, we will delve into the activity and behavior of students with regular and optional activities, badges and their online academically dishonest conduct. The analysis of activity and behavior of students is divided first in exploratory analysis providing descriptive and inferential statistics, like correlations and group comparisons, as well as numerous visualizations that facilitate conveying understandable information. Second, we apply clustering analysis to find different profiles of students for different purposes e.g., to analyze potential adaptation of learning experiences and pedagogical implications. Third, we also provide three machine learning models, two of them to predict learning outcomes (learning gains and certificate accomplishment) and one to classify submissions as illicit or not. We also use these models to discuss about the importance of variables. Finally, we discuss our results in terms of the motivation of students, student profiling, instructional design, potential actuators and the evaluation of visual analytics dashboards providing different recommendations to improve future educational experiments.
#London Insurance Disrupters - Analytics & Big Data - Part 2 of 4
 
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First Panel Session: What's so special about Analytics and Big Data? What do they enable you to do? A Video documenting our Insurance Disrupters Workshop on Analytics & Big Data: http://www.meetup.com/newfinance/events/161074122/ Wednesday 26th February 2014 at Rainmaking Loft, London See the workshop diagrams here: http://files.meetup.com/2243521/2014-02%20Workshop%20diagrams-Insurance%20Disrupters-Analytics%26Big%20Dat.pdf See the photos here: http://www.meetup.com/newfinance/photos/20377322/ Thank You to our event sponsors: Aviva Innovation: http://www.aviva.co.uk/innovation/ Iress: http://www.iress.co.uk/ S2DS: http://www.s2ds.org/
Views: 354 NewFinance
Regression Analysis - SEC Football Data
 
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Blank worksheet can be found here: http://stoneypryor.com/stat/Regression%20Worksheet%20-%20SEC%20data.pdf
Views: 263 StoneyP94
L'analyse statistique des clientèles : Cas Piton
 
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cours gestion muc https://www.monbtsmuc.com www.exo-video.com
R-Session 1 - Statistical Learning - Introduction
 
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Reference: (Book) An Introduction to Statistical Learning with Applications in R (Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani) http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Fourth%20Printing.pdf Reference (Lecture Notes) [1] With permission from Dr. Tibshirani and Dr. Hastie, the Lecture notes are adopted from Stanford-Online StatLearning Statistical Learning [2] With permission from Dr. Al Sharif (USC) part of the Lecture notes were adopted from "DSO 530: Applied Modern Statistical Learning Techniques".
Views: 9720 Hamed Hasheminia
Learn Credit Risk Modelling using SAS | DexLab Analytics | Part 3
 
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---- First you watch ---- Credit Risk Modelling using SAS ( Part 1) - https://www.youtube.com/watch?v=UKvLr_MYVLw Credit Risk Modelling using SAS ( Part 2) - https://www.youtube.com/watch?v=GOCDTcbG45g In this video, you will understand how to identify Normality. There are mainly 3 basic techniques for identifying normality for a given variable – Descriptive, Statistical and Graphical Measures. Also, you will learn more about the descriptive measures of identifying normality. For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/+Dexlabanalyticsbigdata - Facebook : https://www.facebook.com/dexlabanalytics/ - Twitter: https://twitter.com/Dexlabanalytics - LinkedIn: https://www.linkedin.com/company/dexlab-analytics
Views: 589 Dexlab Analytics
Python For Data Science Tutorial 3 (in Urdu)
 
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Watch it at 480p (or higher) screen resolution. This tutorial includes: 1. Tuples and Random Number Generation in Python 2. Pie-chart, Bar chart and histogram creation using matplotlib 3. Accessing data in structured flat-file form (text files, csv files and excel files covered in this tutorial) Relevant data set files and ppt slides for this tutorial may be downloaded from: https://sites.google.com/site/drraheelsiddiqi/teaching/python-for-data-science
Views: 287 Raheel Siddiqi
SPSS Modeler Tutorial 1 - Part1
 
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This tutorial is made by Center for Marketing Engineering, The Chinese University of Hong Kong. Objectives of Tutorial 1: 1. Learn basic skills of SPSS Modeler 2. Learn RFM Analysis of SPSS Modeler Data for master 2015: https://www.dropbox.com/s/rl9og69eef18t4t/SPSS%20Modeler%20Tutorial%201.zip?dl=0 Data for Undergraduate 2016: https://www.dropbox.com/s/tajyuwhj0usfdnk/SPSS%20Tutorial%20Fall%202016.rar?dl=0
Social Media Plan: 7 key elements to be successful
 
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Discover the 7 key ingredients you need to create a successful social media plan. Does include a free download (no email needed) on this site: http://felixrelationshipmarketing.com Your social media plan should include: #1 Mission #2 Goals #3 Target Audience #4 Social Media Presence #5 Content Strategy #6 Resources #7 Track Progress Discover 23 key questions you need to ask yourself to create a successful social media plan: http://felixrelationshipmarketing.com/downloads/social-media-strategy-template.pdf
Views: 48 Guan Felix
Tableau Data Science Tutorial 7 | How to connect Tableau with R | Tableau R Integration examples
 
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Hi guys....in this tableau tutorial video I have talked about how you can integrate tableau with R also I have given an example of tableau r integration to better understand this. For any tableau training, tableau consulting and tableau freelancing for tableau dashboard development please reach out to my email [email protected]
Views: 697 Abhishek Agarrwal
How to convert multidimensional measurements - Measurement Chapter Section 1
 
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This video demonstrates dimensional analysis on multidimensional measurements. https://sites.google.com/site/swtcmath What are multidimensional measurements? They are things that use two distinct units of measure to convey meaning. For example, when you want to describe how fast you are moving you need to involve both distance and time. Examples are things such as 30 miles per hour (driving a car) or 10 feet per second (walking). The table of conversion factors used in this video can be obtained here: https://sites.google.com/site/swtcappliedmath/home/measurement-chapter/Measurement%20Conversion%20Table%20from%20Applied%20Math.pdf?attredirects=0&d=1 This lecture video, presented by Southwest Tech mathematics instructor Helen Mar Adams, corresponds to material in; Occupational Math-Technical, by Peter C. Esser, published by Lulu.com and Applied Math, by Peter C. Esser also by Lulu.com.
C2020-013 – IBM Exam SPSS Modeler Data Test Mining Questions
 
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For more information on IBM Practice Test Questions Please Visit: https://www.Pass-Guaranteed.com/C2020-013.htm What am I going to be tested for? The IBM C2020-013 exam tests your knowledge, skills, and abilities necessary to perform installation, configuration, administration and problem determination of data mining and the fundamentals of using IBM SPSS Modeler, and demonstrates the features and functions of this product to the end user. Which are some of the topics of the C2020-013 Modeler exam? C2020-013 Test Topic 1: Business Test Understanding Questions (Exam Coverage 8%) C2020-013 Test Topic 2: General Operations in Modeler Questions (Exam Coverage 20%) C2020-013 Test Topic 3: Data Test Understanding Questions (Exam Coverage 20%) C2020-013 Test Topic 4: Data Preparation Questions (Exam Coverage 28%) C2020-013 Test Topic 5: Modeling Test Questions (Exam Coverage 16%) C2020-013 Test Topic 6: Deployment Questions (Exam Coverage 8%) Who can attend to the IBM SPSS Modeler Data Mining for Business Partners v2 test? The IBM C2020-013 SPSS Modeler Data Mining for Business Partners (C2020-013) exam is designed mainly for Business Partners with a beginning knowledge of IBM SPSS Modeler version 14 or higher working and use the IBM SPSS Modeler product to perform data mining activities including data preparation, data understanding, and modeling. Can you give me some in-depth information on the C2020-013 exam topics? • Review of the C2020-013 CRISP methodology • Building streams • Running C2020-013 streams • Extent of missing data • Outliers • Automated C2020-013 data preparation • Auto classifier • Exporting C2020-013 model results What’s the C2020-013 passing score and duration? The duration of this exam is 60 minutes (25 questions) and the minimum passing score is 64%
Views: 155 Jacqueline Hornbeck
Interpretable Machine Learning Meetup
 
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This meetup was recorded in New York City on September 10th, 2018. Slides from the meetup can be found here: https://github.com/jphall663/jsm_2018_slides/blob/master/main.pdf Description: The good news is building fair, accountable, and transparent machine learning systems is possible. The bad news is it’s harder than many blogs and software package docs would have you believe. The truth is nearly all interpretable machine learning techniques generate approximate explanations, that the fields of eXplainable AI (XAI) and Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) are very new, and that few best practices have been widely agreed upon. This combination can lead to some ugly outcomes! This talk aims to make your interpretable machine learning project a success by describing fundamental technical challenges you will face in building an interpretable machine learning system, defining the real-world value proposition of approximate explanations for exact models, and then outlining the following viable techniques for debugging, explaining, and testing machine learning models Speaker's Bio: Patrick Hall is a senior director for data science products at H2o.ai where he focuses mainly on model interpretability. Patrick is also currently an adjunct professor in the Department of Decision Sciences at George Washington University, where he teaches graduate classes in data mining and machine learning. Prior to joining H2o.ai, Patrick held global customer facing roles and R & D research roles at SAS Institute. He holds multiple patents in automated market segmentation using clustering and deep neural networks. Patrick was the 11th person worldwide to become a Cloudera certified data scientist. He studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University.
Views: 803 H2O.ai
[Testpassport] IBM SPSS Statistics Level 1 v2 C2090-011 exam dumps
 
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Testpassport released real IBM Certified Specialist C2090-011 Exam Dumps and C2090-011 exam questions for IBM SPSS Statistics Level 1 v2 certification test. We guarantee the followings: 1. All questions are latest and updated questions. 2. Pass C2090-011 exam at your first attempt----Guaranteed! 3. Once fail, Full Refund in 24 hours after getting your report. 4. Subscribe us, $20 voucher will be sent to your email. Direct to Testpassport link http://www.testpassport.com/IBM-Certified-Specialist/C2090-011.asp Testpassport contact email: [email protected] C2090-011 exam questions|C2090-011 exam dumps|C2090-011 questions and answers|C2090-011 test|IBM SPSS Statistics Level 1 v2|C2090-011 practice test|IBM Certified Specialist
Views: 470 Testpassport
How to integrate predictive intelligence into your CRM
 
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Speakers: Amanda Kahlow (6Sense), Scott Broomfield (Xactly) Moderator: Maribel Lopez (Lopez Research) Predictive analytics is adding a new layer of “intelligence" on top of CRM software, which marketing and sales teams rely on to guide their sales efforts. For example, predictive analytics can enable a sales teams to become much more efficient by showing them who to target and what sales efforts work, and which ones don’t, based on past patterns. CRM giants are quickly realizing this, evidenced by Salesforce’s recent purchase of RelateIQ. By transforming "dumb" software into predictive ones, companies can focus their marketing on the right buyers and increase their marketing-qualified-leads to sales-qualified-leads converse to drive substantial revenue growth. Xactly CMO Scott Broomfield talks about how his company integrated predictive analytics tool to achieve such growth.
Views: 153 Mateo Fowler
C2020-012 IBM SPSS Modeler Data Analysis for Business Partners v2
 
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C2020-012 IBM SPSS Modeler Data Analysis for Business Partners v2 http://free-online-exams.com/Pages/Exams/ExamRequest.aspx?vendor=ibm Fill the form in the link above and we will contact you instantly.
Views: 281 Exam Passer
Bhambhri: Data Analysts Will Be Our Leaders In The Future!
 
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I wouldn't say that data scientist is the job of the future. I mean, it's obviously there today, where we need people and champions in organizations who are really wanting to gain new knowledge from data. So that means they have to be motivated to embrace all types of data, all kinds of data that are available to them. Or if they feel that they need data that is not available to them, but is an important part of gaining this knowledge, then they want to be championing gaining that data as well. So the way I see a data scientist is that they want to be able to get their arms around all of this data, and really use the right tools and technologies to explore and discover the patterns in this data. And as they see these patterns, that means they are gaining some new insights or new understanding. Then they have to champion that and tell that story within their enterprise to their IT group, to their business users, so that these new data sources are being embraced by the IT in building the data platform. Then the business users realize they should not just be making decisions based on the reports that they have been getting for maybe the last however many years, but that there is new information available, and they want to be making decisions based on this new information. So they have to empower these data scientists to break the status quo, to function as these change agents, and really help expand the data platform, make sure that IT is moving quickly to build a data platform that is all-encompassing, to break the silos in the enterprise. So that whatever new trends they are discovering, what patterns they are uncovering, they improve their predictive models, so that they are obviously able to predict better and more meaningful business outcomes. So I think it's certainly the need of the hour. Will we see more of these in the future? Yes, because they will be the leaders. There are always the leaders and the followers, so the companies that will encourage or help people grow into these roles, whether it is from within the company or hired from the outside, if they institute a data scientist or a team of data scientists, who will want to gain new knowledge and will want to mine all of this data, uncover the patterns, see the patterns in this data, they will certainly be leading their other players in that space. They will definitely gain an advantage over their competitors. And then of course there are always the followers. As the followers see how the leaders have become more of leaders, I think they will also get motivated to employ more of this kind of people. So when we say the demand is going to increase, that is how the demand is going to increase. But it's not like every company needs thousands of data scientists. You need a set of individuals, obviously based on the size of the company etcetera, who are the change agents and who will really help bridge the gap between IT and business users. IT in terms of building the right data platform, and business users, too: Where they can show them what the new patterns are that they are uncovering, what the new kinds of information are that they have gleaned from this. So part of the role is the storytelling, the education within the company.
Data Analysis with Python : Exercise – Titanic Survivor Analysis | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2qyTs1d]. This video introduces the Titanic disaster data set and discusses some exploratory analysis on the data. The aim of this video is to recap what you learned so far on a real data set, as well as show-case some data visualization examples. • Download the data set and understand the data structure • Extract some summary statistics from the data set • Visualize the data and find correlations between variables For the latest Application development video tutorials, please visit http://bit.ly/1VACBzh Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 35851 Packt Video
orange data mining برنامج اورنج داتا ماينيك - التنقيب عن البيانات
 
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الحلقة الاولى .. مقدمة عن برنامج اورنج للتنقيب عن البيانات يمكن استخدام البرنامج بصورة مجانية من خلال تحمليه من موقع الشركة https://orange.biolab.si/
Views: 614 h technology