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Machine Learning Tutorial 2 - Intro to Predictive Data Analytics
 
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Best Machine Learning book: https://amzn.to/2MilWH0 (Fundamentals Of Machine Learning for Predictive Data Analytics). Machine Learning and Predictive Analytics. #MachineLearning Intro to Predictive Analytics is the second video in this machine learning course. This video explains how machine learning algorithms are used in the field of data analytics to create models of reality. This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Enjoy! Check out the entire series here: https://www.youtube.com/playlist?list=PL_c9BZzLwBRIPaKlO5huuWQdcM3iYqF2w&playnext=1 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Support me! http://www.patreon.com/calebcurry Subscribe to my newsletter: http://bit.ly/JoinCCNewsletter Donate!: http://bit.ly/DonateCTVM2. ~~~~~~~~~~~~~~~Additional Links~~~~~~~~~~~~~~~ More content: http://CalebCurry.com Facebook: http://www.facebook.com/CalebTheVideoMaker Google+: https://plus.google.com/+CalebTheVideoMaker2 Twitter: http://twitter.com/calebCurry Amazing Web Hosting - http://bit.ly/ccbluehost (The best web hosting for a cheap price!)
Views: 9556 Caleb Curry
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 Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. 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: 59003 edureka!
KDD ( knowledge data discovery )  in data mining in hindi
 
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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://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [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: 65372 Last moment tuitions
Mining Unstructured Data in Software Repositories
 
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The amount of unstructured data available to software engineering researchers in versioning systems, issue trackers, achieved communications, etc is continuously growing over time. The mining of such data represents an unprecedented opportunity for researchers to investigate new research questions and to build a new generation of recommender systems supporting development and maintenance activities. This talk describes works on the application of Mining Unstructured Data (MUD) in software engineering. The talk briefly reviews the types of unstructured data available to researchers providing pointers to basic mining techniques to exploit them. Then, an overview of the existing applications of MUD in software engineering is provided with a specific focus on textual data present in software repositories and code components. The talk also discusses perils the "miner" should avoid while mining unstructured data and lists possible future trends for the field.
Views: 230 SANER2016 FOSE
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
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: 4831 WekaMOOC
Intro to Audit Analytics Lecture 3: Descriptive statistics
 
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To receive additional updates regarding our library please subscribe to our mailing list using the following link: http://rbx.business.rutgers.edu/subscribe.html
Calculating Mean, Standard Deviation, Frequencies in R | R Tutorial 2.7| MarinStatsLectures
 
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Calculating Mean, Standard Deviation, Frequencies in R (Descriptive Statistics in R); For more Statistics and R Programming Tutorials: https://goo.gl/4vDQzT - Standard Deviation Explained https://youtu.be/nlm9gfso4mw Learn how to produce numeric summaries for both categorical and numerical variables in R. This tutorial explains how to produce frequency and contingency tables and to calculate mean, median, variance, standard deviation and many more operations using commands such as "table", "mean", "median", "var", "sd", "summary", etc. You can access and download the dataset here: https://statslectures.com/r-stats-datasets Here is a quick overview of the topics addressed in this video: 0:00:36 how to access the Help menu in R for any of the commands 0:00:52 how to summarize a categorical variable 0:00:58 how to produce a "frequency table" in R to summarize a categorical variable using "table" command 0:01:10 how to express the "frequency table" in R using proportion 0:01:18 how to ask R for the number of observations using the "length" commandhttps://youtu.be/nlm9gfso4mw 0:01:51 how to produce a "two-way table" or "contingency table" in R to summarize a categorical variable using "table" command 0:02:09 how to calculate the mean and trimmed mean in R to summarize a numeric variable using "mean" command and "trim" argument 0:02:37 how to calculate the "median" in R to summarize a numeric variable using the "median" command 0:02:45 how to calculate the variance in R to summarize a numeric variable using "var" command 0:02:54 how to calculate the "standard deviation" in R to summarize a numeric variable using the "sd" command or "sqrt" command (taking square root of variance) 0:03:23 how to calculate the minimum, maximum and range in R to summarize a numeric variable using "min", "max" and "range" command 0:03:45 how to calculate specific quantiles or percentiles in R using the "quantile" command and "probs" argument 0:04:53 how to calculate "Pearson's correlation" in R to summarize a numerical variable using the "cor" command 0:05:10 how to calculate "Spearman's correlation" in R to summarize a numerical variable using the "cor" command and "method" argument 0:05:22 how to calculate the covariance in R using the "cov" or "var" command 0:05:43 how to summarize all data (both numeric and categorical) in R using the "summary" command ►►Make sure to check out the Statistics tutorial on Standard Deviation and what it actually measures here: https://youtu.be/nlm9gfso4mw ♠︎♣︎♥︎♦︎To learn more: Subscribe: https://goo.gl/4vDQzT website: http://statslectures.com Facebook:https://goo.gl/qYQavS Twitter:https://goo.gl/393AQG Instagram: https://goo.gl/fdPiDn Our Team: Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC. Producer: Ladan Hamadani (B.Sc., BA., MPH) These #RTutorial are created by #marinstatslectures to support the statistics course (#SPPH400) at The University of British Columbia(UBC) although we make all videos available to the public for free.
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: 5038 Predictive Analytics
Introduction to R Programming Part 1
 
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***Part 1 starts at 1 min and 50 seconds with about 28 minutes of tech support to get the program installed. Formal lecture begins at 30 minutes and 30 seconds. Instructor: David Ruau, PhD - http://www.stanford.edu/people/druau By the end of parts I and II, participants will be able to: · Interact with R using commands passed through the console · Import and export data in various formats and transform those data in R · Make statistical graphics plots (and more) · Write small scripts and functions using the R language. For a complete description of the classes & Installing "R" and other packages prior to the class: please see instructions for "Introduction to R programming I & II course" [pdf] http://elane/laneconnex/public/media/documents/R_Workshops_Description_And_Instructions.pdf
Views: 234068 Lane Medical Library
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: 227338 ACADGILD
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: 41107 SAS Software
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: 2005051 Khan Academy
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: 21077 Packt Video
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: 238 StoneyP94
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: 11823 Simplilearn
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
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.
Views: 344 Datawatch
Data Mining Functionalities || Data Characterization & Data Discrimination || Lecture In Urdu/Hindi
 
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What are data mining functionalities? Data characterization and data Discrimination
Views: 6534 Focus Group
Arijit Sengupta BeyondCore Lecture 04/16/16 Data Science Speakers
 
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Arjit Sengupta is CEO at BeyondCore. Held at the Haas School of Business, University of California, Berkeley, the Data Science & Strategy Lecture Series examines the evolving role of "big data" and analytics in managerial decision-making. In this playlist, lecture series host Prof. Greg La Blanc interviews industry executives and practitioners on key topics in data science, including data mining, machine learning, visualization, advanced statistics and more. For more information please visit: http://businessinnovation.berkeley.edu/data-science-strategy/lecture-series/.
Views: 1117 Berkeley Haas
Getting Started with SAS Enterprise Miner: Exploring Input Data and Replacing Missing Values
 
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http://support.sas.com/software/products/miner/index.html Chip Robie of SAS presents the second in a series of six Getting Started with SAS Enterprise Miner 13.2 videos. This second video focuses on exploring input data and replacing missing values 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 analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make 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: 30871 SAS Software
SPSS for questionnaire analysis:  Correlation analysis
 
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Basic introduction to correlation - how to interpret correlation coefficient, and how to chose the right type of correlation measure for your situation. 0:00 Introduction to bivariate correlation 2:20 Why does SPSS provide more than one measure for correlation? 3:26 Example 1: Pearson correlation 7:54 Example 2: Spearman (rhp), Kendall's tau-b 15:26 Example 3: correlation matrix I could make this video real quick and just show you Pearson's correlation coefficient, which is commonly taught in a introductory stats course. However, the Pearson's correlation IS NOT always applicable as it depends on whether your data satisfies certain conditions. So to do correlation analysis, it's better I bring together all the types of measures of correlation given in SPSS in one presentation. Watch correlation and regression: https://youtu.be/tDxeR6JT6nM ------------------------- Correlation of 2 rodinal variables, non monotonic This question has been asked a few times, so I will make a video on it. But to answer your question, monotonic means in one direction. I suggest you plot the 2 variables and you'll see whether or not there is a monotonic relationship there. If there is a little non-monotonic relationship then Spearman is still fine. Remember we are measuring the TENDENCY for the 2 variables to move up-up/down-down/up-down together. If you have strong non-monotonic shape in the plot ie. a curve then you could abandon correlation and do a chi-square test of association - this is the "correlation" for qualitative variables. And since your 2 variables are ordinal, they are qualitative. Good luck
Views: 503433 Phil Chan
Creating a Decision Tree with IBM SPSS Modeler
 
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This clip demonstrates the use of IBM SPSS Modeler and how to create a decision tree. Such a tool can be a useful business practice and is used in predictive analytics. Please visit www.newcomp.com for more information.
Views: 99871 GoNewcomp
Introduction to R Programming Part 2
 
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Formal lecture starts at 30 min and 50 seconds. Instructor: David Ruau, PhD - http://www.stanford.edu/people/druau By the end of parts I and II, participants will be able to: · Interact with R using commands passed through the console · Import and export data in various formats and transform those data in R · Make statistical graphics plots (and more) · Write small scripts and functions using the R language. For a complete description of the classes & Installing "R" and other packages prior to the class: please see instructions for "Introduction to R programming I & II course" [pdf] http://elane/laneconnex/public/media/documents/R_Workshops_Description_And_Instructions.pdf
Views: 29358 Lane Medical Library
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.
Text Analytics - (Natural Language Processing) Using RPA
 
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This video is all about doing text analytics (NLP) using RPA. Connect with me in Linkedin: https://www.linkedin.com/in/vishalraghav10/ Connect with me in FaceBook: https://www.facebook.com/vishal.raghav1 Connect with me in Instagram: https://www.instagram.com/lash_raghav/ Connect with me in Quora: https://www.quora.com/profile/Vishal-Raghav-6
Views: 548 Vishal Raghav
A Data Analyst   [hindi]&[Engish]
 
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A Data Analyst wabsite: http://mothercomputers.weebly.com/
Decision Tree Classification in R
 
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This video covers how you can can use rpart library in R to build decision trees for classification. The video provides a brief overview of decision tree and the shows a demo of using rpart to create decision tree models, visualise it and predict using the decision tree model
Views: 71953 Melvin L
Data-Driven Analysis of Indian Political Processes
 
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See more at https://www.microsoft.com/en-us/research/video/data-driven-analysis-of-indian-political-processes/
Views: 981 Microsoft Research
R Programming Tutorials for Beginners  easy to learn
 
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Visit our google + page https://plus.google.com/u/1/114326353856656673559/posts To see more stuff Subscribe to our channel. R is a programming language and software surroundings for statistical analysis, pictures representation and reporting. R become created by means of ross ihaka and robert gentleman on the college of auckland, new zealand, and is presently evolved through the r improvement center team. R is freely available underneath the gnu trendy public license, and pre-compiled binary versions are supplied for numerous working systems like linux, windows and mac. This programming language turned into named r, based on the first letter of first name of the 2 r authors (robert gentleman and ross ihaka), and in part a play on the call of the bell labs language s. This educational is designed for software programmers, statisticians and information miners who're searching forward for growing statistical software program using r programming. If you are attempting to apprehend the r programming language as a newbie, this tutorial will come up with enough know-how on almost all the standards of the language from where you can take yourself to better tiers of understanding. Earlier than intending with this tutorial, you must have a basic knowledge of computer programming terminologies. A fundamental information of any of the programming languages will assist you in knowledge the r programming standards and flow rapid at the getting to know song. r programming tutorial, r programming tutorial pdf, r programming tutorial free, r programming tutorial for beginners pdf, r (programming language), r programming tutorial - 1, r programming tutorial online, data mining (software genre), data analysis (media genre), r programming tutorial for beginners, free r programming tutorials -~-~~-~~~-~~-~- Please watch: "7 Learn R programming Language, R operators,Logical, Assignment and Miscellaneous Operators" https://www.youtube.com/watch?v=ReYc5QiOYEk -~-~~-~~~-~~-~-
Views: 844 online
What is Data Processing & Data Analysis & its Methods ? Urdu / Hindi
 
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This Video Give The Basic Concept of What is Data Processing (Data Analysis) And its Methods ? (Urdu / Hindi) ZPZ Education Channel Link: www.youtube.com/channel/UCwFzeQDf9cGm_ZeTXV_t5SA
Views: 5238 ZPZ Education
Google Analytics Profile Comparison R script
 
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Demo of an R script I have written to quickly compare a Classic GA profile to a Universal Analytics profile. Can be used to compare any single metric-dimension combination line by line between two profiles, so could also be used for comparing a new profile configuration with a baseline or checking a Google Tag Manager based implementation before migrating to it.
Views: 325 Michael Hayes
C2090-930 – IBM Exam SPSS Test Modeler Professional Questions
 
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For more information on IBM C2090-930 Practice Test Questions Please Visit: https://www.Pass-Guaranteed.com/C2090-930.htm What am I going to be tested for? The C2090-930 IBM SPSS Modeler Professional v3 candidate’s knowledge will be tested in areas such as: analytical solutions, the understanding of C2090-930 IBM SPSS Modeler capabilities, knowledge of the IBM SPSS Modeler data model, also if he can apply consistent methodologies to every engagement and develop SPSS predictive test models. Which are some of the topics of the C2090-930 Professional exam? C2090-930 Test Topic 1: SPSS Modeler functionalities Questions (Exam Coverage 16%) C2090-930 Test Topic 2: CRISP-DM process Questions (Exam Coverage 23%) C2090-930 Test Topic 3 Modeling Questions (Exam Coverage 21%) C2090-930 Test Topic 4: Deployment test Questions (Exam Coverage 22%) C2090-930 Test Topic 5: Tools for exporting data Questions (Exam Coverage 18%) Who can attend IBM SPSS Modeler Professional v3 test? The C2090-930 SPSS Modeler Professional v3 exam has been designed for technical professional who wish to become an IBM Certified Specialist - SPSS Modeler Professional. It’s assumed that the candidate has knowledge of C2090-930 Database and ODBC test concepts, basic proficiency in statistical concepts and knowledge of basic exam computer programming. Can you give me some in-depth information on the C2090-930 exam topics? • Describe appropriate nodes for summary statistics, distributions, and visualizations • Describe data quality issues (for example, exam outliers and missing data) • Describe classification models (including GLM and test regression) • Describe methods for refining data (for example, Select node, Filter C2090-930 node and Aggregate node) • Demonstrate how to combine C2090-930 test models using the Ensemble node • Demonstrate how to interpret SPSS Modeler test results • Describe how to use model C2090-930 nugget interfaces • Describe segmentation models and C2090-930 auto modeling nodes What’s the C2090-930 passing score and duration? The duration of this exam is 90 minutes (60 questions) and a minimum passing score of 67%.
Views: 161 Tyrell Carte
Systat Data Handling
 
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Overview of the Systat Data Mining and Predictive Analytics System
Views: 6435 Predictive Analytics
S1 Introduction
 
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GCE Statistics 1 An Introduction to the Specification Statistics: Collecting Data Representing Data (Stem Leaf Diagrams, Histograms, Box Plots) Summarizing Data (Central Tendencies - mean, mode, median & quartiles, Dispersion - Range,Inter-Quartile Range, Standard Deviation) Predicting Data (Correlation, Regression) Probability Concepts: Basic Rules (Set Notations, Venn Diagrams, Tree Diagrams) Random Variables & Probability Distributions Normal Distribution
Views: 1147 TheGreenReviews
R-Session 10 - Statistical Learning - Unsupervised Learning
 
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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: 3223 Hamed Hasheminia
Reporting & Analytics Made Easy with Dynamics GP SmartInsight
 
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Dynamics GP SmartInsight is a collection of reports allowing you to consolidate information from Dynamics GP and multiple data sources, presenting them in a single easy to view dashboard using QlikView, the chosen BI tool of over 31,000 companies. Webinar presented by Konrad Wielgosz, BDM at Professional Advantage and Graham Smith, Dynamics GP and QlikView Consultant at Professional Advantage. For more information: Call 1800 126 499 Email [email protected] Visit http://www.pa.com.au/contact/
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: 9098 Hamed Hasheminia
Analysis ToolPak in Excel and VBA Case Solution & Analysis- TheCaseSolutions.com
 
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https://www.thecasesolutions.com/ This case is about Analysis ToolPak in Excel and VBA Get your Analysis ToolPak in Excel and VBA Case Solution at TheCaseSolutions.com TheCaseSolutions.com is the number 1 destination for getting the case studies analyzed. http://www.thecasesolutions.com/analysis-toolpak-in-excel-and-vba-35550
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: 277 Exam Passer
MOOC Clinical Epidemiology: Prognostic Research, Rick Grobbee MD PhD - part 4
 
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This Massive Open Online Course (MOOC) on Clinical Epidemiology is offered by Utrecht University in close cooperation with Elevate Health.
Views: 636 ElevateHealth
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.
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: 43 Guan Felix
Free YouTube Channel Stats & Rankings | Video Amigo Analytics
 
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Check out your or anyone else's YouTube stats from the Channel Directory now: http://www.videoamigo.com/ Touchstorm's Video Amigo brings you the Channel Directory, a tool for researching YouTube channel statistics and rankings. Here brands and creators can find sponsors, collaborators and partners. We've created unique metrics and combine those with YouTube Analytics' API data to provide never before seen channel growth analysis and marketing segmentation...and it's free. TRANSCRIPT Hey creators! Wouldn’t it be great if there was a place where brands and sponsors could find you? Where you could find collaborators or where fellow creators could see what you’re great at...even if you don’t have a million subscribers yet? Something like the Yellow Pages meets Angie’s List, with some Linked In and maybe even a little bit of Match.com thrown in? Well now it’s here. Introducing the Channel Directory. One of many video marketing tools from Touchstorm, on VideoAmigo.com. Who’s Touchstorm? We help brands do YouTube right. Our clients are some of the biggest brands in the world. They asked, why am i searching YouTube trying to find my spokespeople? Why is YouTube preferred the best way I have of knowing who to advertise on? Where’s all that data I need to make decisions? There’s got to be a better way! That’s why we made a Channel Directory. And it’s free! Here’s how it works. Any YouTube creator can have a page in the directory. In fact, there’s a good chance your channel is already in there. Go check yourself out. Not in there? Easy to fix. Every channel page has some good basic information - a channel’s top videos, some vital stats....but that’s where the standard YouTube API information that everyone else has STOPS. The problem with the standard data is that it’s amazingly devoid of insight. The key to all of the missing insights is the ability to compare knitters to knitters, beers to beers, and comedians to comedians to see how they truly stack up. We needed to make YouTube smaller somehow. And we did. By classifying every channel and video into Themes, Conversations, and Topics. See yourself in comparison to your peers. Are you above or below average? What can you improve to run a stronger channel? This graph lets you see how a channel's monthly view growth and subscriber growth stack up against the YouTube average. This Content Cadence module helps you understand when, how often, and on what schedule a channel is releasing content. This Spider Chart compares a channel to the average channel in any of the TVi Categories it’s a part of, based on a dozen different metrics that are important for determining channel health. With just a few clicks, you can change who you’re compared against. And that’s just the point. Brands can make those comparisons too. And that’s what they need to give YOU money. Don’t worry… We’ve included enhanced control options for channel owners to claim their channel so you can put your best face forward to attract potential collaborators, partners and sponsors. And more reporting modules will be added as the landscape evolves. The Channel Directory is filled with in-depth performance details not found anywhere else, and it’s available to everyone because we know how vital it is for creators and brands to find each other. For Creators, the Channel Directory is your place to shine. What are you waiting for? Go claim your channel. Check out your friends’ channels. And get found.
Views: 553 Touchstorm
Analyse de données en épidémiologie avec le logiciel R MOOCSciNum-S3
 
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MOOCSCiNum - Séance 3 - Screencast de Kevin Jean Licence CC BY SA
Views: 4352 Mooc SciNum