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Exploratory Data Analysis
 
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An introduction to exploratory data analysis that includes discussion of descriptive statistics, graphs, outliers, and robust statistics.
Views: 35072 Prof. Patrick Meyer
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: 77794 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: 9381 Analytics University
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: 6377 عتاد صلب
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: 22710 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
Statistical Prediction Rules (SPRs) vs. Clinical experts
 
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https://www.psych.umn.edu/faculty/grove/096clinicalversusmechanicalprediction.pdf An older, but to me, illuminating study on how statistical prediction rules beat out the experts most of the time!
Views: 638 DailyData
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: 19227 MATLAB
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: 270295 ACADGILD
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: 256 StoneyP94
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: 17388 WekaMOOC
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: 5337 Predictive Analytics
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: 5413 WekaMOOC
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: 793739 statisticsfun
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: 73 shashwat jain
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: 3010 Packt Video
Twelve Emerging Trends in Data Analytics (part 1 of four)
 
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Original blog post: http://sctr7.com/2014/07/09/twelve-emerging-trends-in-data-analytics-part-1-of-4/ Business analytics is a practitioner movement uniting several disciplines to drive value-creating decisions from data. Central disciplines include IT / computer science, statistics, data management, decision science, and scientific research methods. Descriptive, predictive, and prescriptive approaches are often used to categorize particular methodological approaches, themselves derived from the fields of business intelligence, financial forecasting and econometrics, and operations management, respectively. While speculative in nature, the intention is to raise consciousness concerning long-term trends for the sake of practitioners, particularly for planners concerned with long-term strategy. 1. Plumbers wanted: data management overhead demands professional data mungers 2. Hardening models: increasingly complex models require tighter approaches to diagnostics and validation 3. The tunnel link: big data engineering and methodological approaches meet in the middle
Views: 361 sark7
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: 288 SANER2016 FOSE
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.
Data Science With R Tutorial | Lesson 1: Introduction to Business Analytics | Simplilearn
 
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Business analytics (BA) is the practice of iterative, methodical exploration of an organization's data, with an emphasis on statistical analysis. Business analytics is used by companies committed to data-driven decision-making. Data Science Certification Training - R Programming: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Business-Analytics-9YXojHh_ZPY&utm_medium=SC&utm_source=youtube About the course: 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. What’s the focus of this course? The Data Science with R training course has been designed to impart an in-depth knowledge of the various data analytics techniques which can be performed using R. The course is packed with real-life projects, case studies, and includes R CloudLabs for practice. Mastering R language: The course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R. Mastering advanced statistical concepts: The course also includes the various statistical concepts like linear and logistic regression, cluster analysis, and forecasting. You will also learn hypothesis testing. As a part of the course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and Internet. R CloudLab has been provided to ensure a practical and hands-on experience. Additionally, we have four more projects for further practice. 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 Prerequisites: There are no prerequisites for this course. If you are new in the field of data science, this is the best course to start with. 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: 9874 Simplilearn
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: 883 online
Calculating Mean, Standard Deviation, Frequencies in R | R Tutorial 2.8| MarinStatsLectures
 
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Calculating Mean, Standard Deviation, Frequencies in R (Descriptive Statistics in R): How to produce numeric summaries for both categorical and numerical variables in R. Here is the Free Practice Dataset (LungCapData): (https://bit.ly/2rOfgEJ); Standard Deviation Explained (https://youtu.be/nlm9gfso4mw) ; For more Statistics and R Programming Tutorials: (https://goo.gl/4vDQzT) ►► Like to support us? You can Donate (https://bit.ly/2CWxnP2), Share our Videos, Leave us a Comment and Give us a Thumbs up! Either way We Thank You! In this R video tutorial, we will learn how to produce numeric summaries for both categorical and numerical variables in R. This tutorial explains: • How to create frequency tables in R • How to create contingency tables in R • How to calculate mean, median, variance, and standard deviation in R • How to calculate Pearson’s correlation in R • How to calculate Spearman’s correlation with R • How to calculate the minimum, maximum and range in R, • How to calculate specific quantiles or percentiles in R • How to calculate the covariance in R Table of Content: 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" command 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 These video tutorials are useful for anyone interested in learning data science and statistics with R programming language using RStudio. ► ► Watch More: ► Intro to Statistics Course: https://bit.ly/2SQOxDH ►Data Science with R https://bit.ly/1A1Pixc ►Getting Started with R (Series 1): https://bit.ly/2PkTneg ►Graphs and Descriptive Statistics in R (Series 2): https://bit.ly/2PkTneg ►Probability distributions in R (Series 3): https://bit.ly/2AT3wpI ►Bivariate analysis in R (Series 4): https://bit.ly/2SXvcRi ►Linear Regression in R (Series 5): https://bit.ly/1iytAtm ►ANOVA Concept and with R https://bit.ly/2zBwjgL ►Hypothesis Testing: https://bit.ly/2Ff3J9e ►Linear Regression Concept and with R Lectures https://bit.ly/2z8fXg1 Follow MarinStatsLectures Subscribe: https://goo.gl/4vDQzT website: https://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 and Creative Manager: Ladan Hamadani (B.Sc., BA., MPH) These videos are created by #marinstatslectures to support some courses at The University of British Columbia (UBC) (#IntroductoryStatistics and #RVideoTutorials for Health Science Research), although we make all videos available to the everyone everywhere for free. Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!
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: 318039 Thales Sehn Körting
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.
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
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: 9569 Hamed Hasheminia
#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
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: 44398 SAS Software
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
The Connected Vehicle: How Analytics Drives Telematics Value
 
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http://www.sas.com/automotive Learn how SAS' Internet of Things technology is turning mundane telematics trouble codes into real value in the automotive and trucking industries. When everything is connected, we need answers, we need the Analytics of Things. SAS AUTOMOTIVE SOLUTIONS Drive better decisions with the world’s best analytics. SAS has automotive solutions for: * Sales & Marketing * Product & Process Quality * Aftermarket Service * Credit & Finance * Supply & Demand Planning * And more... LEARN MORE ABOUT SAS SOLUTIONS FOR AUTOMOTIVE http://www.sas.com/en_us/industry/automotive.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: 27622 SAS Software
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: 10047 ZPZ Education
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: 690 ElevateHealth
SAS Predictive Modelling Demo
 
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Pls visit us on www.sashub.com / call us @+91 9985052355 for any further details regarding online training.
Views: 9395 Mahesh kumar reddy
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: 279 Exam Passer
IBM SPSS Modeler Demo
 
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A model is a set of rules, formulas, or equations that can be used to predict an outcome based on a set of input fields or variables. For example, a financial institution might use a model to predict whether loan applicants are likely to be good or bad risks, based on information that is already known about past applicants. The ability to predict an outcome is the central goal of predictive analytics, and understanding the modeling process is the key to using IBM® SPSS® Modeler. © Copyright IBM Corporation 1994, 2012.
Views: 55645 analysisexpress
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: 44 Guan Felix
Multiple Regression - Dummy variables and interactions - example in Excel
 
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In this video, I present an example of a multiple regression analysis of website visit duration data using both quantitative and qualitative variables. Variables used include gender, browser, mobile/non-mobile, and years of education. Gender and mobile each require a single dummy variable, while browser requires several dummy variables. I also present models that include interactions between the dummy variables and years of education to analyze intercept effects, slope effects, and fully interacted models. In short, I cover: - multiple category qualitative variables - dummy variables - intercept effects - slope effects - dummy interactions I hope you find it useful! Please let me know if you have any questions! --Dr. D.
Views: 248946 Jason Delaney
L'analyse statistique des clientèles : Cas Piton
 
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cours gestion muc https://www.monbtsmuc.com www.exo-video.com
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.
C2020-011 - IBM SPSS Statistics Level 1 v2
 
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For more information regarding the IBM C2020-011 exam:http://www.exam1pass.com/C2020-011-exam.html C2020-011 - IBM SPSS Statistics Level 1 v2
Views: 150 Fggs Sg
2017 New C2090-011 Exam Questions IBM C2090-011 SPSS Statistics Level 1 v2 Practice Test
 
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Views: 67 Killtest
Two-step Cluster Analysis in SPSS
 
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This is a two-step cluster analysis using SPSS. I do this to demonstrate how to explore profiles of responses. These profiles can then be used as a moderator in SEM analyses.
Views: 161018 James Gaskin
Profiling Segments with SAS Enterprise Miner
 
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View this video tip from Catherine Truxillo to learn how to perform a cluster analysis using the segment profile node in SAS® Enterprise Miner™. This material is related to the type of content covered in the SAS training course, "Advanced Analytics for the Modern Business Analyst." To learn more, visit: https://support.sas.com/edu/schedules.html?ctry=us&id=1076 To view another video tip on using the segment profile node, visit http://www.youtube.com/watch?v=-1-GIe8_GgU
Views: 40965 SAS Software
SAS TextMining Introduction
 
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Introduction to Text Mining
Views: 1612 Dothang Truong
How to run cluster analysis in Excel
 
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A step by step guide of how to run k-means clustering in Excel. Please note that more information on cluster analysis and a free Excel template is available at http://www.clusteranalysis4marketing.com
Views: 99660 MktgStudyGuide