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Predicting Instructor Performance Using Data Mining Techniques in Higher Education
 
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Predicting Instructor Performance Using Data Mining Techniques in Higher Education -- Data mining applications are becoming a more common tool in understanding and solving educational and administrative problems in higher education. In general, research in educational mining focuses on modeling student's performance instead of instructors' performance. One of the common tools to evaluate instructors' performance is the course evaluation questionnaire to evaluate based on students' perception. In this paper, four different classication techniquesdecision tree algorithms, support vector machines, articial neural networks, and discriminant analysisare used to build classier models. Their performances are compared over a data set composed of responses of students to a real course evaluation questionnaire using accuracy, precision, recall, and specicity performance metrics. Although all the classier models show comparably high classication performances, C5.0 classier is the best with respect to accuracy, precision, and specicity. In addition, an analysis of the variable importance for each classier model is done. Accordingly, it is shown that many of the questions in the course evaluation questionnaire appear to be irrelevant. Furthermore, the analysis shows that the instructors' success based on the students' perception mainly depends on the interest of the students in the course. The ndings of this paper indicate the effectiveness and expressiveness of data mining models in course evaluation and higher education mining. Moreover, these ndings may be used to improve the measurement instruments. Articial neural networks, classication algorithms, decision trees, linear discriminant analysis, performance evaluation, support vector machines. -- For More Details Contact Us -- S.Venkatesan Arihant Techno Solutions Pudukkottai www.arihants.com Mobile: +91 75984 92789
Educational Data Mining (EDM): Turning Big Data into Big Gains for Students
 
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From making travel plans, to online purchases, to watching videos, each day we generate vast amounts of data that contribute to the world of big data. We have already seen big data play a significant role in areas like marketing and science. Now, education has joined the big data movement. In the past, education data was sparse and disparate. Collected across individual gradebooks and housed within multiple platforms, data was inaccessible, laborious, and difficult to analyze. Thankfully, this has changed. Now, educators and researchers can access incredibly rich and meaningful logs about student learning behavior on educational software, and by employing EDM (education data mining), discover a great deal about how students learn. By connecting this powerful data and asking the right questions, there is potential to change the future of education. Learn about the ability to leverage meaningful data with EDM and learning analytics, and find out how to turn big data into big gains for students. Attend this webinar to discover how: Learning analytics and EDM are already transforming education EDM advancements can assess students’ knowledge as they are learning Specific EDM methods are proving useful in understanding and predicting which students are likely to succeed in 21st century careers Learning analytics can provide insight into the effectiveness of educational technology programs and the conditions under which these programs have the greatest return on learning
Views: 1025 eschoolnews
Educational Data Mining: Predict the Future, Change the Future
 
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Teachers College is proud to introduce the 2012-13 Julius and Rosa Sachs Distinguished Lecturer Professor Ryan Baker, Columbia University. Ryan Shaun Joazeiro de Baker is Visiting Associate Professor in the Department of Human Development. He earned his Ph.D. in Human-Computer Interaction from Carnegie Mellon University, and was a post-doctoral fellow in the Learning Sciences at the University of Nottingham. He earned his Bachelor's Degree (Sc.B.) in Computer Science from Brown University. Dr. Baker has been Assistant Professor of Psychology and the Learning Sciences at Worcester Polytechnic Institute. He previously served as the first Technical Director of the Pittsburgh Science of Learning Center DataShop, the largest public repository for data on the interaction between learners and educational software. He is currently serving as the founding President of the International Educational Data Mining Society, and as Associate Editor of the Journal of Educational Data Mining. His research combines educational data mining, learning analytics and quantitative field observation methods in order to better understand how students respond to educational software, and how these responses impact their learning. He studies these issues within intelligent tutors, simulations, and educational games. In recent years, he and his colleagues have developed strategies to make inferences in real-time about students' motivation, meta-cognition, affect, and robust learning.
Student Performance Measure By Using Different Classification Methods Of Data Mining
 
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This video explains about the various classification methods of data mining to measure the performance of the students using the grades obtained in four semesters.
Views: 2821 Neha Choudhary
classroom visualization through the eras + educational data  mining and E-learning
 
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visualization to a teacher situation through the eras of education, in order to illustrate the impact and the importance of using educational data mining technique
Views: 70 norahalhajri
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: 64659 edureka!
Why is Educational Data Mining Important?
 
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Leading experts from the field of Educational Data Mining weigh in on why educational data mining is important. FEATURING David Lindrum (Founder & Course Designer, Soomo Learning) Piotr Mitros (Chief Scientist, edX) April Galyardt (Assistant Professor, University of Georgia College of Education) Ryan Baker (Associate Professor of Cognitive Studies, Teachers College Columbia University) Tiffany Barnes (Associate Professor of Computer Science, North Carolina State University) RECORDED AND PRODUCED BY Timothy D. Harfield
Views: 759 Timothy Harfield
Prediction of Student Results #Data Mining
 
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We used WEKA datamining s-w which yields the result in a flash.
Views: 31383 GRIETCSEPROJECTS
A Systematic Review on Educational Data Mining | Final year Projects 2016 - 2017
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 474 Clickmyproject
Data Mining in the Medical Field
 
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Video about data mining in the medical field. Made by Aditya Jariwala, Alex Truitt, Tongfei Zhang, and Yishi Xu for Purdue COM 21700 final project, Spring 2017.
Views: 2975 Aditya Jariwala
What is Educational Data Mining?
 
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Leading experts from the field of Educational Data Mining weigh in what exactly they do. FEATURING David Lindrum (Founder & Course Designer, Soomo Learning) Tiffany Barnes (Associate Professor of Computer Science, North Carolina State University) Vineet Sinha (Director Analytics Platforms, Cengage Learning) Ryan Baker (Associate Professor of Cognitive Studies, Teachers College Columbia University) April Galyardt (Assistant Professor, University of Georgia College of Education) Scott McQuiggan (Director, SAS Curriculum Pathways) RECORDED AND PRODUCED BY Timothy D. Harfield
Views: 340 Timothy Harfield
SPS2017: Educational Data Mining Software
 
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The video is giving details about research software developed using WEKA (Open source Data Mining tool) and JAVA (Programming Language). The first version is developed in 2017. Anyone having the link can download this software and directly use this software without any installation. All the instructions are given in 'README.txt' file in a downloaded zip folder. Any suggestions and questions are invited in the comment section below. Feel free to add below. Music Credits: Youtube Audio Library
Views: 79 Prabhjot Kaur
Tubes - Educational Data Mining
 
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Views: 194 Desepta Isna Ulumi
A Systematic Review on Educational Data Mining
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 54 Clickmyproject
INTRODUCTION TO DATA MINING IN HINDI
 
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Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 108997 LearnEveryone
Data Mining Techniques for Detecting Behavioral Patterns of Gifted Students in Online Learning
 
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The paper entitled "Data Mining Techniques for Detecting Behavioral Patterns of Gifted Students in Online Learning Environment (Case Study)" will be presented in the framework of the fourth edition of the international conference "The Future of Education" that will be held in Florence on 12 - 13 June 2014
Views: 236 Pixel Conferences
Detect malicious android applications with data mining techniques
 
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A diploma thesis of one of my undergraduate students. Mr. Konstantinos Ousantzopoulos. ABSTRACT The Android operating system gives access to applications based on model of permissions. In this work we use the permissions of safe and malicious applications as a data structure to excavate knowledge so that we can predict if an application from Google Play is safe or malicious using Rapidminer various data mining techniques and algorithms to get the best possible result. We will show the way data was collected and their analysis to arrive at a desired result which we will apply with an android application and a Java server. The user through a simple android application will be able to type the name of the application on Google Play which wants to check. Then the application will communicate locally with the server where the analysis and prediction through Rapidminer take place . Finally it returns to the screen of the user the prediction whether the application he searched is malicious or not.
Data Mining in Education
 
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I created this video with the YouTube Video Editor (http://www.youtube.com/editor)
Views: 688 stlgretchen
[LAK 2012] May 1: 7 - Learning Analytics and Educational Data Mining
 
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George Siemens Ryan S. J. d. Baker Learning Analytics and Educational Data Mining: Towards Communication and Collaboration
A Review of Educational Data Mining Tools & Techniques IJETL  3, 1 ,17 23
 
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A Review of Educational Data Mining Tools & Techniques
Educational Data Mining in the Service of Building Detectors of Losing interest
 
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Speaker Bio: Neil Heffernan is a professor of Computer Science and the co-director of the PhD program in Learning Sciences and Technologies.  He developed ASSISTments not only to help teachers be more effective in the classroom but also so that he could use the platform to conduct studies to improve the quality of education.  He is very passionate about educational data mining. Professor Heffernan enjoys supervising WPI students helping them create ASSISTments content and features. Several student projects have resulted in peer-reviewed publications looking at comparing different ways to optimize student learning.  Professor Heffernan's goal is to give ASSISTments to millions across the US and internationally as a free service of WPI.  The talk: During this talk, Neil talks about educational data mining and building better educational technology products.  He created ASSISTments at WPI, a product used by 50,000 last year to solve 12 million problems. Neil started ASSISTments about a decade ago, and ever sense then they have been logging student performance. In this session, Neil talks about a cool use of that data.
Views: 38 Shirin Mojarad
Student Learning Evaluation - Predicting Student Performance
 
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Predicting Instructor Performance Using Data Mining Techniques in Higher Education -- Data mining applications are becoming a more common tool in understanding and solving educational and administrative problems in higher education. Generally, research in educational mining focuses on modeling student’s performance instead of instructors’ performance. One of the common tools to evaluate instructors’ performance is the course evaluation questionnaire to evaluate based on students’ perception. In this study, four different classification techniques, –decision tree algorithms, support vector machines, artificial neural networks, and discriminant analysis– are used to build classifier models. Their performances are compared over a dataset composed of responses of students to a real course evaluation questionnaire using accuracy, precision, recall, and specificity performance metrics. Although all the classifier models show comparably high classification performances, C5.0 classifier is the best with respect to accuracy, precision, and specificity. In addition, an analysis of the variable importance for each classifier model is done. Accordingly, it is shown that many of the questions in the course evaluation questionnaire appear to be irrelevant. Furthermore, the analysis shows that the instructors’ success based on the students’ perception mainly depends on the interest of the students in the course. The findings of the study indicate the effectiveness and expressiveness of data mining models in course evaluation and higher education mining. Moreover, these findings may be used to improve measurement instruments. Artificial neural networks, classification algorithms, decision trees, linear discriminant analysis, performance evaluation, support vector machines -- For More Details Contact Us -- S.Venkatesan Arihant Techno Solutions Pudukkottai www.arihants.com Mobile: +91 75984 92789
Educational Data Mining song: Prof. Jack Mostow (CMU)
 
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At the EDM2014 banquet in London,UK
Views: 93 rohitkumarcmu
Educational Data Mining Prospects with Canvas | InstructureCon 2013
 
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Brian Kokensparger of Creighton University Educational Data Mining uses mainstream data mining methods to work with educational data to accomplish educational objectives . Canvas offers attractive features to support educational data mining efforts. This session will present three ways to collect data from the Canvas LMS, as well as some examples of how Canvas data are already being used in data mining projects. Want to get your feet wet in data mining? This session will show you how.
Views: 670 CanvasLMS
Data Mining for Educational Researchers
 
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Chris Brooks
Views: 143 LINK Lab
Data Mining  Association Rule - Basic Concepts
 
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short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
Lecture 59 — Hierarchical Clustering | Stanford University
 
14:08
. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
A Review on Mining Students’ Data for Performance Prediction  | Final Year Projects 2016 - 2017
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 723 Clickmyproject
A Systematic Review on Educational Data Mining | Final Year Projects 2016 - 2017
 
19:41
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://myprojectbazaar.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 269 myproject bazaar
Lecture - 34 Data Mining and Knowledge Discovery
 
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Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 134228 nptelhrd
VirtualMine -  Educational Mining Offer in Slovenia
 
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The Golden Fleece between the Posavje and Secovjie salt pans. Educational Mining offer for childen and youth in Slovenia. Video by Project Partner ZAG
Mining Social Media Data for Understanding Students’ Learning Experiences
 
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Abstract—Students’ informal conversations on social media (e.g. Twitter, Facebook) shed light into their educational experiences—opinions, feelings, and concerns about the learning process. Data from such uninstrumented environments can provide valuable knowledge to inform student learning. Analyzing such data, however, can be challenging. The complexity of students’ experiences reflected from social media content requires human interpretation. However, the growing scale of data demands automatic data analysis techniques. In this paper, we developed a workflow to integrate both qualitative analysis and large-scale data mining techniques. We focused on engineering students’ Twitter posts to understand issues and problems in their educational experiences. We first conducted a qualitative analysis on samples taken from about 25,000 tweets related to engineering students’ college life. We found engineering students encounter problems such as heavy study load, lack of social engagement, and sleep deprivation. Based on these results, we implemented a multi-label classification algorithm to classify tweets reflecting students’ problems. We then used the algorithm to train a detector of student problems from about 35,000 tweets streamed at the geo-location of Purdue University. This work, for the first time, presents a methodology and results that show how informal social media data can provide insights into students’ experiences. Index Terms—Education, computers and education, social networking, web text analysis
Digging Deep into Educational Data
 
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Ryan Baker, assistant professor of learning sciences and psychology at WPI, is an internationally known pioneer in educational data mining, which uses powerful algorithms to pull paradigm-changing insights from the vast quantities of data about how students interact with learning technologies.
Views: 1023 WPI
13. Classification
 
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag introduces supervised learning with nearest neighbor classification using feature scaling and decision trees. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 36257 MIT OpenCourseWare
12. Clustering
 
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag discusses clustering. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 80264 MIT OpenCourseWare
Sebastian Ventura. “Educational Data Mining”
 
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Charla magistral del Congreso Interacción 2014. Sebastian Ventura. “Educational Data Mining” --- Creative Commons: Reconocimiento - No Comercial - Sin Obra Derivada (CC BY NC ND). http://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ES
K-Means Clustering Algorithm - Cluster Analysis | Machine Learning Algorithm | Data Science |Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4 Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #kmeans #clusteranalysis #clustering #datascience #machinelearning 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). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka 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. "
Views: 63633 edureka!
Lecture 58 — Overview of Clustering | Mining of Massive Datasets | Stanford University
 
08:47
. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
AN INVESTIGATION OF STUDENTS BEHAVIOR IN DISCUSSION FORUMS USING EDUCATIONAL DATA MINING.
 
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Presentation of published research at the Twenty-Eighth International Conference on Software Engineering and Knowledge Engineering (SEKE 2016).
Views: 163 Crystiano Jose
How to predict students' performance?
 
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Talk presented at SSCI2014, in Orlando. Download paper from: http://personal.ee.surrey.ac.uk/Personal/Norman.Poh/data/poh_gradcert.pdf Abstract: Student performance depends upon factors other than intrinsic ability, such as environment, socio-economic status, personality and familial-context. Capturing these patterns of influence may enable an educator to ameliorate some of these factors, or for governments to adjust social policy accordingly. In order to understand these factors, we have undertaken the exercise of predicting student performance, using a cohort of approximately 8,000 South African college students. They all took a number of tests in English and Maths. We show that it is possible to predict English comprehension test results from (1) other test results; (2) from covariates about self-efficacy, social economic status, and specific learning difficulties there are 100 survey questions altogether; (3) from other test results + covariates (combination of (1) and (2)); and from (4) a more advanced model similar to (3) except that the covariates are subject to dimensionality reduction (via PCA). Models 1-4 can predict student performance up to a standard error of 13-15%. In comparison, a random guess would have a standard error of 17%. In short, it is possible to conditionally predict student performance based on self-efficacy, socio-economic background, learning difficulties, and related academic test results.
Views: 5951 Norman Poh
Difference Between Data Mining and Machine Learning
 
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Views: 18259 James Aldwin
Predicting Peer-to-Peer Loan Default Using Data Mining Techniques - Callum Stevens
 
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Access a shiny web app at: https://callumstevens.shinyapps.io/logisticregression/ View full slideshow presentation at: https://goo.gl/mGMkXI Abstract: Loans made via Peer-to-Peer Lending (P2PL) Platforms are becoming ever more popular among investors and borrowers. This is due to the current economic environment where cash deposits earn very little interest, whilst borrowers can face high interest rates on credit cards and short term loans. Investors seeking yielding assets are looking towards P2PL, however most lack prior lending experience. Lenders face the problem of knowing which loans are most likely to be repaid. Thus this project evaluates popular Data Mining classification algorithms to predict if a loan outcome is likely to be 'Fully Repaid‘ or 'Charged Off‘. Several approaches have been used in this project, with the aim of increasing predictive accuracy of models. Several external datasets have been blended to introduce relevant economic data, derivative columns have been created to gain meaning between different attributes. Filter attribute evaluation methods have been used to discover appropriate attribute subsets based on several criteria. Synthetic Minority Over-sampling Technique (SMOTE) has been used to address the imbalanced nature of credit datasets, by creating synthetic 'Charged Off‘ loans to ensure a more even class distribution. Tuning of parameters has been performed, showing how each algorithm‘s performance can vary as a result of changes. Data pre-processing methods have been discussed in detail, which previous research lacked discussion on. The author has documented each Data Mining phase to allow researchers to repeat tests. Selected models have been deployed as Web Applications, providing researchers with accuracy metrics upon which to evaluate them. Possible approaches to improve accuracy further have been discussed, with the hope of stimulating research into this area.
Views: 637 Callum Stevens
APPLICATION OF BIG DATA IN EDUCATION DATA MINING
 
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APPLICATION OF BIG DATA IN EDUCATION DATA MINING
Views: 304 Chennai Sunday
Week 1: Introduction to Learning Analytics
 
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Basic Introduction to Learning Analytics by George Siemens
Social Media Social Data and Python: 4 - Social Media Mining Techniques
 
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In this video we will briefly discuss the overall process for building a social media mining application, before digging into the details. ----- ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE Link to playlist: https://goo.gl/WIHiEy ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- ----- Source Code for tutorials on Youtube: http://bit.ly/2nSQSAT ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh
Views: 2357 Sukhvinder Singh
Learn data  Mining with Weka,data analytics by free online course. Find 2017 highest paying job.
 
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Watch complete tutorial: https://click.linksynergy.com/fs-bin/click?id=gD7cdGyIKG4&offerid=529351.12&type=3&subid=0 What topics will you cover:data analytics ,what is data mining ,data mining definition,introduction to data mining,decision tree in data mining,datasets for data mining, educational data mining, weka data mining, data mining techniques,data mining concepts and techniques,data mining process ,data mining analysis ,data mining methods ,data mining in business . Master data mining software,data mining tools ,data analytics tools. We offer free online courses with certificates, online training,online study ,free online classes ,distance education,online learning,distance learning .
Views: 8 Kanji Yun