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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
Applications of Predictive Analytics in Legal | Litigation Analytics, Data Mining & AI | Great Lakes
 
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#PredictiveAnalytics | Learn the prediction of outcome or treatment of a case by legal courts of Appeals based on historical data using predictive analytics. Watch the video to understand analytics in legal using case study on real-life data set. How litigation analytics can flourish with the use of data mining and AI. Know more about our analytics Program: PGP- Business Analytics: https://goo.gl/V9RzVD PGP- Big Data Analytics: https://goo.gl/rRyjj4 Business Analytics Certification Program: https://goo.gl/7HPoUY #LegalTech #LegalAnalytics #GreatLearning #GreatLakes About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/ - Follow our Blog: https://www.greatlearning.in/blog/?utm_source=Youtube
Views: 728 Great Learning
DataMiningVideo2013
 
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It is a short video regarding Data Mining Applications in Higher Education
Applications of Analytics Across Industry Verticals | Uses of Analytics Across Industries
 
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#ApplicationsOfAnalytics | This video gives a high-level overview of Analytics vertical. You also learn how analytics can be applied across industry verticals and what are the widely used analytics tools and techniques being used. Learn More about our Analytics Programs: PGP-Business Analytics: https://goo.gl/RfXK63 PGP-Big Data Analytics: https://goo.gl/yU9Eqi Business Analytics Certificate Program: https://goo.gl/RYPeDR #DataAnalytics #UsesOfDataAnalytics #Analytics #GreatLearning #GreatLakes About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/
Views: 267 Great Learning
High Dimensional Data
 
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Match the applications to the theorems: (i) Find the variance of traffic volumes in a large network presented as streaming data. (ii) Estimate failure probabilities in a complex systems with many parts. (iii) Group customers into clusters based on what they bought. (a) Projecting high dimensional space to a random low dimensional space scales each vector's length by (roughly) the same factor. (b) A random walk in a high dimensional convex set converges rather fast. (c) Given data points, we can find their best-fit subspace fast. While the theorems are precise, the talk will deal with applications at a high level. Other theorems/applications may be discussed.
Views: 2153 Microsoft Research
Ethics of Data Mining and Predictive Analytics in Higher Education
 
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Presented at the Rocky Mountain Association for Institutional Research Conference Laramie, Wyoming | October 5, 2012 Data mining and predictive analytics are increasingly used in higher education to classify students and predict student behavior. But while the potential benefits of such techniques are significant, realizing them presents a range of ethical and social challenges. The immediate challenge considers the extent to which data mining's outcomes are themselves ethical with respect to both individuals and institutions. A deep challenge, not readily apparent to institutional researchers or administrators, considers the implications of uncritical understanding of the scientific basis of data mining. These challenges can be met by understanding data mining as part of a value-laden nexus of problems, models, and interventions; by protecting the contextual integrity of information flows; and by ensuring both the scientific and normative validity of data mining applications.
Views: 746 Jeff Johnson
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: 555 Clickmyproject
Data Mining - Predicting Scientific Impact | lectures On-Demand
 
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Avishay Livne - Graduate Student, Computer Science and Engineering at the University of Michigan The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
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: 227 Pixel Conferences
Scanner: Efficient Video Analysis at Scale (SIGGRAPH 2018)
 
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http://scanner.run/ http://graphics.stanford.edu/papers/scanner/scanner_sig18.pdf Scanner is a system for developing applications that efficiently process large video datasets. Scanner applications can run on a multi-core laptop, a server packed with multiple GPUs, or a large number of machines in the cloud. Scanner has been used for: * Labeling and data mining large video collections: Scanner is in use at Stanford University as the compute engine for visual data mining applications that detect people, commercials, human poses, etc. in datasets as big as 70,000 hours of TV news (12 billion frames, 20 TB) or 600 feature length movies (106 million frames). * VR Video synthesis: scaling the Surround 360 VR video stitching software, which processes fourteen 2048x2048 input videos to produce 8k stereo video output. To learn more about Scanner, see the documentation below or read the SIGGRAPH 2018 Technical Paper: “Scanner: Efficient Video Analysis at Scale” by Poms, Crichton, Hanrahan, and Fatahalian.
Views: 1750 Will Crichton
Big Data, the Science of Learning, Analytics, and Transformation of Education
 
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From the mediaX Conference “Platforms for Collaboration and Productivity”, Candace Thille, with the Stanford Graduate School of Education highlights the power of platform tools and technologies to transform observation and data collection. This process enables researchers from industry and academia to know their user better – as consumers, as producers, and as learners.
Views: 8711 Stanford
Analytics in higher education
 
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Find out how analytics can help you make sense of data and stay one step ahead. From attracting more quality applications, improving graduation results and becoming a centre of research excellence to increasing revenue year on year, data – and intelligent analytics from that data - will give you the insight you need to make a difference. Watch our video to discover more. Visit : http://www.caci.co.uk/technology-solutions/higher-education or call +442076026000
Views: 1065 CACI
APPLICATION OF BIG DATA IN EDUCATION DATA MINING
 
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APPLICATION OF BIG DATA IN EDUCATION DATA MINING
Views: 293 Chennai Sunday
Data Mining in Education
 
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I created this video with the YouTube Video Editor (http://www.youtube.com/editor)
Views: 668 stlgretchen
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
Data Mining Software for Business and Science Video Tour
 
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The newest data mining methods were incorporated into ESTARD Data Miner for carrying out automated data analysis. To work with this data mining tool you won't need SQL knowledge or long special trainings. The tool is a powerful end-to-end analytical solution: using it withing few clicks you will be able to discover hidden relations in data and to apply discovered knowledge for WHAT-IF analysis and searching for data patterns. Prediction becomes easy as never before. This data mining tool can be used knowledge discovery in various sectors including: * insurance industry * banking * finances * marketing campaigns * accounting & inventory management * healthcare * scientific researches * military sphere. Thanks to built in wizards and user-friendly interface unexperienced users need minimum time to start working with our data mining tool. ESTARD Software: https://secure.avangate.com/affiliate.php?ACCOUNT=HESTARD&AFFILIATE=25621&PATH=http%3A%2F%2Fwww.estard.com
Views: 858 Derrick Pride
Development of a Data Mining Education Framework for Data Visualization in Distance Learning Envir.
 
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Development of a Data Mining Education Framework for Data Visualization in Distance Learning Environments. Presentation of published research at the Twenty-Nine International Conference on Software Engineering and Knowledge Engineering (SEKE 2017).
Views: 28 Angelo Dias
Anomaly Detection: Algorithms, Explanations, Applications
 
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Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning. See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/
Views: 9868 Microsoft Research
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: 70732 MIT OpenCourseWare
Graph Clustering Algorithms (September 28, 2017)
 
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Tselil Schramm (Simons Institute, UC Berkeley) One of the greatest advantages of representing data with graphs is access to generic algorithms for analytic tasks, such as clustering. In this talk I will describe some popular graph clustering algorithms, and explain why they are well-motivated from a theoretical perspective. ------------------- References from the Whiteboard: Ng, Andrew Y., Michael I. Jordan, and Yair Weiss. "On spectral clustering: Analysis and an algorithm." Advances in neural information processing systems. 2002. Lee, James R., Shayan Oveis Gharan, and Luca Trevisan. "Multiway spectral partitioning and higher-order cheeger inequalities." Journal of the ACM (JACM) 61.6 (2014): 37. ------------------- Additional Resources: In my explanation of the spectral embedding I roughly follow the exposition from the lectures of Dan Spielman (http://www.cs.yale.edu/homes/spielman/561/), focusing on the content in lecture 2. Lecture 1 also contains some additional striking examples of graphs and their spectral embeddings. I also make some imprecise statements about the relationship between the spectral embedding and the minimum-energy configurations of a mass-spring system. The connection is discussed more precisely here (https://www.simonsfoundation.org/2012/04/24/network-solutions/). License: CC BY-NC-SA 4.0 - https://creativecommons.org/licenses/by-nc-sa/4.0/
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.
Analyzing Big Data in less time with Google BigQuery
 
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Most experienced data analysts and programmers already have the skills to get started. BigQuery is fully managed and lets you search through terabytes of data in seconds. It’s also cost effective: you can store gigabytes, terabytes, or even petabytes of data with no upfront payment, no administrative costs, and no licensing fees. In this webinar, we will: - Build several highly-effective analytics solutions with Google BigQuery - Provide a clear road map of BigQuery capabilities - Explain how to quickly find answers and examples online - Share how to best evaluate BigQuery for your use cases - Answer your questions about BigQuery
Views: 58806 Google Cloud Platform
BADM 6.1 Classification Goals
 
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This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining. It is part of a series of 37 videos, all of which are available on YouTube. For more information: http://www.dataminingbook.com https://www.twitter.com/gshmueli https://www.facebook.com/dataminingbook Here is the complete list of the videos: • Welcome to Business Analytics Using Data Mining (BADM) • BADM 1.1: Data Mining Applications • BADM 1.2: Data Mining in a Nutshell • BADM 1.3: The Holdout Set • BADM 2.1: Data Visualization • BADM 2.2: Data Preparation • BADM 3.1: PCA Part 1 • BADM 3.2: PCA Part 2 • BADM 3.3: Dimension Reduction Approaches • BADM 4.1: Linear Regression for Descriptive Modeling Part 1 • BADM 4.2 Linear Regression for Descriptive Modeling Part 2 • BADM 4.3 Linear Regression for Prediction Part 1 • BADM 4.4 Linear Regression for Prediction Part 2 • BADM 5.1 Clustering Examples • BADM 5.2 Hierarchical Clustering Part 1 • BADM 5.3 Hierarchical Clustering Part 2 • BADM 5.4 K-Means Clustering • BADM 6.1 Classification Goals • BADM 6.2 Classification Performance Part 1: The Naive Rule • BADM 6.3 Classification Performance Part 2 • BADM 6.4 Classification Performance Part 3 • BADM 7.1 K-Nearest Neighbors • BADM 7.2 Naive Bayes • BADM 8.1 Classification and Regression Trees Part 1 • BADM 8.2 Classification and Regression Trees Part 2 • BADM 8.3 Classification and Regression Trees Part 3 • BADM 9.1 Logistic Regression for Profiling • BADM 9.2 Logistic Regression for Classification • BADM 10 Multi-Class Classification • BADM 11 Ensembles • BADM 12.1 Association Rules Part 1 • BADM 12.2 Association Rules Part 2 • Neural Networks: Part I • Neural Networks: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 314 Galit Shmueli
BADM 5.4 K-Means Clustering
 
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This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining. It is part of a series of 37 videos, all of which are available on YouTube. For more information: http://www.dataminingbook.com https://www.twitter.com/gshmueli https://www.facebook.com/dataminingbook Here is the complete list of the videos: • Welcome to Business Analytics Using Data Mining (BADM) • BADM 1.1: Data Mining Applications • BADM 1.2: Data Mining in a Nutshell • BADM 1.3: The Holdout Set • BADM 2.1: Data Visualization • BADM 2.2: Data Preparation • BADM 3.1: PCA Part 1 • BADM 3.2: PCA Part 2 • BADM 3.3: Dimension Reduction Approaches • BADM 4.1: Linear Regression for Descriptive Modeling Part 1 • BADM 4.2 Linear Regression for Descriptive Modeling Part 2 • BADM 4.3 Linear Regression for Prediction Part 1 • BADM 4.4 Linear Regression for Prediction Part 2 • BADM 5.1 Clustering Examples • BADM 5.2 Hierarchical Clustering Part 1 • BADM 5.3 Hierarchical Clustering Part 2 • BADM 5.4 K-Means Clustering • BADM 6.1 Classification Goals • BADM 6.2 Classification Performance Part 1: The Naive Rule • BADM 6.3 Classification Performance Part 2 • BADM 6.4 Classification Performance Part 3 • BADM 7.1 K-Nearest Neighbors • BADM 7.2 Naive Bayes • BADM 8.1 Classification and Regression Trees Part 1 • BADM 8.2 Classification and Regression Trees Part 2 • BADM 8.3 Classification and Regression Trees Part 3 • BADM 9.1 Logistic Regression for Profiling • BADM 9.2 Logistic Regression for Classification • BADM 10 Multi-Class Classification • BADM 11 Ensembles • BADM 12.1 Association Rules Part 1 • BADM 12.2 Association Rules Part 2 • Neural Networks: Part I • Neural Networks: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 418 Galit Shmueli
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
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: 103736 LearnEveryone
Master Innovation Research Informatics - Data Mining and Business Intelligence - FIB
 
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FIB Master's Degrees are official university studies within the framework of the European Higher Education Area (EHEA). Your degree is acknowledged all across the globe and it meets EU’s requirements. More information at: http://masters.fib.upc.edu/ The master empowers graduates with solid knowledge and hands-on experience on the techniques to manage, analyze and extract hidden knowledge from Big Data ensembles, either structured and unstructured, and to build adaptive Analytic systems able to exploit that knowledge in modern organizations. In particular the master addresses the new challenges of the smart society bloom: fraud detection, bioinformatics, extracting information from open linked data, real time analysis of sensor data and social networks, and customer relationship management,
Views: 1946 mediafib
Application of data mining: Diabetes health care in young | 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://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: 120 myproject bazaar
▶️$168,361 How To Get Higher Limit Credit Cards, Learn the SECRET Learn About CARDMATCH
 
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▶️Learn to Leverage your credit and make your credit it work for you. $168,361 How To Get Higher Limit Credit Cards, Learn the SECRET Learn About CARDMATCH Check out CreditCards.com for CARDMATCH How to Remove Negative Credit Items / Collections + Credit Inquiries + Sample Letters PROVIDED, FREE DYI CREDIT REPAIR Link to Free Federal Credit Reports www.annualcreditreport.com Credit Repair Letter Provided by RandomFix https://drive.google.com/file/d/0B8YhYO1fFwFlM3RISnFKMEJXaG8/view?usp=sharing Credit Inquiry Removal by RandomFix https://drive.google.com/file/d/0B8YhYO1fFwFlYU5BU2JFSzRJMVU/view?usp=sharing Cool information about credit score A credit score is a numerical expression based on a level analysis of a person's credit files, to represent the creditworthiness of an individual. A credit score is primarily based on a credit report information typically sourced from credit bureaus. Lenders, such as banks and credit card companies, use credit scores to evaluate the potential risk posed by lending money to consumers and to mitigate losses due to bad debt. Lenders use credit scores to determine who qualifies for a loan, at what interest rate, and what credit limits. Lenders also use credit scores to determine which customers are likely to bring in the most revenue. The use of credit or identity scoring prior to authorizing access or granting credit is an implementation of a trusted system. Credit scoring is not limited to banks. Other organizations, such as mobile phone companies, insurance companies, landlords, and government departments employ the same techniques. Digital finance companies such as online lenders also use alternative data sources to calculate the creditworthiness of borrowers. Credit scoring also has much overlap with data mining, which uses many similar techniques. These techniques combine thousands of factors but are similar or identical. Give the Gift of Prime https://goo.gl/YJTEMn Thanks for your support. God Bless -RandomFIX www.RandomFIXWorld.com **If the video was helpful, remember to give it a and consider subscribing. New videos every Monday** How to get high limit credit cards fast good credit equal high credit limit cards
Views: 4399 RANDOMFIX
SAE J1939 Explained - A Simple Intro (2018)
 
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NEW: You can now get our updated J1939.DBC file below: https://www.csselectronics.com/screen/product/j1939-dbc-file-pgn-spn Looking for a simple intro to J1939? In this video we go through the basics, applications, PGNs, SPNs and more! For the written article go to: http://www.csselectronics.com/screen/page/simple-intro-j1939-explained SAE J1939 is the standard communications network for ECUs within commercial vehicles. This includes in particular heavy duty applications such as trucks, buses, foresting, mining, agriculture etc. J1939 is a higher layer protocol based on CAN bus and specifies e.g. how to handle multi-packet messages - and how to interpret raw data. In particular, J1939 defines standard Parameter Group Numbers (PGNs) and Suspect Parameter Numbers (SPNs). Armed with a J1939 data logger and the J1939-71 standard, one is able to go from raw J1939 CAN bus data to scaled engineering values on e.g. vehicle speed, RPM and more. In this intro, we run through the basics of the SAE J1939 standard incl. applications, message interpretation and considerations when choosing a J1939 data logger or J1939 interface. We also briefly touch upon J1939 DBC files, the J1939 request message and J1939 multi packet messages. For more articles like this, check out our INTEL page: http://www.csselectronics.com/screen/page/can-bus-articles-tools-cases ___________________________________________ At CSS Electronics, we offer powerful, simple and affordable CAN bus analyzers. Our CLX000 series doubles as both a powerful CAN logger with 8GB SD card and a CAN interface integrating with Wireshark. Features include advanced configuration options, DBC file data conversion support (incl. for J1939), real-time graphical plots - and much more. Pricing starts at 169 EUR with free shipping and 100% free software. For more details, check out http://www.csselectronics.com ! Music credits: PC ONE Monachine (Instrumental Acoustic)
Views: 43418 CSS Electronics
The collection and applications of big data - Full interview with Diane Schanzenbach | VIEWPOINT
 
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The modern economy has never been more reliant on data. Businesses, governments, and families must navigate the complexities of a world made possible by new technologies and innovative business practices. Without reliable information about the economic and social environment, it is impossible in many instances to make sensible choices. Diane Schanzenbach and Michael Strain discuss the uses and benefits of the economic and social data that government agencies collect. AEI & Hamilton Project report – The Vital Role of Government-Collected Data: https://goo.gl/UOfB0m Michael Strain is Director of Economic Policy Studies and Resident Scholar at American Enterprise Institute: https://goo.gl/RQl1na Diane Schanzenbach is Director of the Hamilton Project at the Brookings Institution: https://goo.gl/VTN2zf Subscribe to AEI's YouTube Channel https://www.youtube.com/user/AEIVideos?sub_confirmation=1 Like us on Facebook https://www.facebook.com/AEIonline Follow us on Twitter https://twitter.com/AEI For more information http://www.aei.org Thumbnail photo credit: BY - Eric Fischer https://goo.gl/GX4xjh Photos marked "BY" are used under Creative Commons Attribution License: https://creativecommons.org/licenses/by/2.0/ Third-party photos, graphics, and video clips in this video may have been cropped or reframed. Music in this video may have been recut from its original arrangement and timing. In the event this video uses Creative Commons assets: If not noted in the description, titles for Creative Commons assets used in this video can be found at the link provided after each asset. The use of third-party photos, graphics, video clips, and/or music in this video does not constitute an endorsement from the artists and producers licensing those materials. AEI operates independently of any political party and does not take institutional positions on any issues. AEI scholars, fellows, and their guests frequently take positions on policy and other issues. When they do, they speak for themselves and not for AEI or its trustees or other scholars or employees. More information on AEI research integrity can be found here: http://www.aei.org/about/ #aei #news #politics #government #education #data #bigdata #census #business #economy #economics #taxes
VC-Dimension and Rademacher Averages - Part 1
 
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Author: Matteo Riondato, Eli Upfal Abstract: Rademacher Averages and the Vapnik-Chervonenkis dimension are fundamental concepts from statistical learning theory. They allow to study simultaneous deviation bounds of empirical averages from their expectations for classes of functions, by considering properties of the functions, of their domain (the dataset), and of the sampling process. In this tutorial, we survey the use of Rademacher Averages and the VC-dimension in sampling-based algorithms for graph analysis and pattern mining. We start from their theoretical foundations at the core of machine learning, then show a generic recipe for formulating data mining problems in a way that allows to use these concepts in efficient randomized algorithms for those problems. Finally, we show examples of the application of the recipe to graph problems (connectivity, shortest paths, betweenness centrality) and pattern mining. Our goal is to expose the usefulness of these techniques for the data mining researcher, and to encourage research in the area. ACM DL: http://dl.acm.org/citation.cfm?id=2789984 DOI: http://dx.doi.org/10.1145/2783258.2789984
Technologies to Fuel the Next Decade of Big Data
 
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The value and intelligence that can be extracted from Big Data, and other large data generating applications, has created strategic opportunities for HDD capacity expansion in the enterprise. By delivering the best and most stable sealed, helium-filled HDD environment, with improved mechanical head positioning through multi-stage micro actuation, Western Digital has delivered higher capacities with each product generation. The use of the Damascene process for developing head assemblies for our helium HDD portfolio, coupled with energy-assisted magnetic recording technology, will enable us to deliver even higher HDD capacities in the future. Learn more: https://www.datamakespossible.com/ Join the conversation on Twitter with @WesternDigital and #DataMakesPossible ABOUT WESTERN DIGITAL: Western Digital creates environments for data to thrive. Everywhere data is captured, preserved, accessed and transformed, we’re leading the charge to unlock its potential.
How can data science and analytics improve education? [Wamda TV]
 
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The Arab region still lags behind when it comes to mining big data for decision making, especially in the education sector. While globally, universities and schools embarked on a data driven approach to predict students behavior. In this video, Dr. Fatima Abu Salem from the AUB and Sara Najem consultant at Caltech discuss the importance of data science in disrupting the education sector in the Arab region. Help us caption & translate this video! http://amara.org/v/5u57/
Views: 432 Wamda
Text Mining with Big Data
 
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The video illustrates how text mining techniques allow the analysis of text written in natural language, in order to detect semantic relationships and enable text classification. Audio in Italian. English subtitles available. Illustrations developed by Monica Franceschini, Solution Architecture Manager, Big Data & Analytics Competency Center, Engineering Group.
Views: 331 ItalyMadeOpenSource
evaluation of predictive data mining algorithms in soil data classification for optimized crop
 
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evaluation of predictive data mining algorithms in soil data classification for optimized crop recom - IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS 1. RRPhish Anti-Phishing via Mining Brand Resources Request 2. Confidence-interval Fuzzy Model-based Indoor Localization COMPUTER-BASED MEDICAL SYSTEMS (CBMS) 1. Population Health Management exploiting Machine Learning Algorithms to identify High-Risk Patients (23 July 2018) PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1. Trunk-Branch Ensemble Convolutional Neural Networks for Video-based Face Recognition ( April 1 2018 ) 2. Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection 3. Ordinal Constraint Binary Coding for Approximate Nearest Neighbor Search SOFTWARE ENGINEERING,COMPUTER GRAPHICS 1. Reviving Sequential Program Birthmarking for Multithreaded Software Plagiarism Detection 2. EVA: Visual Analytics to Identify Fraudulent Events 3. Performance Specification and Evaluation with Unified Stochastic Probes and Fluid Analysis 4. Trustrace: Mining Software Repositories to Improve the Accuracy of Requirement Traceability Links 5. Amorphous Slicing of Extended Finite State Machines 6. Test Case-Aware Combinatorial Interaction Testing 7. Using Timed Automata for Modeling Distributed Systems with Clocks: Challenges and Solutions 8. EDZL Schedulability Analysis in Real-Time Multicore Scheduling 9. Ant Colony Optimization for Software Project Scheduling and Staffing with an Event-Based Scheduler 10. Locating Need-to-Externalize Constant Strings for Software Internationalization with Generalized String-Taint Analysis 11. Systematic Elaboration of Scalability Requirements through Goal-Obstacle Analysis 12. Centroidal Voronoi Tessellations- A New Approach to Random Testing 13. Ranking and Clustering Software Cost Estimation Models through a Multiple Comparisons Algorithm 14. Pair Programming and Software Defects--A Large, Industrial Case Study 15. Automated Behavioral Testing of Refactoring Engines 16. An Empirical Evaluation of Mutation Testing for Improving the Test Quality of Safety-Critical Software 17. Self-Management of Adaptable Component-Based Applications 18. Elaborating Requirements Using Model Checking and Inductive Learning 19. Resource Management for Complex, Dynamic Environments 20. Identifying and Summarizing Systematic Code Changes via Rule Inference 21. Generating Domain-Specific Visual Language Tools from Abstract Visual Specifications 22. Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers 23. On Fault Representativeness of Software Fault Injection 24. A Decentralized Self-Adaptation Mechanism for Service-Based Applications in the Cloud 25. Coverage Estimation in Model Checking with Bitstate Hashing 26. Synthesizing Modal Transition Systems from Triggered Scenarios 27. Using Dependency Structures for Prioritization of Functional Test Suites
Views: 4 MICANS VIDEOS
The Data-Mining Revolution: MUM prepares students for the skills and jobs of the future
 
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http://www.mum.edu Prof. Anil Maheshwari, Ph.D., discusses the new immersion program Maharishi University of Management has just launched to train students in the next wave of data-mining software. In today's data-driven economy there is an urgent need for more sophisticated software programs to mine and better utilize data coming in over multiple platforms from diverse sectors of the economy, not only for business, but also for higher education. To help Maharishi University of Management students build essential skills in analytics technology, we recently joined the IBM Academic Initiative, which offers participating schools no-charge access to IBM software, discounted hardware, course materials, training and curriculum development—over 6,000 universities and 30,000 faculty members worldwide are members of the program. "We are using industrial strength tools such as IBM SPSS Modeler," Dr. Maheshwari said, "along with open-source tools, to provide our students a strong data-mining toolkit to engage with Big Data, and generate interesting insights and new knowledge." Students will learn more than just how to operate the software, but how to use it effectively as a business tool. Dr. Maheshwari said, "Our students will have end-to-end skills to discern what is the business problem, what is the data being generated, how do I mine the data, how do I generate intelligence out of it and feed it back to the business so the business can actually benefit from it. That whole cycle is what we're training, not just the tool itself." Industry analysts have identified predictive analytics as the fastest growing software category for company spending. They also expect that the need for staff with these capabilities will outpace available skill sets in many organizations. This will mean that expertise in data mining and predictive analytics will be highly sought after for years to come. "Having this kind of software suite on their resumes can be a big advantage for our students headed for IT/management jobs," said Dr. Maheshwari. For more videos about MUM, visit our Video Café: http://www.mum.edu/video-cafe At MUM, Consciousness-Based education connects everything you learn to the underlying wholeness of life. So each class becomes relevant, because the knowledge of that subject is connected with your own inner intelligence. You study traditional subjects, but you also systematically cultivate your inner potential developing your creativity and learning ability. Your awareness expands, improving your ability to see the big picture, and to relate to others. Maharishi University of Management (MUM) offers undergraduate and graduate degree programs in the arts, sciences, business, and humanities. The University is accredited through the doctoral level by the Higher Learning Commission. Founded in 1971 by Maharishi Mahesh Yogi, the University features Consciousness-Based education to develop students' inner potential. All students and faculty practice the Transcendental Meditation technique, which extensive published research has found boosts learning ability, improves brain functioning, and reduces stress. Maharishi University uses the block system in which each student takes one course at a time. Students report they learn more without the stress of taking 4-5 courses at once. The University has a strong focus on sustainability and natural health, and serves organic vegetarian meals. The B.S. in Sustainable Living is MUM's most popular undergraduate major. Maharishi University of Management: http://www.mum.edu Consciousness-Based education: http://www.mum.edu/cbe BS Sustainable Living: http://www.mum.edu/sustainable_living/ Transcendental Meditation: http://www.mum.edu/tm Research: http://www.mum.edu/tm_research Block system: http://www.mum.edu/cbe/block Sustainability: http://www.mum.edu/sustainability Natural health: http://www.mum.edu/cbe/natural_health Organic veg meals: http://www.mum.edu/campus/dining
RITMO: Reinventing Urban Transportation and Mobility
 
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The RITMO project is led by Pascal Van Hentenryck, Seth Bonder Collegiate Professor of Industrial and Operations Engineering, and is funded by the Michigan Institute of Data Science and aims at reinventing urban transportation and mobility. It builds on two key enablers, connected and automated vehicles, and leverages the tremendous progress in data science to design and operate a new generation of on-demand urban transit systems. RITMO assembles a multi-disciplinary team of researchers, from computer science, industrial and operations engineering, medicine, the school of information, urban planning, and the transportation research institute. RITMO carries basic research in data science, from descriptive to predictive and prescriptive analytics, spanning research in data mining, machine learning, optimization, computational economics, marketing, and urban planning. RITMO also aims at deploying its results on significant case studies through the development of mobile applications and high-performance analytics over massive data sets. The project is partnering with the UM Parking and Transportation Services, the UM Information and Technology Services,, the UM advanced research computing technology services for the deployment of our technologies, and the Mobility Transformation Center. A first deployment on the UM campus should take place in 2017.
ICPR 2018: Probabilistic Sparse Subspace Clustering Using Delayed Association
 
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International Conference on Pattern Recognition 2018: Probabilistic Sparse Subspace Clustering Using Delayed Association Abstract—Discovering and clustering subspaces in high- dimensional data is a fundamental problem of machine learning with a wide range of applications in data mining, computer vision, and pattern recognition. Earlier methods divided the problem into two separate stages of finding the similarity matrix and finding clusters. Similar to some recent works, we integrate these two steps using a joint optimization approach. We make the following contributions: (i) we estimate the reliability of the cluster assignment for each point before assigning a point to a subspace. We group the data points into two groups of “certain” and “uncertain”, with the assignment of latter group delayed until their subspace association certainty improves. (ii) We demonstrate that delayed association is better suited for clustering subspaces that have ambiguities, i.e. when subspaces intersect or data are contaminated with outliers/noise. (iii) We demonstrate experimentally that such delayed probabilistic association leads to a more accurate self-representation and final clusters. The proposed method has higher accuracy both for points that exclusively lie in one subspace, and those that are on the intersection of subspaces. (iv) We show that delayed association leads to huge reduction of computational cost, since it allows for incremental spectral clustering. Maryam Jaberi, Department of Computer Science, University of Central Florida, Orlando, FL, USA Marianna Pensky, Department of Mathematics, University of Central Florida, Orlando, FL, USA Hassan Foroosh, Department of Computer Science, University of Central Florida, Orlando, FL, USA https://arxiv.org/abs/1808.09574
Views: 23 maryam9586
Student data mining solution–knowledge management system related to higher education institutions
 
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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://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: 31 myproject bazaar
What is Analytics?
 
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We asked several higher ed professionals what analytics means to them. The results provide an insight into the nature of analytics and what it means to higher education institutions.
Views: 45482 educause
01 - Application to Clustering (12 min)
 
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Description
Views: 39 xind xrci
The Logic of Data Mining in Social Research
 
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This video is a brief introduction for undergraduates to the logic (not the nitty-gritty details) of data mining in social science research. Four orienting tips for getting started and placing data mining in the broader context of social research are included.
Views: 331 James Cook
Next in (Data) Science | Part 1 | Radcliffe Institute
 
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The Next in Science Series provides an opportunity for early-career scientists whose innovative, cross-disciplinary research is thematically linked to introduce their work to one another, to fellow scientists, and to nonspecialists from Harvard and the greater Boston area. This year’s program focuses on innovative applications of data science to a wide range of disciplines. The speakers’ talks demonstrate how data science approaches have become critical to a variety of fields, including social media, the movie industry, public health, and the study of the origins of our universe. Welcome and Introduction Alyssa A. Goodman, faculty codirector of the science program, Radcliffe Institute for Advanced Study, and Robert Wheeler Willson Professor of Applied Astronomy, Faculty of Arts and Sciences, Harvard University (5:55) “Uncovering Online Censorship and Propaganda in China” Jennifer Pan, assistant professor of communication and, by courtesy, of political science and sociology, Stanford University (31:16) “Hollywood Data Science: The Role of Inference and Prediction” Nathan Sanders, vice president of quantitative analytics, Legendary Entertainment
Views: 5296 Harvard University
Principal Component Analysis Tutorial Part 1 | Python Machine Learning Tutorial Part 3
 
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Principal Component Analysis Tutorial | Python Machine Learning Tutorial Part 3 https://acadgild.com/big-data/data-science-training-certification?aff_id=6003&source=youtube&account=CeXxokx8izc&campaign=youtube_channel&utm_source=youtube&utm_medium=python-machine-learning-pca-part3&utm_campaign=youtube_channel Machine learning algorithm typically finds the pattern and relationships in data without human intervention but the data that the machine learning algorithm had to deal with are usually very high dimensional. Welcome back to another session of Machine Learning Algorithms in Python tutorial powered by Acadgild. In the previous video, you have learned the linear regression. If you have missed the previous, please check the links as follows. Simple Linear Regression - https://www.youtube.com/watch?v=iL_iWFSzjK8&t=7s Implementing Linear Regression in Python - https://www.youtube.com/watch?v=M1mzE1IT-Is&t=225s In this machine learning tutorial, you will be able to learn Principal Component Analysis in python. Principal Component Analysis is a data pre-processing technique that allows the data to be transformed from higher dimensional space to a lower dimensional space in such a way that information that is crucial to drawing conclusions about the data is not lost. So, What Exactly is Principal Component Analysis (PCA)? • Principal Component Analysis (PCA) is a dimensionally-reduction technique that is often used to transform a high-dimensional dataset into smaller-dimensional subspace • PCA is mathematically defined as an orthogonal linear transformation that transforms the data to a new coordinate system such that the greatest variance by some projection of the data comes to lie on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on. What are Principal Components? • Directions in which the data has the most variance – directions in which the data is most spread out • Mathematically, Eigenvectors of the symmetric covariance matrix of the original dataset • Each Eigenvector has the corresponding Eigenvalue. The Eigenvalue is a scalar that explains how much variance there is in the corresponding Eigenvector direction. Applications of Principal Component Analysis (PCA) • Compression • Visualization of high dimensional data • Speeding up of machine learning algorithms • Reducing noise from data Using Principal Component Analysis (PCA) for Compression: Once Eigenvectors are computed, compress the dataset by ordering k eigenvectors according to largest eigenvalues and compute Axk Reconstruct from the compressed version. We can reconstruct the data back by using inverse transformation mathematically represented by Axk x k.T Kindly, go through the complete video and please like, share and subscribe the channel. #PCA, #principalcomponentanalysis, #python, #datascience, #machinelearning Please like share and subscribe the channel for more such video. For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 952 ACADGILD
Building Enigma / The largest Ethereum Mining Facility
 
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https://genesis-mining.com/pricing https://ethereum.org Ethereum is the first ‘world computer’. It is a decentralized network that can be used by anyone and is capable of running applications with no possibility of downtime, censorship or fraud. It's native currency, Ether is one of the fastest growing cryptocurrencies next to Bitcoin. Just a few months ago, the price was $1, then it shot up to $13 and today it has settled at just under $10. This rapid growth excited investors who were eager not to miss out on another hyper-growth investment opportunity. While some choose to invest in Ethereum directly, many are turning to Cloud Mining to enter the market. Our Enigma Farm is a computation cluster built for exactly this venture. If you are as fascinated by the Ethereum project as we are and want to participate, head over to our website and become a part of the project!
Views: 2016223 Genesis Mining
CppCon 2017: Tobias Fuchs “Multidimensional Index Sets for Data Locality in HPC Applications”
 
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The Point of Views: Multidimensional Index Sets for Data Locality in HPC Applications http://CppCon.org — Presentation Slides, PDFs, Source Code and other presenter materials are available at: https://github.com/CppCon/CppCon2017 — In High Performance Computing, data access has complex implications and requires concepts that are fundamentally different from those provided in the STL. Iterators as we know them just are not enough. The proposed range concepts for the standard library are a significant improvement but are designed for the mental model of iterating and mapping values, not hierarchical domain decomposition. Even for a seemingly trivial array there are countless ways to partition and store its elements in distributed memory, and algorithms are required to behave and scale identically for all of them. It also does not help that most applications operate on multidimensional data structures where efficient access to neighborhood regions is crucial. Among HPC developers, it is therefore widely accepted that canonical iteration space and physical memory layout must be specified as separate concepts. For this, we use views based on multidimensional index sets, inspired by the proposed range concepts. In this session, we will explain the challenges when distributing container elements for thousands of cores and how modern C++ allows to achieve portable efficiency. As an HPC afficionado, you know you want this: copy( matrix_a | local() | block({ 2,3 }), matrix_b | block({ 4,5 }) ) If this does not look familiar to you: we give a gentle introduction to High Performance Computing along the way. — Tobias Fuchs: LMU Munich, Leibniz Supercomputing Centre, Research Associate Tobi is a freelancer in embedded systems and real-time applications for over 10 years, mostly for medical devices, and went back to academia for PhD studies in High Performance Computing at LMU Munich. He is the lead developer of the DASH C++ template library, a project of the German Research Foundation (DFG), and currently focuses on models and programming abstractions for data locality. As a hobby, he lures unsuspecting students into his C++ programming course to entrap them in category theory. — Videos Filmed & Edited by Bash Films: http://www.BashFilms.com
Views: 3635 CppCon
Big Data and Education | PennX on edX
 
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Enroll now: https://www.edx.org/course/big-data-education-pennx-bde1x Learn the methods and strategies for using large-scale educational data to improve education and make discoveries about learning. Online and software-based learning tools have been used increasingly in education. This movement has resulted in an explosion of data, which can now be used to improve educational effectiveness and support basic research on learning. In this course, you will learn how and when to use key methods for educational data mining and learning analytics on this data. You will examine the methods being developed by researchers in the educational data mining, learning analytics, learning-at-scale, student modeling, and artificial intelligence communities. You’ll also gain experience with standard data mining methods frequently applied to educational data. You will learn how to apply these methods and when to apply them, as well as their strengths and weaknesses for different applications. The course will discuss how to use each method to answer education research questions, and to drive intervention and improvement in educational software and systems. Methods will be covered at a theoretical level, and in terms of learning how to apply them using software tools like RapidMiner. We will also discuss validity and generalizability; establishing how trustworthy and applicable the analysis results. What you'll learn Key methods for educational data mining How to apply methods using standard tools such as RapidMiner How to use methods to answer practical educational questions
Views: 1652 edX

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