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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 and AI
 
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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
Views: 627 Great Learning
DataMiningVideo2013
 
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It is a short video regarding Data Mining Applications in Higher Education
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: 2001 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: 734 Jeff Johnson
A.I. Experiments: Visualizing High-Dimensional Space
 
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Check out https://g.co/aiexperiments to learn more. This experiment helps visualize what’s happening in machine learning. It allows coders to see and explore their high-dimensional data. The goal is to eventually make this an open-source tool within TensorFlow, so that any coder can use these visualization techniques to explore their data. http://g.co/aiexperiments Built by Daniel Smilkov, Fernanda Viégas, Martin Wattenberg, and the Big Picture team at Google. More resources: http://www.tensorflow.org
Views: 449710 Google Developers
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: 284 James Cook
Cloud-based Data Mining Tools part 1
 
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Cloud-based Data Mining Tools for Storage, Distributed Processing, and Machine Learning Systems for Scientific Data (part 1) Author: Dennis Gannon, Computer Science Department, Indiana University Vani Mandava, Microsoft Research Abstract: This hands-on training is intended to familiarize researchers and data scientists with the services Azure offers to aid them in their research, especially with regard to high-performance computing, big-data analysis, and analyzing data streaming from Internet-of-Things (IoT) devices. https://a4ronline.azurewebsites.net/ More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 72 KDD2017 video
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: 1665 Will Crichton
Applications of Analytics across Industry Verticals | Uses of Analytics across Industries
 
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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
Views: 248 Great Learning
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: 424 ClickMyProject
Application of Big Data In Education Data Mining And Lerning Analytics A Literature Review
 
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Application of Big Data In Education Data Mining And Lerning Analytics A Literature Review
Data Mining in Higher Education
 
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Table of Contents: 00:04 - Better predict each student's Performance by taking into account More than grades 00:12 - Better manage marketing dollars for recruitment. 00:16 - Better understanding of factors related to struggling students, ultimately to increase retention. 00:22 - An understanding of support programs' effectiveness. 00:26 - Better understanding demographic and other factors 00:32 - Determine which non-need based packages attract the best students. 00:39 - What factors lead to student retention, especially at-risk students? 00:45 - Predict which students are likely to default on their student loans. 00:50 - Comment!
Views: 295 Salford Systems
Data Mining in Education
 
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I created this video with the YouTube Video Editor (http://www.youtube.com/editor)
Views: 655 stlgretchen
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: 7375 Microsoft Research
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: 99896 LearnEveryone
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: 2012831 Genesis Mining
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
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: 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: 51859 Google Cloud Platform
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: 273 ItalyMadeOpenSource
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: 62066 MIT OpenCourseWare
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
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: 1868 mediafib
Developer Data Scientist – New Analytics Driven Apps Using Azure Databricks & Apache Spark | B116
 
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This session gives an introduction to machine learning for developers who are new to data science, and it shows how to build end-to-end MLlib Pipelines in Apache Spark. It provides example code to personalize recommendations, score inbound leads, or do natural language processing in Scala and Python. See how to productionize machine learning pipelines to create richer, more useful applications.
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.
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: 221 PixelConference
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: 44071 educause
Learning Data Mining with R : Data Points & Distn in a Multidimensional Vector Space | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2lXhDAx]. We want to explain that data is nothing but points in a multidimensional vector space exemplified by an example. • Learn how to transform a table into a multi-dimensional vector space • Learn about distance measures and multidimensional vector spaces For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 164 Packt Video
Mining Social Media Data for Understanding Students’ Learning Experiences
 
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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 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.
Statistics intro: Mean, median, and mode | Data and statistics | 6th grade | Khan Academy
 
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This is a fantastic intro to the basics of statistics. Our focus here is to help you understand the core concepts of arithmetic mean, median, and mode. Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/mean-and-median/e/calculating-the-mean?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Watch the next lesson: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/mean-and-median/v/mean-median-and-mode?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Missed the previous lesson? https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/histograms/v/interpreting-histograms?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Grade 6th on Khan Academy: By the 6th grade, you're becoming a sophisticated mathemagician. You'll be able to add, subtract, multiply, and divide any non-negative numbers (including decimals and fractions) that any grumpy ogre throws at you. Mind-blowing ideas like exponents (you saw these briefly in the 5th grade), ratios, percents, negative numbers, and variable expressions will start being in your comfort zone. Most importantly, the algebraic side of mathematics is a whole new kind of fun! And if that is not enough, we are going to continue with our understanding of ideas like the coordinate plane (from 5th grade) and area while beginning to derive meaning from data! (Content was selected for this grade level based on a typical curriculum in the United States.) About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to Khan Academy‰Ûªs 6th grade channel: https://www.youtube.com/channel/UCnif494Ay2S-PuYlDVrOwYQ?sub_confirmation=1 Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 1833454 Khan Academy
One Platform for Your Functions, Applications, and Containers (Cloud Next '18)
 
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Come learn how you can unify function, application, and container developer workflows under a single API, making you more productive and your system easier to manage. Event schedule → http://g.co/next18 Watch more Application Development sessions here → http://bit.ly/2zMcTJc Next ‘18 All Sessions playlist → http://bit.ly/Allsessions Subscribe to the Google Cloud channel! → http://bit.ly/NextSub
Views: 1872 Google Cloud Platform
The future will be decentralized | Charles Hoskinson | TEDxBermuda
 
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This talk was given at a local TEDx event, produced independently of the TED Conferences. Tech entrepreneur and mathematician Charles Hoskinson says Bitcoin-related technology is about to revolutionise property rights, banking, remote education, private law and crowd-funding for the developing world. Charles Hoskinson is Chief Executive Officer at Thanatos Holdings, Director at The Bitcoin Education Project, and President at the Hoskinson Content Group LLC. Charles is a Colorado based technology entrepreneur and mathematician. He attended University of Colorado, Boulder to study analytic number theory in graduate school before moving into cryptography and social network theory. His professional experience includes work with NoSQL and Bigdata using MongoDB and Hadoop for several data mining projects involving crowdsource research and also development of web spiders. He is the author of several white papers on the design and deployment of low bandwidth prolog based semantical web scraping bots as well as analysis of metamorphic computer viruses through a case study on Zmist. His current projects focus on evangelism and education for Bitcoin and fully homomorphic encryption schemes. About TEDx, x = independently organized event In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)
Views: 279676 TEDx Talks
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: 1058 CACI
An Online Hierarchical Algorithm for Extreme Clustering
 
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An Online Hierarchical Algorithm for Extreme Clustering Ari Kobren (University of Massachusetts Amherst) Nicholas Monath (University of Massachusetts Amherst) Akshay Krishnamurthy (University of Massachusetts Amherst) Andrew McCallum (University of Massachusetts Amherst) Many modern clustering methods scale well to a large number of data items, N, but not to a large number of clusters, K. This paper introduces PERCH, a new non-greedy algorithm for online hierarchical clustering that scales to both massive N and K-a problem setting we term extreme clustering. Our algorithm efficiently routes new data points to the leaves of an incrementally-built tree. Motivated by the desire for both accuracy and speed, our approach performs tree rotations for the sake of enhancing subtree purity and encouraging balancedness. We prove that, under a natural separability assumption, our non-greedy algorithm will produce trees with perfect dendrogram purity regardless of online data arrival order. Our experiments demonstrate that PERCH constructs more accurate trees than other tree-building clustering algorithms and scales well with both N and K, achieving a higher quality clustering than the strongest flat clustering competitor in nearly half the time. More on http://www.kdd.org/kdd2017/
Views: 1021 KDD2017 video
Chi-squared Test
 
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Paul Andersen shows you how to calculate the ch-squared value to test your null hypothesis. He explains the importance of the critical value and defines the degrees of freedom. He also leaves you with a problem related to the animal behavior lab. This analysis is required in the AP Biology classroom. Intro Music Atribution Title: I4dsong_loop_main.wav Artist: CosmicD Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/ Creative Commons Atribution License
Views: 1272644 Bozeman Science
Using Topological Data Analysis on your BigData
 
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Synopsis: Topological Data Analysis (TDA) is a framework for data analysis and machine learning and represents a breakthrough in how to effectively use geometric and topological information to solve 'Big Data' problems. TDA provides meaningful summaries (in a technical sense to be described) and insights into complex data problems. In this talk, Anthony will begin with an overview of TDA and describe the core algorithm that is utilized. This talk will include both the theory and real world problems that have been solved using TDA. After this talk, attendees will understand how the underlying TDA algorithm works and how it improves on existing "classical" data analysis techniques as well as how it provides a framework for many machine learning algorithms and tasks. Speaker: Anthony Bak, Senior Data Scientist, Ayasdi (http://ayasdi.com) Prior to coming to Ayasdi, Anthony was at Stanford University where he did a postdoc with Ayasdi co-founder Gunnar Carlsson, working on new methods and applications of Topological Data Analysis. He completed his Ph.D. work in algebraic geometry with applications to string theory at the University of Pennsylvania and ,along the way, he worked at the Max Planck Institute in Germany, Mount Holyoke College in Germany, and the American Institute of Mathematics in California. Thanks to our Sponsors Microsoft [ http://microsoftnewengland.com ] for providing awesome venue for the event. Ayasdi [ http://ayasdi.com ] for providing the food/drinks. cognizeus [ http://cognizeus.com ] for hosting the event and providing books to give away as raffle.
Views: 4859 AnalyticsWeek
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/
Large linear classification when data cannot fit in memory
 
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Recent advances in linear classification have shown that for applications such as document classification, the training can be extremely efficient. However, most of the existing training methods are designed by assuming that data can be stored in the computer memory. These methods cannot be easily applied to data larger than the memory capacity due to the random access to the disk. We propose and analyze a block minimization framework for data larger than the memory size. At each step a block of data is loaded from the disk and handled by certain learning methods. We investigate two implementations of the proposed framework for primal and dual SVMs, respectively. As data cannot fit in memory, many design considerations are very different from those for traditional algorithms. Experiments using data sets 20 times larger than the memory demonstrate the effectiveness of the proposed method.
Views: 365 kchang10uiuc
Common Core - Data Mining Concerns
 
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A Colorado Teacher Jennifer, Shares her knowledge of and concern over Federal Testing aka PARCC designed for Common Core Standards. Video #3
Views: 349 Jack Matthews
Scalable Learning of Graphical Models (Part 1)
 
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Authors: Geoff Webb, Faculty of Information Technology, Monash University François Petitjean, Faculty of Information Technology, Monash University Abstract: From understanding the structure of data, to classification and topic modeling, graphical models are core tools in machine learning and data mining. They combine probability and graph theories to form a compact representation of probability distributions. In the last decade, as data stores became larger and higher-dimensional, traditional algorithms for learning graphical models from data, with their lack of scalability, became less and less usable, thus directly decreasing the potential benefits of this core technology. To scale graphical modeling techniques to the size and dimensionality of most modern data stores, data science researchers and practitioners now have to meld the most recent advances in numerous specialized fields including graph theory, statistics, pattern mining and graphical modeling. This tutorial covers the core building blocks that are necessary to build and use scalable graphical modeling technologies on large and high-dimensional data. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 189 KDD2016 video
▶️$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: 3703 RANDOMFIX
ERPEM 2014 - "High Dimensional Estimation: from foundations to Econometric models"  - Aula 01
 
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ERPEM 2014 - Minicourse: "High Dimensional Estimation: from foundations to Econometric models" Professor: Alexandre Belloni Aula 01 - 10/11/2014 Página: http://www.impa.br/opencms/en/eventos/store_old/evento_1412 Download dos vídeos: http://video.impa.br/index.php?page=erpem-2014 Abstract: In this mini-course we start with the foundations of modern statistical techniques based on L1-penalization for high dimensional estimation under sparsity assumptions. We will cover rates of convergence for Lasso, sparsity bounds on the selected model, and simple lower bounds on its performance. We then will shift our interests to how further develop these ideas on models that are motivated by Econometric applications. For example, heteroskedastic errors, logistic regression, conditional quantiles, and error-in-variables. Some emphasis will be placed on properly handling the different assumptions induced by the (econometric) data generating process (e.g. approximate sparse models and non- Gaussian errors). We will finish the mini-course by establishing results on the uniform validity of confidence regions (e.g. confidence intervals) for parameters. We will attempt to cover partially linear models, instrumental variables, and Z-estimators. IMPA - Instituto Nacional de Matemática Pura e Aplicada © http://www.impa.br | http://video.impa.br
Privacy-Preserving Distributed Multi-Task Learning with Asynchronous Updates
 
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Liyang Xie (Michigan State University) Inci Baytas (Michigan State University) Kaixiang Lin (Michigan State University) Jiayu Zhou (Michigan State University) Many data mining applications involve a set of related learning tasks. Multi-task learning (MTL) is a learning paradigm that improves generalization performance by transferring knowledge among those tasks. MTL has attracted so much attention in the community, and various algorithms have been successfully developed. Recently, distributed MTL has also been studied for related tasks whose data is distributed across different geographical regions. One prominent challenge of the distributed MTL frameworks is to maintain the privacy of the data. The distributed data may contain sensitive and private information such as patient records and registers of a company. In such cases, distributed MTL frameworks are required to preserve the privacy of the data. In this paper, we propose a novel privacy-preserving distributed MTL framework to address this challenge. A privacy-preserving proximal gradient algorithm, which asynchronously updates models of the learning tasks, is introduced to solve a general class of MTL formulations. The proposed asynchronous approach is robust against network delays and provides a guaranteed differential privacy through carefully designed perturbation. Theoretical guarantees of the proposed algorithm are derived and supported by the extensive experimental results. More on http://www.kdd.org/kdd2017/.
Views: 654 KDD2017 video
Analysis of Big Multidimensional Data
 
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Google Tech Talks June 1, 2007 ABSTRACT Big two dimensional data as photos or spreadsheets are very common in applications. Higher dimensional data as three dimensional picture, or picture with voices, or profile of a person from several angles lead to a higher dimensional data. Usually this data has a lot of redundancy, has noise and may miss the information at all in certain percentage of data. In this talk I will discuss the following general problems: * Reduction process which reduces the storage space of the data * Denoising the data. * Predicting the values of the missing data For the two dimensional data the singular value decomposition (SVD) of an m x n matrix emerged as a very...
Views: 2273 GoogleTechTalks
CAREERS IN CSE –COMPUTER SCIENCE ENGINEERING,GATE,Software Jobs,MBA,MTech
 
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CSE-COMPUTER SCIENCE ENGINEERING CAREERS. Go through the career opportunities of CSE, Govt jobs and Employment News channel from Freshersworld.com – The No.1 job portal for freshers in India. Visit http://www.freshersworld.com for detailed job information, campus recruitment GATE notification, GATE pattern, higher education details of CSE- COMPUTER SCIENCE ENGINEERING. Computer Science engineering deals with design, implementation, management of information system of both software and hardware processes. Computer Science engineers are involved in developing the software applications, testing and debugging the code , deployment of software and designing and modification of the component. They use different software to store and manage data in a secured manner. Every computer science engineer will have a basic knowledge about the programming languages like c,c++,java, python etc.. ,they will have problem solving skills, logical skills, data mining, knowledge in artificial intelligence, different algorithms and current technologies which are existing and used by the enterprises. Apart from this there are various domain specifications as well, like ruby on rails, agile, php, etc. Thus Computer science engineers after completion of the degree, has got engrossing and challenging oppurtunities available when compared to the other fields of engineering. One can work in database management, IT, embedded systems, Telecommunication, computer hardware & software implementation & maintenance, multimedia, web designing, gaming, and almost all other industries in this sector. It is worthwhile to note that the computer industry has witnessed such phenomenal growth in recent years that IT majors like Infosys & TCS have been the major recruiters across all other branches throughout the engineering colleges in the country. • When it comes to job, we have competitive exams like GATE exam which provide a CTC upto 8.00+lacs and also other firms like TCS • Infosys • Wipro • HCL • Accenture • Cognizant • Microsoft • IBM • Adobe • Google • Accenture • Cisco • Oracle • Sun Microsystems • Yahoo • Tech Mahindra • Mahindra Satyam • Toshiba, • Amazon, etc,.recruits a huge number of employees every year with eye-catching pacakages. (masters in computer science ) Those who are interested in pursuing higher ducation after bachelor’s have vast choices like Msc in data mining, artificial intelligence, distribution system, computer design, human-computer interaction, visual computing, software development, robot designing etc,. The degree will be worth for those who have a desire towards programming and other cutting edge technologies. Visit Preparation and placement tips for IT jobs at: http://placement.freshersworld.com?src=Youtube For more jobs & career information and daily job alerts, subscribe to our channel and support us. You can also install our Mobile app for govt jobs for getting regular notifications on your mobile. Freshersworld.com is the No.1 job portal for freshers jobs in India. Check Out website for more Jobs & Careers. http://www.freshersworld.com?src=Youtube - - ***Disclaimer: This is just a career guidance video for fresher candidates. The name, logo and properties mentioned in the video are proprietary property of the respective companies. The career and job information mentioned are an indicative generalised information. In no way Freshersworld.com, indulges into direct or indirect recruitment process of the respective companies.
Mining Reliable Information from Passively and Actively Crowdsourced Data (Part 1)
 
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Authors: Jiawei Han, Department of Computer Science, University of Illinois at Urbana-Champaign Wei Fan, Baidu, Inc. Bo Zhao, LinkedIn Corporation Qi Li, Department of Computer Science and Engineering, University at Buffalo Jing Gao, Department of Computer Science and Engineering, University at Buffalo Abstract: Recent years have witnessed an astonishing growth of crowd-contributed data, which has become a powerful information source that covers almost every aspect of our lives. This big treasure trove of information has fundamentally changed the ways in which we learn about our world. Crowdsourcing has attracted considerable attentions with various approaches developed to utilize these enormous crowdsourced data from different perspectives. From the data collection perspective, crowdsourced data can be divided into two types: "passively" crowdsourced data and "actively" crowdsourced data; from task perspective, crowdsourcing research includes information aggregation, budget allocation, worker incentive mechanism, etc. To answer the need of a systematic introduction of the field and comparison of the techniques, we will present an organized picture on crowdsourcing methods in this tutorial. The covered topics will be interested for both advanced researchers and beginners in this field. More on http://www.kdd.org/kdd2016/ KDD2016 conference is published on http://videolectures.net/
Views: 153 KDD2016 video
Advances in Regularization: Bridge Regression and Coordinate Algorithms by Giovanni Seni  20120604
 
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http://www.sfbayacm.org/event/advances-regularization-bridge-regression-and-coordinate-descent-algorithms Speaker: Giovanni Seni A widely held principle in Statistical model inference is that accuracy and simplicity are both desirable. But there is a tradeoff between the two: a flexible (more complex) model is often needed to achieve higher accuracy, but it is more susceptible to overfitting and less likely to generalize well. Regularization techniques "damp down" the flexibility of a model fitting procedure by augmenting the error function with a term that penalizes model complexity. Minimizing the augmented error criterion requires a certain increase in accuracy to "pay" for the increase in model complexity (e.g., adding another term to the model). This talk offers a concise introduction to this topic and a review of recent developments leading to very fast algorithms for parameter estimation with various types of penalties. It concludes with an example in R, showing an application of the techniques to a document classification task with 1-Million predictors. Speaker Bio Giovanni Seni is currently a Senior Data Scientist with Intuit. As an active data mining practitioner in Silicon Valley, he has over 15 years R&D experience in statistical pattern recognition, data mining, and human-computer interaction applications. He has been a member of the technical staff at large technology companies, and a contributor at smaller organizations. He holds five US patents and has published over twenty conference and journal articles. His book with John Elder, "Ensemble Methods in Data Mining - Improving accuracy through combining predictions", was published in February 2010 by Morgan & Claypool. Giovanni is also an adjunct faculty at the Computer Engineering Department of Santa Clara University, where he teaches an Introduction to Pattern Recognition and Data Mining class.
Career in BTECH(CSE),Computer Science and ENGINEERING
 
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CSE-COMPUTER SCIENCE ENGINEERING Hii Hii friends In this video we know about CAREERS. Go through the career opportunities of CSE, campus recruitment GATE notification, GATE pattern, higher education details of CSE- COMPUTER SCIENCE ENGINEERING. Computer Science engineering deals with design, implementation, management of information system of both software and hardware processes. Computer Science engineers are involved in developing the software applications, testing and debugging the code , deployment of software and designing and modification of the component. They use different software to store and manage data in a secured manner. Every computer science engineer will have a basic knowledge about the programming languages like c,c++,java, python etc.. ,they will have problem solving skills, logical skills, data mining, knowledge in artificial intelligence, different algorithms and current technologies which are existing and used by the enterprises. Apart from this there are various domain specifications as well, like ruby on rails, agile, php, etc. Thus Computer science engineers after completion of the degree, has got engrossing and challenging oppurtunities available when compared to the other fields of engineering. One can work in database management, IT, embedded systems, Telecommunication, computer hardware & software implementation & maintenance, multimedia, web designing, gaming, and almost all other industries in this sector. It is worthwhile to note that the computer industry has witnessed such phenomenal growth in recent years that IT majors like Infosys & TCS have been the major recruiters across all other branches throughout the engineering colleges in the country. • When it comes to job, we have competitive exams like GATE exam which provide a CTC upto 8.00+lacs and also other firms like TCS • Infosys • Wipro • HCL • Accenture • Cognizant • Microsoft • IBM • Adobe • Google • Accenture • Cisco • Oracle • Sun Microsystems • Yahoo • Tech Mahindra • Mahindra Satyam • Toshiba, • Amazon, etc,.recruits a huge number of employees every year with eye-catching pacakages. (masters in computer science ) Those who are interested in pursuing higher ducation after bachelor’s have vast choices like Msc in data mining, artificial intelligence, distribution system, computer design, human-computer interaction, visual computing, software development, robot designing etc,. The degree will be worth for those who have a desire towards programming and other cutting edge technologie If you like my video so please,SUBSCRIBE, SHARE, LIKE,and COMMENT THANKS FOR WATCHING BHARAT MATA KI JAI Video produce -Ramkrishan singh ALL TYPES OF KNOWLEDGE
Views: 68867 ALL TYPES OF KNOWLEDGE