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Data Mining for Educational Researchers
 
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Chris Brooks
Views: 151 LINK Lab
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: 317 Salford Systems
LASI 2015 Bilbao - Applications of Social Data Mining to Learning Analytics
 
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LASI 2015 Bilbao: Learning Analytics Summer Institute. 22-23 junio 2015. Experiences Session, Kais Dai.
Data mining
 
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Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amount of data, not the extraction of data itself. It also is a buzzword, and is frequently also applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The popular book "Data mining: Practical machine learning tools and techniques with Java" (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis", or "analytics" -- or when referring to actual methods, artificial intelligence and machine learning -- are more appropriate. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 1696 Audiopedia
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: 239 Pixel Conferences
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
Intro to Data Analysis / Visualization with Python, Matplotlib and Pandas | Matplotlib Tutorial
 
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Python data analysis / data science tutorial. Let’s go! For more videos like this, I’d recommend my course here: https://www.csdojo.io/moredata Sample data and sample code: https://www.csdojo.io/data My explanation about Jupyter Notebook and Anaconda: https://bit.ly/2JAtjF8 Also, keep in touch on Twitter: https://twitter.com/ykdojo And Facebook: https://www.facebook.com/entercsdojo Outline - check the comment section for a clickable version: 0:37: Why data visualization? 1:05: Why Python? 1:39: Why Matplotlib? 2:23: Installing Jupyter through Anaconda 3:20: Launching Jupyter 3:41: DEMO begins: create a folder and download data 4:27: Create a new Jupyter Notebook file 5:09: Importing libraries 6:04: Simple examples of how to use Matplotlib / Pyplot 7:21: Plotting multiple lines 8:46: Importing data from a CSV file 10:46: Plotting data you’ve imported 13:19: Using a third argument in the plot() function 13:42: A real analysis with a real data set - loading data 14:49: Isolating the data for the U.S. and China 16:29: Plotting US and China’s population growth 18:22: Comparing relative growths instead of the absolute amount 21:21: About how to get more videos like this - it’s at https://www.csdojo.io/moredata
Views: 259599 CS Dojo
What is Data Mining?
 
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I Have No Intention To Claim The Ownership Of This Video All Credits To The Owner Of This Video! This Has Been Upload For Educational Purpose Only. Please Do Not Take Down This Channel! If You Do Not Agree Please Message Me So That I Can Delete The Video! Thank You Very Much! Original Video Link: https://www.youtube.com/watch?v=R-sGvh6tI04 Data mining is the computing process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.[1] It is an interdisciplinary subfield of computer science.[1][2][3] The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.[1] Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.[1] Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.[4]The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself.[5] It also is a buzzword[6] and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java[7] (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons.[8] Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate.The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps.The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations. Lets Connect: Twitter: https://twitter.com/BLAmedia1 Google+: https://plus.google.com/115816603020714793797 Facebook: https://www.facebook.com/BLAmedia-1884144591836064 LinkedIn: https://www.linkedin.com/in/blamedia
Views: 19 Pedro Puerto
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
What future for Big Data mining?
 
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Policymakers are showing growing interest for real-time analysis of public opinion and Big Data. From finance to political campaigners, social media have become a primary source of information, especially when it comes to understanding public opinion trends. However, the potential of social media still needs to be fully exploited. With the explosion of structured and unstructured Big Data, the ability to harness information has become paramount for those who want to successfully use information originating from social media. On the regulatory side, the European Commission wants to promote the data-driven economy as part of its Digital Single Market strategy. The strategy includes better online access and digitalisation as a driver for growth.
Views: 946 SSIX Project
Weka Data Mining Tutorial for First Time & Beginner Users
 
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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 463920 Brandon Weinberg
Lorena Barba: Keynote - Data driven Education and the Quantified Student
 
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PyData Seattle 2015 Education has seen the rise of a new trend in the last few years: Learning Analytics. This talk will weave through the complex interacting issues and concerns involving learning analytics, at a high level. The goal is to whet the appetite and motivate reflection on how data scientists can work with educators and learning scientists in this swelling field. Higher education has used analytics for a long time to guide administrative decisions. Universities are already adept at developing data-driven admissions strategies and increasingly they are using analytics in fund-raising. Learning analytics is a newer trend. Its core goal is to improve teaching, learning and student success through data. This is very appealing, but it's also fraught with complex interactions among many concerns and with disciplinary gaps between the various players. Faculty have always collected data on students' performance on assessments and responses on surveys for the purposes of grading and complying with accreditation, sometimes also for improving teaching methods and more rarely for research on how students learn. To call it Learning Analytics, though, requires scale and some form of systemic effort. Some early university efforts in analytics developed predictive models to identify at-risk first-year students, aiming to improve freshman retention (e.g., Purdue's "Signals" project). Others built alert systems in support of student advising, with the goal of increasing graduation rates (e.g., Arizona State University's "eAdvisor" system). Experts now segregate these efforts out of learning analytics, proper, because retention and graduation are not the same as learning. The goal, in that case, is to improve the function of the educational system, while learning analytics should be guided by educational research and be aimed at enhancing learning. To elucidate what is learning analytics, it looks like we first need to answer: what is learning? What is knowledge? And can more data lead to better learning? That is perhaps the zeroth assumption of learning analytics—and it needs to be tested. There are assumptions behind any data system that go as far back as selecting what to track, where it will be tracked, how it will be collected, stored and delivered. Most analytics is based on log data in the Learning Management System (LMS). This "learning in a box" model is inadequate, but the diverse ecosystem of apps and services used by faculty and students poses a huge interoperability problem. The billion-dollar education industry of LMS platforms, textbook publishers and testing companies all want a part in the prospect of "changing education" through analytics. They're all marketing their dazzling dashboards in a worrying wave of ed-tech solutionism. Meanwhile, students' every move gets tracked and logged, often without their knowledge or consent, adding ethical and legal issues of privacy for the quantified student. Slides available here: http://figshare.com/articles/Data_driven_Education_and_the_Quantified_Student/1495511
Views: 2897 PyData
Prof Ryan Baker, Data Science in Education, Part 1
 
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In this talk, Prof. Baker discusses how the methods of educational data mining draw from broader trends in data science, and some of the problems and methods more specific to education research. Throughout the talk, Baker discusses both the current state of the art in educational data mining, and some of the key research challenges and opportunities for data scientists working in this emerging area.
Views: 272 Shirin Mojarad
Student's t-test
 
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Excel file: https://dl.dropboxusercontent.com/u/561402/TTEST.xls In this video Paul Andersen explains how to run the student's t-test on a set of data. He starts by explaining conceptually how a t-value can be used to determine the statistical difference between two samples. He then shows you how to use a t-test to test the null hypothesis. He finally gives you a separate data set that can be used to practice running the test. Do you speak another language? Help me translate my videos: http://www.bozemanscience.com/translations/ Music Attribution Intro Title: I4dsong_loop_main.wav Artist: CosmicD Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/ Creative Commons Atribution License Outro Title: String Theory Artist: Herman Jolly http://sunsetvalley.bandcamp.com/track/string-theory All of the images are licensed under creative commons and public domain licensing: 1.3.6.7.2. Critical Values of the Student’s-t Distribution. (n.d.). Retrieved April 12, 2016, from http://www.itl.nist.gov/div898/handbook/eda/section3/eda3672.htm File:Hordeum-barley.jpg - Wikimedia Commons. (n.d.). Retrieved April 11, 2016, from https://commons.wikimedia.org/wiki/File:Hordeum-barley.jpg Keinänen, S. (2005). English: Guinness for strenght. Retrieved from https://commons.wikimedia.org/wiki/File:Guinness.jpg Kirton, L. (2007). English: Footpath through barley field. A well defined and well used footpath through the fields at Nuthall. Retrieved from https://commons.wikimedia.org/wiki/File:Footpath_through_barley_field_-_geograph.org.uk_-_451384.jpg pl.wikipedia, U. W. on. ([object HTMLTableCellElement]). English: William Sealy Gosset, known as “Student”, British statistician. Picture taken in 1908. Retrieved from https://commons.wikimedia.org/wiki/File:William_Sealy_Gosset.jpg The T-Test. (n.d.). Retrieved April 12, 2016, from http://www.socialresearchmethods.net/kb/stat_t.php
Views: 516865 Bozeman Science
Some Thoughts on Privacy and Security for Educational Data by Ryan Baker
 
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Ryan Baker from the University of Pennsylvania presents his talk for the DIMACS/Northeast Big Data Hub Workshop on Privacy and Security for Big Data April 24 - 25, 2017 DIMACS Center, CoRE Building, Rutgers University Organizing Committee: René Bastón, Columbia University Joseph Lorenzo Hall, The Center for Democracy and Technology Adam Smith, Pennsylvania State University Sean Smith, Dartmouth College Rebecca Wright, Rutgers University Moti Yung, Snapchat Presented under the auspices of the DIMACS Big Data Initiative on Privacy and Security, the DIMACS Special Focus on Cybersecurity and in collaboration with the Northeast Big Data Innovation Hub. http://dimacs.rutgers.edu/Workshops/BigDataHub/program.html
Views: 50 Rutgers University
Big Data and Machine Learning Hit Education @ TransformingEDU CES 2017
 
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Predictive analytics is really making its mark in education. Educators and administrators can leverage the latest in machine learning and brain science for a more personalized student experience. This session covers the importance of Big Data in education.
Views: 244 Cerego
Scales of Measurement - Nominal, Ordinal, Interval, Ratio (Part 1) - Introductory Statistics
 
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This video reviews the scales of measurement covered in introductory statistics: nominal, ordinal, interval, and ratio (Part 1 of 2). Scales of Measurement Nominal, Ordinal, Interval, Ratio YouTube Channel: https://www.youtube.com/user/statisticsinstructor Subscribe today! Lifetime access to SPSS videos: http://tinyurl.com/m2532td Video Transcript: In this video we'll take a look at what are known as the scales of measurement. OK first of all measurement can be defined as the process of applying numbers to objects according to a set of rules. So when we measure something we apply numbers or we give numbers to something and this something is just generically an object or objects so we're assigning numbers to some thing or things and when we do that we follow some sort of rules. Now in terms of introductory statistics textbooks there are four scales of measurement nominal, ordinal, interval, and ratio. We'll take a look at each of these in turn and take a look at some examples as well, as the examples really help to differentiate between these four scales. First we'll take a look at nominal. Now in a nominal scale of measurement we assign numbers to objects where the different numbers indicate different objects. The numbers have no real meaning other than differentiating between objects. So as an example a very common variable in statistical analyses is gender where in this example all males get a 1 and all females get a 2. Now the reason why this is nominal is because we could have just as easily assigned females a 1 and males a 2 or we could have assigned females 500 and males 650. It doesn't matter what number we come up with as long as all males get the same number, 1 in this example, and all females get the same number, 2. It doesn't mean that because females have a higher number that they're better than males or males are worse than females or vice versa or anything like that. All it does is it differentiates between our two groups. And that's a classic nominal example. Another one is baseball uniform numbers. Now the number that a player has on their uniform in baseball it provides no insight into the player's position or anything like that it just simply differentiates between players. So if someone has the number 23 on their back and someone has the number 25 it doesn't mean that the person who has 25 is better, has a higher average, hits more home runs, or anything like that it just means they're not the same playeras number 23. So in this example its nominal once again because the number just simply differentiates between objects. Now just as a side note in all sports it's not the same like in football for example different sequences of numbers typically go towards different positions. Like linebackers will have numbers that are different than quarterbacks and so forth but that's not the case in baseball. So in baseball whatever the number is it provides typically no insight into what position he plays. OK next we have ordinal and for ordinal we assign numbers to objects just like nominal but here the numbers also have meaningful order. So for example the place someone finishes in a race first, second, third, and so on. If we know the place that they finished we know how they did relative to others. So for example the first place person did better than second, second did better than third, and so on of course right that's obvious but that number that they're assigned one, two, or three indicates how they finished in a race so it indicates order and same thing with the place finished in an election first, second, third, fourth we know exactly how they did in relation to the others the person who finished in third place did better than someone who finished in fifth let's say if there are that many people, first did better than third and so on. So the number for ordinal once again indicates placement or order so we can rank people with ordinal data. OK next we have interval. In interval numbers have order just like ordinal so you can see here how these scales of measurement build on one another but in addition to ordinal, interval also has equal intervals between adjacent categories and I'll show you what I mean here with an example. So if we take temperature in degrees Fahrenheit the difference between 78 degrees and 79 degrees or that one degree difference is the same as the difference between 45 degrees and 46 degrees. One degree difference once again. So anywhere along that scale up and down the Fahrenheit scale that one degree difference means the same thing all up and down that scale. OK so if we take eight degrees versus nine degrees the difference there is one degree once again. That's a classic interval scale right there with those differences are meaningful and we'll contrast this with ordinal in just a few moments but finally before we do let's take a look at ratio.
Views: 369584 Quantitative Specialists
Implementing and Training Predictive Customer Lifetime Value Models in Python
 
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Implementing and Training Predictive Customer Lifetime Value Models in Python by Jean-Rene Gauthier, Ben Van Dyke Customer lifetime value models (CLVs) are powerful predictive models that allow analysts and data scientists to forecast how much customers are worth to a business. CLV models provide crucial inputs to inform marketing acquisition decisions, retention measures, customer care queuing, demand forecasting, etc. They are used and applied in a variety of verticals, including retail, gaming, and telecom. This tutorial is separated into two parts: In the first part, we will provide a brief overview of the ins and outs of probabilistic models, which can be used to quantify the future value of a customer, and demonstrate how e-commerce companies are using the outputs of these models to identify, retain, and target high-value customers. In the second part, we will implement, train, and validate predictive customer lifetime value models in a hands-on Python tutorial. Throughout the tutorial, we will use a real-world retail dataset and go over all the steps necessary to build a reliable customer lifetime value model: data exploration, feature engineering, model implementation, training, and validation. We will also use some of the probabilistic programming language packages available in Python (e.g. Stan, PyMC) to train these models. The resulting Python notebooks will lay out the foundation for more advanced models tailored to the specifics of each business setting. Throughout the tutorial, we will give the audience additional tips on how to tweak the models to fit different business settings. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 14180 PyData
International Journal of Data Mining & Knowledge Management Process (IJDKP)
 
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International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data Mining Foundations Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining Data Mining Applications Databases, Bioinformatics, Biometrics, Image Analysis, Financial Mmodeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining Knowledge Processing Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources Paper submission Authors are invited to submit papers for this journal through e-mail [email protected] . Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
Views: 179 ijdkp jou
K-Means Clustering Algorithm - Cluster Analysis | Machine Learning Algorithm | Data Science |Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4 Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #kmeans #clusteranalysis #clustering #datascience #machinelearning How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 69280 edureka!
Decision Tree Tutorial in 7 minutes with Decision Tree Analysis & Decision Tree Example (Basic)
 
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Clicked here http://www.MBAbullshit.com/ and OMG wow! I'm SHOCKED how easy.. No wonder others goin crazy sharing this??? Share it with your other friends too! Fun MBAbullshit.com is filled with easy quick video tutorial reviews on topics for MBA, BBA, and business college students on lots of topics from Finance or Financial Management, Quantitative Analysis, Managerial Economics, Strategic Management, Accounting, and many others. Cut through the bullshit to understand MBA!(Coming soon!) http://www.youtube.com/watch?v=a5yWr1hr6QY
Views: 557590 MBAbullshitDotCom
Machine Learning For Traders - An Introduction To Classification
 
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In the previous video of this series (https://youtu.be/oG4ks-9zYn8) we learnt how to import Python libraries. In this video, we will discuss the following points: - Introduction to Classification - Application in various fields such as: - Medical Diagnosis - Fraud detection - Handwriting recognition - Customer segmentation - Risk assessment - An example of a Classification used by E-Commerce websites - It is not restricted to text and numbers, even images can be classified. - Supervised classifier algorithms - The classifier algorithms can be chosen, depending on - Size of training data - Independence of features set - System speed The classifier algorithms covered are: - K-Nearest Neighbours Algorithm (KNN) - Random Forests Using Decision Trees - Artificial Neural Networks (ANN) - Naive Bayes Classification If you want to master the whole course: Trading With Machine Learning: Classification and SVM. Head on over to the following link: https://quantra.quantinsti.com/course/trading-machine-learning-classification-svm If you want to learn more about machine learning for free, check out the following link: https://quantra.quantinsti.com/course/introduction-to-machine-learning-for-trading QuantInsti® is one of the pioneer algorithmic trading research and training institutes across the globe. With its educational initiatives, QuantInsti is preparing financial market professionals for the contemporary field of algorithmic and quantitative trading. EPAT™ (Executive Programme in Algorithmic Trading) by QuantInsti is Asia’s first algorithmic trading education program. This comprehensive course exposes its participants to various strategy paradigms and enables them to build an algorithmic trading system. Quantra® is an e-learning portal by QuantInsti that specializes in short self-paced courses on algorithmic and quantitative trading. Quantra offers an interactive environment which supports ‘learning by doing’ through guided coding exercises, videos and presentations.
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
Machine Learning Knowledge Extraction MAKE it short
 
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MAKE stands for MAchine Learning & Knowledge Extraction. Machine learning deals with understanding intelligence for the design and development of algorithms that can learn from data and improve over time. The original definition was “the artificial generation of knowledge from experience”. The challenge is to discover relevant structural patterns and/or temporal patterns (“knowledge”) in such data, which are often hidden and not accessible to a human. Today, machine learning is the fastest growing technical field, having many application domains, e.g. health, Industry 4.0, recommender systems, speech recognition, autonomous driving (Google car), etc. The grand challenge is in decision making under uncertainty, and probabilistic inference enormously influenced artificial intelligence and statistical learning. The inverse probability allows to infer unknowns, learn from data and make predictions to support decision making. Whether in social networks, recommender systems, smart health or smart factory applications, the increasingly complex data sets require efficient, useful and useable intelligence for knowledge discovery and knowledge extraction. A synergistic combination of methodologies and approaches of two domains offer ideal conditions towards unraveling these challenges and to foster new, efficient and user-friendly machine learning algorithms and knowledge extraction tools: Human-Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), aiming at augmenting human intelligence with computational intelligence and vice versa. Successful Machine Learning & Knowledge extraction needs a concerted international effort without boundaries, supporting collaborative and integrative cross-disciplinary research between experts from 7 fields: in short: 1-data, 2-learning, 3-graphs, 4-topology, 5-entropy, 6-visualization, and 7-privacy; see http://hci-kdd.org/about-the-holzinger-group https://cd-make.net/about/ http://www.mdpi.com/journal/make/about Andreas Holzinger, 14.05.2017
Views: 1145 Andreas Holzinger
The end of privacy "The Data Brokers: Selling your personal information"
 
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The upload for mere educational and non-commercial use : http://www.cbsnews.com/news/the-data-brokers-selling-your-personal-information/ Part one: https://www.youtube.com/watch?v=qAT_ina93NY For more information: www.wsoctv.com/videos/news/9-investigates-data-brokers-selling-your-personal/vCRjDm/
Views: 63626 iBroadcastMedia
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: 1913061 Khan Academy
TMPA School 2018: Software Testing, Data Analysis and Machine Learning
 
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Exactpro’s latest educational project — TMPA School: Tools and Methods of Software Testing — will continue this fall in partnership with the Modeling and Control of Complex Systems lab of the Higher School of Economics. The TMPA School project launched on 2-4 March 2018 with over 200 participants. The geography of the fall events will expand, with the fall Schools taking place in two cities and expecting a ballpark of 600 students. Sponsored by Exactpro, the School aims to encourage the development of the IT industry in the region and the implementation of the cutting-edge research in software verification. The leading specialists in program engineering, software testing and verification come together to share their own experience and the most recent advances in the field. https://youtube.com/exactprosystems TMPA School - https://school.tmpaconf.org/ TMPA - https://tmpaconf.org/ Extent conference - https://extent.exactpro.com/ Exactpro website - https://exactpro.com/
Views: 176 Exactpro Systems
Sampling: Simple Random, Convenience, systematic, cluster, stratified - Statistics Help
 
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This video describes five common methods of sampling in data collection. Each has a helpful diagrammatic representation. You might like to read my blog: https://creativemaths.net/blog/
Views: 769898 Dr Nic's Maths and Stats
Extracting Structured Data from Legal Documents - Zack Witten
 
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PyData LA 2018 You’ll learn how to take a never-before-seen legal document, like a contract or a convertible note, and use machine learning to “read” the document and answer questions like “Who’s the investor” and “What interest rate did the parties agree to?” --- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 735 PyData
LASI 2015 Bilbao - Educational data analysis using R
 
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LASI 2015 Bilbao: Learning Analytics Summer Institute. 22-23 junio 2015. Workshop, Pedro J. Muñoz Merino.
Research Methodology Meaning Types Objectives [Hindi]
 
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Methodology is the systematic, theoretical analysis of the methods applied to a field of study. A research method is a systematic plan for conducting research. Sociologists draw on a variety of both qualitative and quantitative research methods, including experiments, survey research, participant observation, and secondary data.
Views: 150878 Manager Sahab
DATA MINING: Predicting Tipping Points: By Dr. Philip Gordon, PhD
 
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Tipping Points as evidenced in global events are, in many ways, influenced by media. DATA MINING for predicting and analyzing world events. This just released, ground-breaking book: DATA MINING: PREDICTING TIPPING POINTS by Dr. Philip Gordon, PhD, details three case studies which were selected on the basis of common Tipping Point Attributes: Each involved media contagiousness and stickiness during their development and, each arrived at a "dramatic moment in time", which could only be characterized by the phenomenon of Tipping Points. Three recent case studies explore the leading edge technologies of DATA MINING and the theory of TIPPING POINTS: The first case study, the 2008 Presidential Campaign of Barack Obama was chosen to examine a narrower scope and timeframe for the application of the analysis. In contrast to the second case study, the International Financial Crisis of 2007--2010, which involves a broader data study period to identify trends and more complex issues. The third study, Climate Change was included as consideration because the data mining research and analysis revealed critical relationships between Media Impact and Global Events. As the issue of Climate Change is still evolving, Dr. Gordon provides a Data Mining and Tipping Point Theory methodology for analyzing and predicting our planets' most pressing Global Tipping Points. Review Comments: "The genius of the formulation of DATA MINING: PREDICTING TIPPING POINTS is that it takes explicit account of the role of social media and the internet at facilitating bifurcations and promoting dynamical instability. In effect, we have trimmed a few feet of tail off the kite. As a reader, I was informed and educated as to the factors which conspire to influence stability / instability in complex social systems. ...the book does a good job of making sense of past bifurcations and dynamical instabilities, namely political instability, our perception of global climate change, and international economic crises...my compliments on a truly insightful Media Tipping Points." -Prof. Dr. (med.) Peter S. Geissler, A.B., B.S., M.S., M.Phil., Ph.D. (Yale) M.A., M.Eng., M.S., Ph.D., M.S., M.D., M.Phil.(Cantab) "A truly fascinating book that (teaches) a whole new way of thinking about major events and how the media can influence them. - Being a political junkie I was heavily into the media coverage of the 2008 Obama election and the global financial meltdown both via TV and the blogosphere. I now find myself looking for the tipping points and stickiness factors as other key events unfold. Usually, I have trouble reading theoretical books but this one was an easy read and if you want supporting data then the references are there. This could become a solid reference for those in the media who truly want to understand what they are reporting. Highly recommended and I look forward to Dr. Gordon's ongoing analysis of (future) events." -Dr. Ralph Moorhouse, Ph.D. Political junkie, Expert: natural polymers for industries "The application of Data Mining and Tipping Point Theory to media and global events, particularly the financial crisis and climate change, is a fascinating one." -Dr. Serge Besanger, PhD Expert, International Monetary Fund "...very interesting application (of the Tipping Point Theory)...potential opportunity for predicting other global events, i.e.: Egyptian crisis and perhaps, even terrorism activities." -Dr. Adam AJLANI, PhD Professor, Sciences Politic and Political Consultant, France TV1
Views: 275 BlueMatrixCatalog
4. What is Integration (Hindi) |
 
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What is Integration and need for it.
Views: 383558 Lighthouse
MSM Reveals Phony Outrage On Facebook Data-Mining
 
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Michael Knowles critiques the phony outrage coming from the media on the Facebook data-mining scandal with Cambridge Analytica.
Views: 2974 The Daily Wire
Towards Long-Term and Actionable Prediction of Student Outcomes (Ryan Baker)
 
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Ryan Baker (Associate Professor of Cognitive Studies at Teachers College, Columbia University) lectures on Long-Term and Actionable Prediction of Student Outcomes using Automated Detectors of Engagement and Affect (Delivered 9 February 2015) ABSTRACT: In recent years, researchers have been able to model an increasing range of aspects of student interaction with online learning environments, including affect, meta-cognition, robust learning, and engagement. In this talk, Ryan Baker discusses how automated detectors of engagement and learning can be used in prediction of long-term student outcomes, illustrating this with examples of how affect, engagement, and learning during middle school use of educational software can support prediction of student long-term success, including end-of-year learning, decisions about whether to attend college, and even what major a student chooses. These predictive models can in turn support inference about what factors make a specific student at-risk for poorer learning or lower long-term engagement in learning. This is the third lecture in the Institute of Quantitative Theory and Method's ( http://quantitative.emory.edu/ ) 2014-2015 Learning Analytics Speaker Series. For more information about the ways that educational data is being used at Emory University to promote student success, please visit https://scholarblogs.emory.edu/ale.
Views: 173 Emory University
Lecture 48 — Dimensionality Reduction with SVD | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Data Mining - Advanced Research Computing at U-M | Lectures On-Demand
 
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Brock Palen, Senior HPC Systems Administrator - CoE, 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 Students Through Common Core
 
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http://www.thenewamerican.com/culture/education/item/15213-data-mining-students-through-common-core Awareness is growing rapidly about the recent initiative to bring Common Core Standards to schools across America. Although the standards were supposedly proposed by the National Governors Association (NGA) and the Council of Chief State School Officers (CCSSO) — giving the illusion that the agenda is “state-led,” it was the federal government that endorsed the plan by offering $4 billion in grant money through Obama’s Race to the Top program to cooperating states. Representative Blaine Luetkemeyer (R-Mo.) recently decided to take action and write a letter to U.S. Department of Education Secretary Arne Duncan and is currently seeking co-signers from congressional colleagues. Congressman Luetkemeyer addressed several issues of concern with Common Core — and in the last half of his letter he emphasized the crux of the problem: data mining. “We understand that as a condition of applying for [Race to the Top] grant funding, states obligated themselves to implement a State Longitudinal Database System (SLDS) used to track students by obtaining personally identifiable information,” Luetkemeyer said. “We formally request a detailed description of each change to student privacy policy that has been made under your leadership, including the need and intended purpose for such changes.” Parents might reasonably assume that the “personally identifiable information” collected for the database will include students' test scores and perhaps other measures of academic proficiency. But they would be much less likely to imagine that the federal government envisions something far more extensive and invasive than merely tracking academic performance. According to the Department of Education’s February 2013 report Promoting Grit, Tenacity, and Perseverance: Critical Factors for Success in the 21st Century, “Researchers are exploring how to gather complex affective data and generate meaningful and usable information to feed back to learners, teachers, researchers, and the technology itself. Connections to neuroscience are also beginning to emerge.” (Emphasis added.)
Views: 122 VisionLiberty
"Brain + Data: How Neuroscience Can Increase Data Mining Effectiveness", Dr. Jonathan T. Mall
 
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"Brain + Data: How Neuroscience Can Increase Data Mining Effectiveness", Dr. Jonathan T. Mall, CEO of NeuroFlash Slides can be found here: http://bit.ly/2eXnS6a Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo Visit the conference website to learn more: www.datanatives.io Follow Data Natives: https://www.facebook.com/DataNatives https://twitter.com/DataNativesConf Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2016: http://bit.ly/1WMJAqS About the Author: Jonathan is a computational Neuropsychologist turned serial entrepreneur. Seduced by the opportunity to optimize consumer experience using machine learning, he led the Science team in a dutch IBM Big Data Venture (Gumbolt.com). Afterwards, he Co-Founded Neuro-Flash.com a market research institute, using online experiments that illuminate the true drivers of desire and purchase behaviour. When he’s not combining Neuroscience and Big-Data to test innovative ideas, he likes burgers and a friendly match of badminton.
Views: 466 Data Natives
How Big Data Can Influence Decisions That Actually Matter | Prukalpa Sankar | TEDxGateway
 
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Its crazy how big data is used to solve some kinds of problems and not others. Prukalpa Sankar reimagines a world where we can catch criminals at the scene of the crime – not years later, reroute cars in real time to prevent traffic congestion that we all hate so much, predict if a child is going to drop out of school before they even knows it and eradicate a disease as it breaks, not 1000s of deaths later. Prukalpa is the co-founder of SocialCops, a data intelligence company. Their platform brings the entire decision-making process to one place — from collecting primary data and accessing secondary data to merging internal data and visualizing data via easy-to-use dashboards. They work with over 150 organizations in 7+ countries, including the Gates Foundation, Tata Trusts, Government of India, Unilever, and Frost & Sullivan. SocialCops was on the 2016 Forbes Asia 30 Under 30 and Fortune India 40 Under 40 lists. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 179908 TEDx Talks
BITCOIN PASSES $5,400! - CME Bitcoin Futures Longs Up 88% - Ripple Xpring Bain Cap - Ethereum Dapps
 
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Get the Ledger Nano X to Safely store your Crypto - https://www.ledgerwallet.com/r/acd6 Help support the channel by joining my Patreon group - https://www.patreon.com/thinkingcrypto Sign up with Coinbase for Free and get $10 free Bitcoin when you spend $100! - https://www.coinbase.com/join/59db057bed984302ff3b1275 Easily purchase Altcoins such as XRP, Cardano and more on the Binance exchange - https://www.binance.com/?ref=2157551 Follow on Twitter - https://twitter.com/ThinkingCrypto1 Follow on Facebook - https://www.facebook.com/thinkingcrypto/ Website - http://www.ThinkingCrypto.com/ Follow on Steemit - https://steemit.com/@thinkingcrypto ================================================= Help support the channel! Donations : BTC - 3GPcKwB3UGML4UiYqZM6BYx7Nu5Dj7GKDD ETH - 0x7929e49cabe8d95d31392eaf974f378b508da2f4 LTC - MWMhsyGX7tsTPGS2EtSCAWpy3ywCv25r6B XRP - rDsbeomae4FXwgQTJp9Rs64Qg9vDiTCdBv Destination Tag - 35594196 ================================================= #Bitcoin #Ethereum #XRP - Bitcoin's price crossed over $5,400 today - Data from the US Commodity and Futures Trading Commission reveals that institutional investors flipped bullish on Bitcoin as of April 2nd. The date coincides with the latest bitcoin price rally when it soared from around $4,100 to more than $5,300 in minutes. Notably, 315 long Bitcoin futures contracts on CME’s platform were opened by April 2nd. This is a whopping 88 percent increase compared to the previous week. Moreover, the number of short positions saw a 63 percent decrease – from 241 to 89 contracts. - Japanese finance regulator the Financial Services Agency (FSA) no longer wishes to describe Bitcoin (BTC) as a virtual currency - A new bill wants to give the Federal Trade Commission $25M to go after crypto’s bad actors - The Philippines’ Central Bank Has Already Legalized 10 Bitcoin Exchanges - Bain Capital and Ripple’s Xpring Invest in DeFi Founder’s ‘Scout Fund’ - Worldwide Mobile Credit and Viber now can be top-ups with Ripple(XRP) at http://uquid.com . XRP is also used to: Pay online bills, Food vouchers, PIN-less call - Ethereum-based dapps dominate the market, note more inactive dapps than other blockchains - Coinbase Launches Crypto Visa Debit Card for UK and EU Customers - New York Rejects Bittrex Exchange’s BitLicense Application - $60 Million and Rising: China’s Crypto Funds Try Lending to Beat Bear Market ================================================= Disclaimer - Thinking Crypto and Tony are not financial or investment experts. You should do your own research on each cryptocurrency and make your own conclusions and decisions for investment. Invest at your own risk, only invest what you are willing to lose. This channel and its videos are just for educational purposes and NOT investment or financial advice. Bitcoin, Bitcoin News, Bitcoin ETF, XRP, XRP News, Ripple, Ripple XRP, Ripple News, Ethereum, Ethereum News, Litecoin, Litecoin News, Crypto, Crypto News, Token Taxonomy Act, Bakkt, Erisx, Fidelity Digital Assets, Cryptocurrency, Digital Assets
Views: 3566 Thinking Crypto
Market basket 2012 - TEAM GOLD
 
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The Market-Basket is a short-term project that is implemented in existing semesters, to allow non-mobile students a more global orientated learning experience. Initiated by lecturers of the Hogeschool van Amsterdam, Business Academy Aarhus and the University of Southern Indiana, it focuses on an international marketing comparison, and is performed by virtual student teams. This semester's International Market Basket Analysis Project offers a collaboration between student teams on products typically found on a breakfast table. The learning objectives of this collaboration include experiencing cross-cultural cooperation and getting a better understanding of the application of marketing principles to more than one country. This is done by informing each other and comparing a selected number of products. Over a limited period of time the Danish and Dutch teams work towards their papers. Afterwards a matching Danish multimedia design team will be responsible for presenting the results in the online market-basket museum, with a focus on storytelling. On the website of the project: http://marketbasketmuseum.org/ you can find scans of selected international articles on this project, giving you details on the educational internationalisation aspects.
Views: 57 CEngelgreen
MBA Business Analytics & Intelligence Management Career Opportunities Field Salary Colleges by Brain
 
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http://www.brainchecker.in MBA Business Analytics & Intelligence Management Career by BrainChecker Stay tuned for regular updates from BrainChecker Channel. We provide excellent education related tips and excellent career guidance. Contact: https://goo.gl/forms/cmB1rRC4v5qF2rf73 Fill the form above and we would get in touch with you Hey there and welcome to the Brain Checker's YouTube channel, India’s largest Career Counseling Company!! The career we would be discussing today is: MBA in BUSINESS ANALYTICS AND INTELLIGENCE Management Our entire video would be divided into 5 sections: - Introduction. - Nature of work. - Eligibility and Professional Courses available. - Best Colleges - Career prospects and Salary I would like to point out that the data given in this video is not exhaustive in nature and has been made for educational purposes only. Students are requested to perform their own due research before choosing a career. You can check the description for additional details and assistance from Brain Checker. So Let’s begin.. Introduction MBA in Business Analytics & Intelligence offers you an opportunity to explore all avenues which are promising for your career development and growth. The course comprises of Computer simulation, optimization, statistics, decision analysis, artificial intelligence, data mining and visualization, predictive modeling, marketing, supply chain, applications in finance and many more. In a survey that was conducted by Computer World, the respondents gave following responses towards the use of business analytics in their organization. Now let’s go to, Nature of Work Business analytics has the potential to resolve all the problem areas which are causing hindrances in the growth of an enterprise. The facts and figures are based on the trends which the industry witnessed in past. These facts aids in smooth and effective decision making. Further, the respondents who participated in the survey were also probed on the relevance of business analytics in streamlining organizational function. Business Analytics is used by Companies to data-driven decision- making. Simply it’s a study of data through statistical and operations analysis, the formation of predictive models, applications used for optimizing techniques etc. Business Analytics is used in industries like financial services, retail, healthcare, manufacturing, energy, gas & oil, social media, gaming, e-commerce, sports etc. Now let’s go to, Eligibility and Professional Courses • 10+2 Science with in either Arts/Science or Commerce is mandatory with at least 60% marks. • Student has to be a graduate in any stream for example BA,B.Com, B.Sc or BBA to be considered for a Masters Program with at least 60% marks. • Most Top Colleges require the applicant to clear the CAT Exam conducted by the IIMs and widely accepted by all leading Management Institutions in India • State CET , MAT, CAT, GMAT, JMET or XAT are also accepted by some colleges as alternatives to CAT Scores We at Brain Checker help students in choosing their career. To know if this career suits your talents or skill sets, you can consult a Brain Checker Career Specialist. Check link in the description for more details. Now we are going to look at few good colleges offering this qualification: • IIM Bangalore • Bharati Vidyapeeth Pune • Great Lakes Institute of Management Gurgaon • College of Management and Economic Studies, UPES Dehradhun • Institute of Management and Entrepreneurial Development Pune • Alliance School of Business Bangalore • Ansal University Gurgaon And many more….. Moving on to the next part of the video........ Career Prospects Business analytics has a wide range of application and usages. Some important applications of this specialization are: • Qualitative Analyst • Market Research Analyst • Technical Team Leader • Data Analyst SAS Programmer • Big Data Analyst • Data Warehousing Expert • Business Intelligence Expert • Data Warehousing and BA Architecture • Data Mining Expert And many more………. MBA in Business Analytics & Intelligence, the average salary offered to candidates usually lies between Rs.50,000 to Rs.60,000 per month. It is important to note that the salary packages may vary as per industry trends and the type of organization you are working for or the college you have done your studies from
ILTA educational webinar: Integrated analytics for legal marketers
 
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We are awash in a world of analytics. Everywhere we turn, there is a piece about big data, artificial intelligence and how data is the most important asset for an organization. It's enough to make your head spin. What does it really mean and how does one make sense of it all? In this webinar, we discussed a data approach utilizing what we call 'integrated analytics.' We'll shared the practical metrics you should be looking at today, how they are generated and how you can leverage content usage information to better target communications. We covered which metrics are truly useful, new ideas on measuring content quality and how to effectively use data to drive more business.
Views: 22 Tikit
Machine Learning with R Tutorial: Identifying Clustering Problems
 
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Make sure to like & comment if you liked this video! Take Hank's course here: https://www.datacamp.com/courses/unsupervised-learning-in-r Many times in machine learning, the goal is to find patterns in data without trying to make predictions. This is called unsupervised learning. One common use case of unsupervised learning is grouping consumers based on demographics and purchasing history to deploy targeted marketing campaigns. Another example is wanting to describe the unmeasured factors that most influence crime differences between cities. This course provides a basic introduction to clustering and dimensionality reduction in R from a machine learning perspective, so that you can get from data to insights as quickly as possible. Transcript: Hi! I'm Hank Roark, I'm a long-time data scientist and user of the R language, and I'll be your instructor for this course on unsupervised learning in R. In this first chapter I will define ‘unsupervised learning’, provide an overview of the three major types of machine learning, and you will learn how to execute one particular type of unsupervised learning using R. There are three major types of machine learning. The first type is unsupervised learning. The goal of unsupervised learning is to find structure in unlabeled data. Unlabeled data is data without a target, without labeled responses. Contrast this with supervised learning. Supervised learning is used when you want to make predictions on labeled data, on data with a target. Types of predictions include regression, or predicting how much of something there is or could be, and classification which is predicting what type or class some thing is or could be. The final type is reinforcement learning, where a computer learns from feedback by operating in a real or synthetic environment. Here is a quick example of the difference between labeled and unlabeled data. The table on the left is an example with three observations about shapes, each shape with three features, represented by the three columns. This table, the one on the left is an example of unlabeled data. If an additional vector of labels is added, like the column of labels on the right hand side, labeling each observation as belonging to one of two groups, then we would have labeled data. Within unsupervised learning there are two major goals. The first goal is to find homogeneous subgroups within a population. As an example let us pretend we have a population of six people. Each member of this population might have some attributes, or features — some examples of features for a person might be annual income, educational attainment, and gender. With those three features one might find there are two homogeneous subgroups, or groups where the members are similar by some measure of similarity. Once the members of each group are found, we might label one group subgroup A and the other subgroup B. The process of finding homogeneous subgroups is referred to as clustering. There are many possible applications of clustering. One use case is segmenting a market of consumers or potential consumers. This is commonly done by finding groups, or clusters, of consumers based on demographic features and purchasing history. Another example of clustering would be to find groups of movies based on features of each movie and the reviews of the movies. One might do this to find movies most like another movie. The second goal of unsupervised learning is to find patterns in the features of the data. One way to do this is through ‘dimensionality reduction’. Dimensionality reduction is a method to decrease the number of features to describe an observation while maintaining the maximum information content under the constraints of lower dimensionality. Dimensionality reduction is often used to achieve two goals, in addition to finding patterns in the features of the data. Dimensionality reduction allows one to visually represent high dimensional data while maintaining much of the data variability. This is done because visually representing and understanding data with more than 3 or 4 features can be difficult for both the producer and consumer of the visualization. The third major reason for dimensionality reduction is as a preprocessing step for supervised learning. More on this usage will be covered later. Finally a few words about the challenges and benefits typical in performing unsupervised learning. In unsupervised learning there is often no single goal of the analysis. This can be presented as someone asking you, the analyst, “to find some patterns in the data.” With that challenge, unsupervised learning often demands and brings out the deep creativity of the analyst. Finally, there is much more unlabeled data than labeled data. This means there are more opportunities to apply unsupervised learning in your work. Now it's your turn to practice what you've learned.
Views: 2402 DataCamp
ICO Review: DxChain (DX) - Big Data & Machine Learning Network
 
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DxChain is developing a decentralized big data and machine learning network. Learn more: https://crushcrypto.com/dxchain-ico-review/ Project website: https://www.dxchain.com White paper: https://docsend.com/view/8w3fman Download the PDF version of the presentation: https://crushcrypto.com/wp-content/uploads/2018/07/CrushCrypto-ICO-Review-DxChain-DX.pdf Download the free ICO Guide which contains 6 simple steps for analyzing any ICOs to find the winning projects: https://crushcrypto.com/youtube/ Note: This is not a paid review. We do not offer promotional or advertising services. Our content is based on our own research, analysis and personal opinion. _______________________________________ What does the company/project do? DxChain is developing a decentralized big data and machine learning network. The project is based on the premise that data is valuable and data creators should be able to own and benefit from their own data. The team aims to tackle major big data issues such as privacy, ownership, and security while supporting business intelligence and machine learning applications. With DxChain’s decentralized data exchange platform, users would be able to own and control their own data, and securely trade and analyze data. Utilizing blockchain technology featuring multi-nodes and distributed storage, the costs of data retrieval and storage would also be reduced significantly. DxChain is based on a chains-on-chain architecture which includes one master chain and two side chains. The structure was designed as such in order to solve multiple issues related to data computation, storage, and privacy issues; this would otherwise be difficult to do simultaneously with only one chain. _______________________________________ What are the tokens used for and how can token holders make money? The DX token is the network’s native protocol token. The token will initially be issued as ERC-20 tokens after the crowdsale and will be migrated to the DxChain mainnet after it is launched. There are several uses for the tokens: - DX tokens will serve as a secure and primary method of payment between participants in the network. - Providers of computational and storage resources that are required for running various apps and transactions will be rewarded with DX tokens. - Miners will be rewarded with DX tokens based on the usefulness of work that they passively provide. DX tokens should appreciate in value as more users join and use the network. This is driven by numerous factors, including the number and type of applications available on the platform, user experience, data storage capacity and processing speed, etc. _______________________________________ Opportunities - Concerns over data ownership and privacy is becoming more and more prevalent around the world. The recent enactment of the GDPR in the European Union shows how serious the issue has become for both individuals and governments on legitimate collection and use of personal data. - If the team is successful in decentralizing Hadoop, a distributed file storage and computation solution, we believe it provides a substantial potential. - The team has a strong technical background and relevant working experience in the fields of big data, distributed systems, blockchain research, network security, and so on. _______________________________________ Concerns - Proof of Spacetime is a new concept and no established blockchain is currently using it. It is unclear whether this concept will ultimately work. While PoST doesn’t require expensive mining hardware, there is still the concern that a rich player, such as a nation state or a manufacturer that enjoys cost advantage, could employ a large amount of storage and get to sign most blocks. - Multiple key members of the team are still working at Trustlook. We are unsure how they will allocate their time between the two ventures. _______________________________________ Disclaimer The information in this video is for educational purposes only and is not investment advice. Please do your own research before making any investment decisions. Cryptocurrency investments are volatile and high risk in nature. Don't invest more than what you can afford to lose. Crush Crypto makes no representations, warranties, or assurances as to the accuracy, currency or completeness of the content contained in this video or any sites linked to or from this video.
Views: 2017 Crush Crypto
Mining application video 2016
 
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Applications with Rossi gear motors in mining industry
Views: 1230 Rossi Spa
Panel: Beyond Fairness in Machine Learning: Social Justice and Causal Inference
 
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Inaugural AI Research Week, hosted by the MIT-IBM Watson AI Lab. Joi Ito, Director of the MIT Media Lab, chairs a panel examining fairness in machine learning. Speakers:  Chair: Joi Ito, Director of the MIT Media Lab  Panelists: Chelsea Barabas, Research scientist, MIT  Francesca Rossi, Distinguished Research Staff Member, and AI Ethics Global Leader, IBM Research Miguel Hernan, Kolokotrones Professor of Biostatistics and Epidemiology, Harvard Vikash Mansinghka, Research Scientist, MIT
Views: 225 IBM Research

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