prototype in java/processing for university project Visual Data Mining 2,000 companies from Forbes 2000 29,118 company relations Nikolay Borisov, Christian Brändel, Bettina Kirchner, Berit Lochner, Florian Schneider, Benjamin Vetter and Stefan Wagner mentors: Moritz Biehl, Marius Brade, Klaus Engel, Prof. Rainer Groh chair of Media Design, TU Dresden T-Systems Multimedia Solutions
Views: 8552 Stefan Wagner
There are many definitions for Business Intelligence, or BI. To put it simply, BI is about delivering relevant and reliable information to the right people at the right time with the goal of achieving better decisions faster. If you wanna have efficient access to accurate, understandable and actionable information on demand, then BI might be right for your organization. For more information, contact Hitachi Solutions Canada (canada.hitachi-solutions.com).
Views: 400393 Hitachi Solutions Canada
Data mining Advance topics - Web mining - Text Mining -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~- Follow us on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 64101 Well Academy
This video is part of the UBC Learning Commons three-minute tutorials series. The tutorial will introduce you to the concepts of data visualization, provide examples of how it is done, and show you some online tools to get you started. Visit the UBC Learning Commons Study Toolkits: http://learningcommons.ubc.ca/get-started/study-toolkits/ or attend one of our online workshops: http://learningcommons.ubc.ca/get-started/learning-skills-resources/online-workshop-resources/
The entire world runs on data. With so much data at our disposal, how do we begin to make sense of it all. The answer can be found in data visualization and visual analytics. This video will show you the importance of both. For more information on this subject, download the whitepaper here: https://www.onlinewhitepapers.com/marketing/why-we-need-visual-analytics/ *** Subscribe to OnlineWhitepapers.com HERE: http://www.onlinewhitepapers.com For more content from OnlineWhitepapers.com, click HERE: http://www.onlinewhitepapers.com Watch other videos from OnlineWhitepapers.com HERE: https://www.youtube.com/channel/UCGTxz2nU-LVbyxtVq1C_MWQ Like OnlineWhitepapers.com on Facebook HERE: https://www.facebook.com/OnlineWhitePapers/ Follow OnlineWhitepapers.com on Twitter HERE: https://twitter.com/onlineWPs Follow OnlineWhitepapers.com Instagram HERE: https://www.instagram.com/onlinewhitepapers/ Follow OnlineWhitepapers.com LinkedIn HERE: https://www.linkedin.com/showcase/onlinewhitepapers/ ABOUT: Online White Papers, a web brand of Bython Media, helps company executives and IT decision-makers identify the problem areas of their business as well as strategies, techniques, and technologies to inform employees, give insight and support where needed, and to streamline the business process. A vast repository of professional resources from leaders in the IT, finance, marketing, and human resources industries brings solutions to your fingertips. Find out more at OnlineWhitePapers.com.
Views: 1350 Bython Media
This is a powerpoint/video compilation I made for a project in my Systems Engineering class. It is a tutorial of Data Mining in the Retail Industry and includes a trip I took to Harris Teeter to prove the importance of Market Basket Analysis in the real world.
Views: 7712 bgood717
Support Vector Machine (SVM) - Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS COURSE - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML ►MACHINE LEARNING COURSES -http://augmentedstartups.info/machine-learning-courses ------------------------------------------------------------------------ A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. To understand SVM’s a bit better, Lets first take a look at why they are called support vector machines. So say we got some sample data over here of features that classify whether a observed picture is a dog or a cat, so we can for example look at snout length or and ear geometry if we assume that dogs generally have longer snouts and cat have much more pointy ear shapes. So how do we decide where to draw our decision boundary? Well we can draw it over here or here or like this. Any of these would be fine, but what would be the best? If we do not have the optimal decision boundary we could incorrectly mis-classify a dog with a cat. So if we draw an arbitrary separation line and we use intuition to draw it somewhere between this data point for the dog class and this data point of the cat class. These points are known as support Vectors – Which are defined as data points that the margin pushes up against or points that are closest to the opposing class. So the algorithm basically implies that only support vector are important whereas other training examples are ‘ignorable’. An example of this is so that if you have our case of a dog that looks like a cat or cat that is groomed like a dog, we want our classifier to look at these extremes and set our margins based on these support vectors. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 215353 Augmented Startups
Cloud, IoT and Mobile have spurred rapid change in the way consumers interact with businesses. As a result, companies need to innovate at unprecedented speeds to remain competitive. How can you utilize integration, event processing and analytics to give your company the edge it needs to move digitally with the world? Watch this short teaser and email me at [email protected] to find out.
Views: 5573 ronnie xie
Data Centre Green Tech & Data Centre Strategics Conference Shanghai 2010 Data Mining is a research emphasis on discovering patterns from a huge data set. In this talk, we will have some intuitive understanding of this technology and address its usefulness in the real-world applications and the future world. Prof. Zengchang Qin (Ph.D.) Associate Professor Intelligent Computing and Machine Learning Lab School of Automation Science and Electrical Engineering Beihang University
Views: 1047 PacificStrategicGrp
Introduction to students considering studying data mining, by Gregory Piatetsky-Shapiro
Views: 5413 Gregory Piatetsky-Shapiro
http://www.ted.com With the drama and urgency of a sportscaster, statistics guru Hans Rosling uses an amazing new presentation tool, Gapminder, to present data that debunks several myths about world development. Rosling is professor of international health at Sweden's Karolinska Institute, and founder of Gapminder, a nonprofit that brings vital global data to life. (Recorded February 2006 in Monterey, CA.) TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes. TED stands for Technology, Entertainment, Design, and TEDTalks cover these topics as well as science, business, development and the arts. Closed captions and translated subtitles in a variety of languages are now available on TED.com, at http://www.ted.com/translate. Follow us on Twitter http://www.twitter.com/tednews Checkout our Facebook page for TED exclusives https://www.facebook.com/TED
Views: 2918410 TED
In this Data Mining Fundamentals tutorial, we introduce you to data exploration and visualization and what they are to data mining. Data exploration is visualization and calculation to better understand characteristics of data. We will tell you the key motivations of data exploration as well as the techniques used in data exploration. -- Learn more about Data Science Dojo here: https://hubs.ly/H0hCsJv0 Watch the latest video tutorials here: https://hubs.ly/H0hCsqp0 See what our past attendees are saying here: https://hubs.ly/H0hCsJw0 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 830 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 7370 Data Science Dojo
Our site: http://www.cernido.com/ Cernido provides data mining and big data analysis services. Main focus is collaboration with business consulting companies to provide their customers competitive advantage by analyzing information from different sources.
Views: 379 Cernido Yinius
Using public social media data from twitter and Facebook, actions and announcements of terrorists – in this case ISIS – can be monitored and even be predicted. With his project #DataShield Wassim shares his idea of having a tool to identify oncoming threats and attacks in order to protect people and to induce preventive actions. Wassim Zoghlami is a Tunisian Computer Engineering Senior focussing on Business Intelligence and ERP with a passion for data science, software life cycle and UX. Wassim is also an award winning serial entrepreneur working on startups in healthcare and prevention solutions in both Tunisia and The United States. During the past years Wassim has been working on different projects and campaigns about using data driven technology to help people working to uphold human rights and to promote civic engagement and culture across Tunisia and the MENA region. He is also the co-founder of the Tunisian Center for Civic Engagement, a strong advocate for open access to research, open data and open educational resources and one of the Global Shapers in Tunis. At TEDxMünster Wassim will talk about public social media data mining for counter-terrorism and his project idea DataShield. 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: 2214 TEDx Talks
What is DATA EXPLORATION? What does DATA EXPLORATION mean? DATA EXPLORATION meaning - DATA EXPLORATION definition - DATA EXPLORATION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Data exploration is an approach similar to initial data analysis, whereby a data analyst uses visual exploration to understand what is in a dataset and the characteristics of the data, rather than through traditional data management systems. These characteristics can include size or amount of data, completeness of the data, correctness of the data, possible relationships amongst data elements or files/tables in the data. Data exploration is typically conducted using a combination of automated and manual activities. Automated activities can include data profiling or data visualization or tabular reports to give the analyst an initial view into the data and an understanding of key characteristics. This is often followed by manual drill-down or filtering of the data to identify anomalies or patterns identified through the automated actions. Data exploration can also require manual scripting and queries into the data (e.g. using languages such as SQL or R) or using Excel or similar tools to view the raw data. All of these activities are aimed at creating a clear mental model and understanding of the data in the mind of the analyst, and defining basic metadata (statistics, structure, relationships) for the data set that can be used in further analysis. Once this initial understanding of the data is had, the data can be pruned or refined by removing unusable parts of the data, correcting poorly formatted elements and defining relevant relationships across datasets. This process is also known as determining data quality. At this stage, the data can be considered ready for deeper analysis or be handed off to other analysts or users who have specific needs for the data. Data exploration can also refer to the adhoc querying and visualization of data to identify potential relationships or insights that may be hidden in the data. In this scenario, hypotheses may be created and then the data is explored to identify whether those hypotheses are correct. Traditionally, this had been a key area of focus for statisticians, with John Tukey being a key evangelist in the field. Today, data exploration is more widespread and is the focus of data analysts and data scientists; the latter being a relatively new role within enterprises and larger organizations.
Views: 330 The Audiopedia
SECO (the Swiss Unemployment Agency) is a MicroStrategy customer since 2009. In this video Elmar Benelli -- Head Data Warehouse at SECO -- describes how SECO implemented the MicroStrategy platform leveraging dashboards, mobile applications and Visual Insight in order to centralize and define KPIs for the entire organization (900 users across all Switzerland). SECO considers the MicroStrategy software platform as the most flexible, intuitive, and easy to use BI platform. Their selection process found MicroStrategy to be the best fit for Business Intelligence and for Visual Exploration.
Views: 3497 MicroStrategy
Big Data For Beginners | Big Data Analytics | Hadoop | Courses | Scope | Salary In this video we will explained Big Data and We will also explain how you can make career in it. So Do Like it Share it and Subscribe the channel for more such updates related to Education, Opinions and Interesting Facts. ►Ethical Hacking Details - https://www.youtube.com/watch?v=o0_janBsLso ✔ ►Digital Marketing Details - https://www.youtube.com/watch?v=r1suKCHLmXo&t=36s ✔ ►Top Online Website for Learning - https://www.youtube.com/watch?v=Xcm1iPiUJxo ✔ WATCH OUR BEST VIDEOS RELATED TO EDUCATION ►Everything About MBA in India - https://goo.gl/wGD8NM ✔ ►Top College Rankings - https://goo.gl/LEFzun ✔ ►All about Investment Banking - https://goo.gl/Hk1rix ✔ ►Financial Certification in Hindi - https://goo.gl/sKPqod ✔ ►Internship and Apprenticeship Video in Hindi - https://goo.gl/RCBqBY ✔ ►MCA Course Detail Hindi - https://goo.gl/bxntn3 ✔ ►After 12th Best Courses for Science, Commerce and Arts - https://goo.gl/rVMcTR ✔ ►BCA Course Related Video in Hindi - https://goo.gl/wsCM2G ✔ ►BTech Course Related Video in Hindi - https://goo.gl/DqvfGF ✔ ►Fastest and Easiest Way to Learn English - https://www.youtube.com/watch?v=GF5OHAZcW0k ✔ ► How to Get Education LOAN in INDIA - https://www.youtube.com/watch?v=DoluUHBZ1zw ✔ ► MBA INDIA VS MBA ABROAD - WHICH IS BEST ? - https://www.youtube.com/watch?v=ufgd8pkvtjE ✔ ► Highest Paying Careers in India - https://www.youtube.com/watch?v=GF5OHAZcW0k ✔ WATCH OUR BEST VIDEOS RELATED TO INTERESTING FACTS & OPINIONS [HINDI] ►Padmavati controversy in 5 minutes :- https://www.youtube.com/watch?v=ar_orIiwQqU&t=2s ✔ ►North Korea vs USA Nuclear War :- https://www.youtube.com/watch?v=HrJkIqc3lB8&t=1s ✔ ►Kamlesh Viral Video Truth :- https://www.youtube.com/watch?v=J-t0u81Tpt0&t=2s ✔ ►Dangal Girl Zaira Wasim Issue :- https://www.youtube.com/watch?v=j9z7MHSKG14&t=28s ✔ ►Countries where Education is free for Indians - https://www.youtube.com/watch?v=qDNG6H5qRA0&t=2s ✔ BUY OUR RECORDING GEAR AT DISCOUNTED PRICES:- External Recording Blue Mic -http://amzn.to/2ynJOSn My Nikon Dslr d5600 - http://amzn.to/2ynN7sV My Collar Mic- http://amzn.to/2x3LAEf ABOUT US :- Praveen Dilliwala is a youth oriented Review Channel Where you will get Videos related to Education,Opinions, Jobs, Motivational, Interesting Facts and also I will share my experience about these things. Our Motto is to provide unbiased and right information so that you make informed decision. Follow us on : Facebook - https://www.facebook.com/PraveenDilliwala Twitter - https://twitter.com/praveendiliwala Instagram - https://www.instagram.com/praveendilliwala Google Plus - https://plus.google.com/u/0/113602971119506935594
Views: 78679 Praveen Dilliwala
Think Before You Ink is a video mini-series created by the University of British Columbia's Digital Tattoo project that aims to raise awareness among the general public about issues surrounding digital identity and citizenship. Ever wonder how Amazon knew you'd want to buy that slap chop set? Or how Netflix predicted you'd love House of Cards before you even knew about it? Data Mining is the powerful technology behind this predictive magic. To learn more about data mining and how it impacts your daily life, watch the video above! And don't forget to visit our website at www.digitaltattoo.ubc.ca to learn more. Music offered by Syril: Licensed for public use. CC copyright. https://www.youtube.com/watch?v=BArOuD_UBGE
Views: 2100 The Digital Tattoo Project
This is short tutorial for What it is? (What do we mean by a cluster?) How it is different from decision tree? What is distance and linkage function? What is hierarchical clustering? What is scree plot & dendogram? What is non hierarchical clustering (k-means)? How to learn it in detail (step by step)? --------------------------------- Read in great detail along with Excel output, computation and SAS code ---------------------------------- https://www.udemy.com/cluster-analysis-motivation-theory-practical-application/?couponCode=FB_CA_001
Views: 139493 Gopal Malakar
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: 389436 Quantitative Specialists
This Edureka "What Is SAS" video will help you get started with SAS. This video will also introduce you to Data Analytics and SAS Programming concepts. Check out our SAS Tutorial Playlist: https://goo.gl/aywPvo This video helps you to learn following topics: 1. Data Analytics 2. Data Analytical Tools 3. Why SAS? 4. What Is SAS? 5. SAS Framework 6. SAS Programming Concepts 7. SAS Applications Subscribe to our channel to get video updates. Hit the subscribe button above. #SAS #WhatIsSAS #SASProgramming #SASTraining #SASforbeginners #SASTutorial #SASBuildingBlocks #SASDataAndProcSteps ----------------------------------------------------------------- How it Works? 1. This is a 4 Week Instructor led Online Course, 25 hours of assignment and 20 hours of project work. 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. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! -------------------------------------------------------------------- About The Course Edureka's SAS Course is designed to provide knowledge and skills to become a successful Analytics professional. It starts with the fundamental concepts of rules of SAS as a Language to an introduction to advanced SAS topics like SAS Macros. ---------------------------------------------------------------------- Who should go for this course? This course is designed for professionals who want to learn widely acceptable data mining and exploration tools and techniques, and wish to build a booming career around analytics. The course is ideal for: 1. Analytics professionals who are keen to migrate to advanced analytics 2. BI /ETL/DW professionals who want to start exploring data to eventually become data scientist 3. Project Managers to help build hands-on SAS knowledge, and to become a SME via analytics 4. Testing professionals to move towards creative aspects of data analytics 5. Mainframe professionals 6. Software developers and architects 7. Graduates aiming to build a career in Big Data as a foundational step ----------------------------------------------------------------------- Why learn SAS? The Edureka SAS training certifies you as an ‘in demand’ SAS professional, to help you grab top paying analytics job titles with hands-on skills and expertise around data mining and management concepts. SAS is the primary analytics tool used by some of the largest KPOs, Banks like American Express, Barclays etc., financial services irms like GE Money, KPOs like Genpact, TCS etc., telecom companies like Verizon (USA), consulting companies like Accenture, KPMG etc use the tool effectively. For more information, please write back to us at [email protected] Call us at US: 1844 230 6362(toll free) or India: +91-90660 20867 Website: https://www.edureka.co/sas-training Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 180043 edureka!
Data Preparation: Comparison of Programming Languages, Frameworks and Tools for Data Preprocessing and (Inline) Data Wrangling in Machine Learning / Deep Learning Projects. A key task to create appropriate analytic models in machine learning or deep learning is the integration and preparation of data sets from various sources like files, databases, big data storages, sensors or social networks. This step can take up to 80% of the whole project. This session compares different alternative techniques to prepare data, including extract-transform-load (ETL) batch processing (like Talend, Pentaho), streaming analytics ingestion (like Apache Storm, Flink, Apex, TIBCO StreamBase, IBM Streams, Software AG Apama), and data wrangling (DataWrangler, Trifacta) within visual analytics. Various options and their trade-offs are shown in live demos using different advanced analytics technologies and open source frameworks such as R, Python, Apache Hadoop, Spark, KNIME or RapidMiner. The session also discusses how this is related to visual analytics tools (like TIBCO Spotfire), and best practices for how the data scientist and business user should work together to build good analytic models. Key takeaways for the audience: - Learn various options for preparing data sets to build analytic models - Understand the pros and cons and the targeted persona for each option - See different technologies and open source frameworks for data preparation - Understand the relation to visual analytics and streaming analytics, and how these concepts are actually leveraged to build the analytic model after data preparation Slide Deck: http://www.slideshare.net/KaiWaehner/data-preparation-vs-inline-data-wrangling-in-data-science-and-machine-learning
Views: 2841 Kai Wähner
What is INFORMATION VISUALIZATION? What does INFORMATION VISUALIZATION mean? INFORMATION VISUALIZATION meaning - INFORMATION VISUALIZATION definition - INFORMATION VISUALIZATION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Information visualization or information visualisation is the study of (interactive) visual representations of abstract data to reinforce human cognition. The abstract data include both numerical and non-numerical data, such as text and geographic information. However, information visualization differs from scientific visualization: "it’s infovis when the spatial representation is chosen, and it’s scivis when the spatial representation is given". The field of information visualization has emerged "from research in human-computer interaction, computer science, graphics, visual design, psychology, and business methods. It is increasingly applied as a critical component in scientific research, digital libraries, data mining, financial data analysis, market studies, manufacturing production control, and drug discovery". Information visualization presumes that "visual representations and interaction techniques take advantage of the human eye’s broad bandwidth pathway into the mind to allow users to see, explore, and understand large amounts of information at once. Information visualization focused on the creation of approaches for conveying abstract information in intuitive ways." Data analysis is an indispensable part of all applied research and problem solving in industry. The most fundamental data analysis approaches are visualization (histograms, scatter plots, surface plots, tree maps, parallel coordinate plots, etc.), statistics (hypothesis test, regression, PCA, etc.), data mining (association mining, etc.), and machine learning methods (clustering, classification, decision trees, etc.). Among these approaches, information visualization, or visual data analysis, is the most reliant on the cognitive skills of human analysts, and allows the discovery of unstructured actionable insights that are limited only by human imagination and creativity. The analyst does not have to learn any sophisticated methods to be able to interpret the visualizations of the data. Information visualization is also a hypothesis generation scheme, which can be, and is typically followed by more analytical or formal analysis, such as statistical hypothesis testing. The modern study of visualization started with computer graphics, which "has from its beginning been used to study scientific problems. However, in its early days the lack of graphics power often limited its usefulness. The recent emphasis on visualization started in 1987 with the special issue of Computer Graphics on Visualization in Scientific Computing. Since then there have been several conferences and workshops, co-sponsored by the IEEE Computer Society and ACM SIGGRAPH". They have been devoted to the general topics of data visualisation, information visualization and scientific visualisation, and more specific areas such as volume visualization.
Views: 1352 The Audiopedia
Data Mining For Business Intelligence: Concepts, Techniques, And Applications In Microsoft Office Excel With XLMiner. B... http://www.thebookwoods.com/book02/0470084855.html Author of the book in this video: Galit Shmueli Nitin R. Patel Peter C. Bruce The book in this video is published by: Wiley-Interscience THE MAKER OF THIS VIDEO IS NOT AFFILIATED WITH OR ENDORSED BY THE PUBLISHING COMPANIES OR AUTHORS OF THE BOOK IN THIS VIDEO. ---- DISCLAIMER --- 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. All content in this video and written content are copyrighted to their respective owners. All book covers and art are copyrighted to their respective publishing companies and/or authors. We do not own, nor claim ownership of any images used in this video. All credit for the images or photography go to their rightful owners.
Views: 318 Johan Lidrag Hagen
This Random Forest Algorithm tutorial will explain how Random Forest algorithm works in Machine Learning. By the end of this video, you will be able to understand what is Machine Learning, what is Classification problem, applications of Random Forest, why we need Random Forest, how it works with simple examples and how to implement Random Forest algorithm in Python. Below are the topics covered in this Machine Learning tutorial: 1. What is Machine Learning? 2. Applications of Random Forest 3. What is Classification? 4. Why Random Forest? 5. Random Forest and Decision Tree 6. Use case - Iris Flower Analysis Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the Slides here: https://goo.gl/K8T4tW Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Random-Forest-Tutorial-eM4uJ6XGnSM&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Random-Forest-Tutorial-eM4uJ6XGnSM&utm_medium=Tutorials&utm_source=youtube #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - - Who should take this Machine Learning Training Course? We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning - - - - - - For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 68435 Simplilearn
http://theexcelclub.com/forecasting-and-hind-casting-in-power-bi The Excel Club blog has been powered with #steem, we are a tokenized website where you can earn while you learn FREE Power BI course - Power BI - The Ultimate Orientation http://theexcelclub.com/free-excel-training/ Or on Udemy https://www.udemy.com/power-bi-the-ultimate-orientation Microsoft's Power BI contains a forecast function that will also you make future predictions based on a time series of data. In this video we will look at the forecast trend line and how you can use this to predict future values. You can also use the forecast function in #PowerBI to hindcast and check to see if your forecasting model works Would love to hear your feedback...please visit our blog and earn tokens for engaging on the post Sign up to our newsletter http://theexcelclub.com/newsletter/ Watch more Power BI videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiEsQ-68y0tdnaU9hCqjJ5Dh Watch Excel Videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiFFpjWeK7CE3AEXy_IRZp4y
Views: 32217 Paula Guilfoyle
( Data Science Training - https://www.edureka.co/data-science ) This Edureka Random Forest tutorial will help you understand all the basics of Random Forest machine learning algorithm. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn random forest analysis along with examples. Below are the topics covered in this tutorial: 1) Introduction to Classification 2) Why Random Forest? 3) What is Random Forest? 4) Random Forest Use Cases 5) How Random Forest Works? 6) Demo in R: Diabetes Prevention Use Case Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #RandomForest #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). 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: 63068 edureka!
In this Statistics Using Python Tutorial, Learn cleaning Data in Python Using Pandas. learn basic data cleaning steps in excel before importing data in python. We use Pandas Functions to clean data perform exploratory data analysis on our Data set. 🔷🔷🔷🔷🔷🔷🔷🔷 Jupyter Notebooks and Practice Files: https://github.com/theengineeringworld/statistics-using-python 🔷🔷🔷🔷🔷🔷🔷🔷 Data Wrangling With Python Using Pandas, Data Science For Beginners, Statistics Using Python 🐍🐼 https://youtu.be/tqv3sL67sC8 Cleaning Data In Python Using Pandas In Data Mining Example, Statistics With Python For Data Science https://youtu.be/xcKXmXilaSw Cleaning Data In Python For Statistical Analysis Using Pandas, Big Data & Data Science For Beginners https://youtu.be/4own4ojgbnQ Exploratory Data Analysis In Python, Interactive Data Visualization [Course] With Python and Pandas https://youtu.be/VdWfB30QTYI 🔷🔷🔷🔷🔷🔷🔷🔷 *** Complete Python Programming Playlists *** * Python Data Science https://www.youtube.com/watch?v=Uct_EbThV1E&list=PLZ7s-Z1aAtmIbaEj_PtUqkqdmI1k7libK * NumPy Data Science Essential Training with Python 3 https://www.youtube.com/playlist?list=PLZ7s-Z1aAtmIRpnGQGMTvV3AGdDK37d2b * Python 3.6.4 Tutorial can be fund here: https://www.youtube.com/watch?v=D0FrzbmWoys&list=PLZ7s-Z1aAtmKVb0fpKyINNeSbFSNkLTjQ * Python Smart Programming in Jupyter Notebook: https://www.youtube.com/watch?v=FkJI8np1gV8&list=PLZ7s-Z1aAtmIVV0dp08_X-yDGrIlTExd2 * Python Coding Interview: https://www.youtube.com/watch?v=wwtzs7vTG50&list=PLZ7s-Z1aAtmJqtN1A3ydeMk0JoD3Lvt9g
Views: 7824 TheEngineeringWorld
What is CULTURAL ANALYTICS? What does CULTURAL ANALYTICS mean? CULTURAL ANALYTICS meaning - CULTURAL ANALYTICS definition - CULTURAL ANALYTICS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Cultural analytics is the exploration and research of massive cultural data sets of visual material – both digitized visual artifacts and contemporary visual and interactive media. Taking on the challenge of how to best explore large collections of rich cultural content, cultural analytics researchers developed new methods and intuitive visual techniques which rely on high-resolution visualization and digital image processing. These methods are used to address both the existing research questions in humanities, to explore new questions, and to develop new theoretical concepts which fit the mega-scale of digital culture in the early 21st century. The term "cultural analytics" was coined by Lev Manovich in 2007. Cultural analytics shares many ideas and approaches with visual analytics ("the science of analytical reasoning facilitated by visual interactive interfaces") and visual data analysis: Visual data analysis blends highly advanced computational methods with sophisticated graphics engines to tap the extraordinary ability of humans to see patterns and structure in even the most complex visual presentations. Currently applied to massive, heterogeneous, and dynamic datasets, such as those generated in studies of astrophysical, fluidic, biological, and other complex processes, the techniques have become sophisticated enough to allow the interactive manipulation of variables in real time. Ultra high-resolution displays allow teams of researchers to zoom in to examine specific aspects of the renderings, or to navigate along interesting visual pathways, following their intuitions and even hunches to see where they may lead. New research is now beginning to apply these sorts of tools to the social sciences and humanities as well, and the techniques offer considerable promise in helping us understand complex social processes like learning, political and organizational change, and the diffusion of knowledge. While increased computing power and technical developments allowing for interaction visualization have made the exploration of large data sets using visual presentations possible, the intellectual drive to understand cultural and social processes and production pre-dates many of these computational advances. Charles Joseph Minard's famous dense graphic showing Napoleon's March on Moscow (1869) offers a 19th-century example. More recently, Pierre Bourdieu's historical survey of the cultural consumption practices of mid-century Parisians, documented in La Distinction, foregrounds the study of culture and aesthetics through the lens of large data sets. Most recently, Franco Moretti's Graphs, maps, trees: abstract models for a literary history. along with many projects in the Digital Humanities reveal the benefit of large scale analysis of cultural material. To date, cultural analytics techniques have been applied to films, animations, video games, comics, magazines, books, and other print publications, artworks, photos, and a variety of other media content. The technology used ranges from open-source programs downloadable on any personal computer to supercomputer processing and large-scale displays such as the HIPerSpace (42,000 x 8000 pixels). The methodologies which fall under the umbrella of cultural analytics includes the data mining of large sets of culturally-relevant data (such as studies of library catalogs, image collections, and social networking databases.) Image processing of still and moving video, with feature recognition as well as image data extraction is used to support research into cultural and historical change. Cultural analytical methodologies are deployed to study and interpret videogames and other software forms, both at the phenomonological level (human-computer interface, feature extraction) or at the object level (the analysis of source code.) Cultural analytics relies heavily on software-based tools, and the field is related to the nascent discipline of software studies. While the objects of a cultural analytical approach are often digitized representations of the work, rather than the work in its original material form, the objects of study need not be digital works in themselves.
Views: 138 The Audiopedia
A description of the concepts behind Analysis of Variance. There is an interactive visualization here: http://demonstrations.wolfram.com/VisualANOVA/ but I have not tried it, and this: http://rpsychologist.com/d3-one-way-anova has another visualization
Views: 561938 J David Eisenberg
Blog: http://code-ai.mk/ Source Code: http://code-ai.mk/?p=72 K-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. - The Algorithm K-Means starts by randomly defining k centroids. From there, it works in iterative (repetitive) steps to perform two tasks: Assign each data point to the closest corresponding centroid, using the standard Euclidean distance. In layman’s terms: the straight-line distance between the data point and the centroid. For each centroid, calculate the mean of the values of all the points belonging to it. The mean value becomes the new value of the centroid. Once step 2 is complete, all of the centroids have new values that correspond to the means of all of their corresponding points. These new points are put through steps one and two producing yet another set of centroid values. This process is repeated over and over until there is no change in the centroid values, meaning that they have been accurately grouped. Or, the process can be stopped when a previously determined maximum number of steps has been met. This application is written in C# with my own implementation of K-Means algorithm. The work of the algorithm is displayed in Windows Forms and it is enough to see it in action one iteration at a time. Let me know if you want the source code. Please remember this implementation is intended for studying purpose.
Views: 1486 Vanco Pavlevski
Data Mining with Weka (1.6: Visualizing your data)
Views: 60 Data Mining with Weka
This is a very basic visual introduction to the concepts behind a blockchain. We introduce the idea of an immutable ledger using an interactive web demo. Part 2 is here: https://youtu.be/xIDL_akeras If you are interested in playing with this on your own, it is available online at: http://anders.com/blockchain/ The code that runs this demo is also on GitHub: https://github.com/anders94/blockchain-demo I'm @anders94 on Twitter and @andersbrownworth on Steemit. Donations: BTC: 1K3NvcuZzVTueHW1qhkG2Cm3viRkh2EXJp ETH: 0x84a90e21d9d02e30ddcea56d618aa75ba90331ff ETC: 0xab75ad757c89fa33b92090193a797e6700769ef8
Views: 965622 Anders Brownworth
Julian Sanchez joins us this week for a discussion about online privacy in the era of mass data collection. When we’re online, what kind of data are we creating, and who’s watching us? Full episode here: http://bit.ly/2qBnHBz Subscribe through iTunes: https://bitly.com/18wswtX Subscribe through Google Play Music: http://bit.ly/1VLM4sh Free Thoughts RSS feed: http://bit.ly/1q2vZQP
Views: 404 Libertarianism.org
To support us visit http://www.patreon.com/sprouts Additional Video by Barbara Oakley: https://www.youtube.com/watch?v=PxLHWgQ0cHk Script: 081127882 is a hard number to remember. If you chunk the number into 081 127 882 its easier. Cutting large bits of information into smaller pieces helps us to understand. If we put small pieces back together, we can see the big picture and that helps us to remember. The process is called Chunking. This is how it works. Our short-term memory is fast but tiny. According to learning expert Dr. Oakley it can hold only 4 chunks of information at once. So when new inputs arrive it has two ways to pick them up. First, it can overwrite and forget what it has. To make space for new information. Or it can use mental effort to move a chunk from the working memory into the long-term memory where it can be stored and remembered later. This is why its almost impossible to recall 9 digits like 081127882. There is simply not enough space. Once chunked, there is. There are several ways to chunk. You can break a larger piece into smaller bits, identify patterns or group pieces to see the larger picture. Once a chunk is created, you can use deliberate practice to move it into your long-term memory where it connects with exercising experiences. Now it can be stored for years and if regularly used, accessed without much mental effort To make this transfer more effective it helps to add context which acts like memory super glue. Great instructors always try to give you the big picture before going into detail. If you study by yourself, you can skim through your textbook first by reading chapter headlines. Learning facts without understanding the big picture is pretty useless, as we will forget what we have learned very fast. Professional piano teachers first show their students the entire song so they understand the mood. Then they ask their students to practice one measure at the time. Once the part has been learned and the neural connections in the brain have been built, then students go to the next measure. After all, chunks can be played separately, they are combined until the entire piece is connected. Now the student can play the piece with less mental effort. Chunking also helps to understand complex topics, say trade between China and India. First study China: the people, the culture and the economy. Then summarize and put what you learned in your own simple language. Repeat the process for India. Then study trade itself: the mechanics, benefits, and problems. Again, simplify to form an underlying idea. In the end, you might just have summarized several books onto one napkin. Try chunking next time you feel the limits of your working memory. Just like how clever restaurants chunks their menus into starters, mains, desserts, with 3-4 options each. With chunking, it's easy to compare our options and make a decision. If you like our videos and want to support our channel, visit us at patreon.com/sprouts and see if you want to donate just 1 dollar. With your support, we plan to create many more minute-videos about learning and education.
Views: 113692 Sprouts