Search results “Information visualization and visual data mining pdf”
The beauty of data visualization - David McCandless
View full lesson: http://ed.ted.com/lessons/david-mccandless-the-beauty-of-data-visualization David McCandless turns complex data sets, like worldwide military spending, media buzz, and Facebook status updates, into beautiful, simple diagrams that tease out unseen patterns and connections. Good design, he suggests, is the best way to navigate information glut -- and it may just change the way we see the world. Talk by David McCandless.
Views: 639802 TED-Ed
Data Mining with Weka (1.6: Visualizing your data)
Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Visualizing your data http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 70964 WekaMOOC
Exploratory Data Analysis
An introduction to exploratory data analysis that includes discussion of descriptive statistics, graphs, outliers, and robust statistics.
Views: 35065 Prof. Patrick Meyer
Analyzing And Visualizing Data With Excel 2016
In this workshop, get an introduction to the latest analysis and visualization capabilities in Excel 2016. See how to import data from different sources, create mash/ups between data sources, and prepare the data for analysis. After preparing the data, learn about how business calculations - from simple to more advanced - can be expressed using DAX, how the result can be visualized and shared.
Views: 34065 Microsoft Power BI
Zack Witten: Extracting Structured Data from Legal Documents | PyData LA 2018
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: 887 PyData
Factorization Machines, Visual Analytics, and Personalized Marketing
Competition in customer experience management has never been as challenging as it is now. Customers spend more money in aggregate, but less per brand. The average size of a single purchase has decreased, partly because competitive offers are just one click away. Predicting offer relevance to potential (and existing) customers plays a key role in segmentation strategies, increasing macro- and micro-conversion rates, and the average order size. This session (and the associated white paper) covers the following topics: factorization machines and how they support personalized marketing; how SAS® Visual Data Mining and Machine Learning with SAS® Customer Intelligence 360 support building and deploying factorization machines with digital experiences; and a step-by-step demonstration and business use case for the sas.com bookstore. Presenter: Suneel Grover is an Advisory Solutions Architect supporting digital intelligence, marketing analytics and multi-channel marketing at SAS. By providing client-facing services for SAS in the areas of predictive analytics, digital analytics, visualization and data-driven integrated marketing, Grover provides technical consulting support in industry verticals such as media, entertainment, hospitality, communications, and sports. In addition to his role at SAS, Grover is a professorial lecturer at The George Washington University (GWU) in Washington DC, teaching in the Masters of Science in Business Analytics graduate program within the School of Business and Decision Science. Grover has an MBA in Marketing Research & Decision Science from The George Washington University (GWU), and an MS in Integrated Marketing Analytics from New York University (NYU). Presentation Outline 00:15 – The Romance of Being Digital – Why? 01:50 – The Reality of Customer Experiences – Content Shock & Micro-Moments 03:38 – Do Brands Need to Adapt? – To Capture The Attention Of Consumers 04:50 – Every Brand Offers A Digital Experience – Recommendation Systems Play A Role 05:35 – SAS Communities Example 07:25 – Analytically-Driven Marketing – Various Flavors 09:58 – Measuring Consumer Interest In Products & Services – Recommendation Systems Require More Than Just Statistical Models 11:25 – Before You Do Analysis – You Need Data 13:20 – There Are A Few Challenges – Every Recommendation System Must Overcome 17:17 – Factorization Machines – Algorithmic Firepower For Personalized Marketing 18:23 – SAS Customer Intelligence 360 and SAS Viya – An End-To-End Solution 20:48 – SAS Customer Intelligence 360 and SAS Viya – The Bridge For Machine Learning Within Marketing 21:45 – Factorization Machines, Visual Analytics, and Personalized Marketing – Show Me The Demo View White Paper and Full Demo Factorization Machines, Visual Analytics, and Personalized Marketing – https://www.sas.com/content/dam/SAS/support/en/sas-global-forum-proceedings/2019/3087-2019.pdf SAS Customer Intelligence 360 Meets SAS Viya: Show me the demo (full demo) – https://blogs.sas.com/content/customeranalytics/2019/03/28/sas-customer-intelligence-360-meets-sas-viya-show-me-the-demo/ For additional content from SAS Global Forum 2019, visit https://www.sas.com/en_us/events/sas-global-forum/virtual.html Learn More about SAS Software SAS® Customer Intelligence 360 – https://support.sas.com/en/software/customer-intelligence-360.html SAS Viya – https://www.sas.com/en_us/software/viya.html SAS® Visual Data Mining and Machine Learning – https://www.sas.com/en_us/software/visual-data-mining-machine-learning.html SAS® Intelligent Decisioning – https://www.sas.com/en_us/software/intelligent-decisioning.html SUBSCRIBE TO THE SAS USERS YOUTUBE CHANNEL https://www.youtube.com/channel/UCWOfmTlbeesYiDJNflqsWQA?sub_confirmation=1 ABOUT SAS SAS is a trusted analytics powerhouse for organizations seeking immediate value from their data. A deep bench of analytics solutions and broad industry knowledge keep our customers coming back and feeling confident. With SAS®, you can discover insights from your data and make sense of it all. Identify what’s working and fix what isn’t. Make more intelligent decisions. And drive relevant change. CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 68 SAS Users
Data Visualization Lessons
This video serves as a portal to 10 other curated videos on YouTube which cover the topic of "Data Visualization" and other related topics such as "Infographics". Videos: _________________________________________ 1: The value of data visualization - http://www.youtube.com/watch?v=xekEXM0Vonc Additional Reading: - Column Five (video creator) blog: http://columnfivemedia.com/news/ - Visua.ly blog post about why data visualization is so hot: http://blog.visual.ly/why-is-data-visualization-so-hot/ - Article titled "Data visualization Past,Present, and Future": http://www.perceptualedge.com/articles/Whitepapers/Data_Visualization.pdf _________________________________________ 2: What Are Infographics? - http://www.youtube.com/watch?v=x3RTS1JfMy8 Additional Reading: - Wikipedia: http://en.wikipedia.org/wiki/Infographic - An infographic explaining what infographics are: http://www.customermagnetism.com/infographics/what-is-an-infographic/ _________________________________________ 3: Big Data Week Data Visualization London - Francesco D'Orazio "10 reasons why we visualize data" - http://www.youtube.com/watch?v=npEKPZxQuns Additional Reading: - Slides used in the video: http://www.slideshare.net/Facegroup/10-reasons-why-we-visualise-data - Blog post on why we should visualize data: http://seeingcomplexity.wordpress.com/2011/03/13/why-visualize-data-we-dont-know-yet/ - Using Data Visualization to Find Insights in Data: http://datajournalismhandbook.org/1.0/en/understanding_data_7.html _________________________________________ 4: David McCandless: The beauty of data visualization - http://www.youtube.com/watch?v=pLqjQ55tz-U Additional Reading: - David McCandless website: http://www.informationisbeautiful.net/ - The Information is Beautiful Awards website: http://www.informationisbeautifulawards.com/ - Beautiful Data blog: http://beautifuldata.net/ _________________________________________ 5: I Like Pretty Graphs: Best Practices for Data Visualization Assignments - http://www.youtube.com/watch?v=pD_OvRtH0aY Additional Reading: - Eight Principles of Data Visualization blog post: http://www.information-management.com/news/Eight-Principles-of-Data-Visualization-10023032-1.html - Design principles slides: http://www.slideshare.net/gelvan/design-principles _________________________________________ 6: How to Create Infographics Part I - http://www.youtube.com/watch?v=X4-_e8zliqg Additional Reading: - Interactive tutorial on creating an infographic: http://www.asmallbrightidea.com/pages/tutorial.html - Blog post with 5 infographics to teach you how to create infographics in powerpoint: http://blog.hubspot.com/blog/tabid/6307/bid/34223/5-Infographics-to-Teach-You-How-to-Easily-Create-Infographics-in-PowerPoint-TEMPLATES.aspx _________________________________________ 7: EFFECTIVE INFORMATION VISUALIZATION by Matthias Shapiro - EP 31 - http://www.youtube.com/watch?v=_l-Dby7-JG4 Additional Reading: - Blog post on creating effective data visualizations: http://online-behavior.com/analytics/effective-data-visualization _________________________________________ 8: Data, Design, Meaning - http://www.youtube.com/watch?v=vfYul2E56fo Additional Reading: - Idan Gazit personal website: http://gazit.me/ - Collection of Idan Gazit's slides including the ones used in the videos: https://speakerdeck.com/idangazit _________________________________________ 9: Data Viz: You're Doing it Wrong - http://www.youtube.com/watch?v=i93iWza8sG8 Additional Reading: - Common Mistakes in Data visualization slides: http://www.slideshare.net/amedeevangasse/common-mistakes-in-data-visualization - Visua.ly blog post about 4 easy visualization mistakes to avoid: http://blog.visual.ly/data-visualization-mistakes-to-avoid/ _________________________________________ 10: Designing Data Visualizations with Noah Iliinsky - http://www.youtube.com/watch?v=R-oiKt7bUU8 Additional Reading: - Noah Iliinsky books published and profile: http://www.oreillynet.com/pub/au/4419 - Noah Iliinsky virtual seminar on "Telling the Right Story With Data Visualizations": http://www.uie.com/brainsparks/2012/03/16/noah-iliinsky-telling-the-right-story/ - Noah Iliinsky podcast on "The Power of Data Visualizations": http://www.uie.com/brainsparks/2012/01/27/noah-iliinsky-the-power-of-data-visualizations/ _________________________________________
Views: 1869 JohnLio07
HWTAC Webinar 017 - Data Visualization: Strategies, Tips, and Tools
HWTAC Webinar 017 - Data Visualization: Strategies, Tips, and Tools Data visualization can be a powerful tool for detecting patterns in data and for sharing data accessibly with a wide audience. This webinar will introduce the basics of data visualization with an eye towards practice, including simple tips and tricks to help create effective visualizations. The webinar will also discuss the major tools available to create static or web-based interactive visualizations. Live Q&A session included. Original broadcast: February 10, 2016 Presenter: Matt Jansen, Data Analyst, UNC Libraries Moderator: David Armstrong, PhD
Excel Data Analysis: Sort, Filter, PivotTable, Formulas (25 Examples): HCC Professional Day 2012
Download workbook: http://people.highline.edu/mgirvin/ExcelIsFun.htm Learn the basics of Data Analysis at Highline Community College Professional Development Day 2012: Topics in Video: 1. What is Data Analysis? ( 00:53 min mark) 2. How Data Must Be Setup ( 02:53 min mark) Sort: 3. Sort with 1 criteria ( 04:35 min mark) 4. Sort with 2 criteria or more ( 06:27 min mark) 5. Sort by color ( 10:01 min mark) Filter: 6. Filter with 1 criteria ( 11:26 min mark) 7. Filter with 2 criteria or more ( 15:14 min mark) 8. Filter by color ( 16:28 min mark) 9. Filter Text, Numbers, Dates ( 16:50 min mark) 10. Filter by Partial Text ( 20:16 min mark) Pivot Tables: 11. What is a PivotTable? ( 21:05 min mark) 12. Easy 3 step method, Cross Tabulation ( 23:07 min mark) 13. Change the calculation ( 26:52 min mark) 14. More than one calculation ( 28:45 min mark) 15. Value Field Settings (32:36 min mark) 16. Grouping Numbers ( 33:24 min mark) 17. Filter in a Pivot Table ( 35:45 min mark) 18. Slicers ( 37:09 min mark) Charts: 19. Column Charts from Pivot Tables ( 38:37 min mark) Formulas: 20. SUMIFS ( 42:17 min mark) 21. Data Analysis Formula or PivotTables? ( 45:11 min mark) 22. COUNTIF ( 46:12 min mark) 23. Formula to Compare Two Lists: ISNA and MATCH functions ( 47:00 min mark) Getting Data Into Excel 24. Import from CSV file ( 51:21 min mark) 25. Import from Access ( 54:00 min mark) Highline Community College Professional Development Day 2012 Buy excelisfun products: https://teespring.com/stores/excelisfun-store
Views: 1582681 ExcelIsFun
Cartographic Treemaps for the Visualization of Public Healthcare Data Practice Talk by Chao Tong
The following is a practice talk for the Conference on Computer Graphics and Visual Computing, 14-15 September 2017 given by Chao Tong. Chao Tong, Richard Roberts, Robert S Laramee, Daniel Thayer, Damon Berridge, Cartographic Treemaps for the Visualization of Public Healthcare Data, The Computer Graphics and Visual Computing (CGVC) Conference 2017, 14-15 September 2017, Manchester, UK PDF http://cs.swan.ac.uk/~csbob/research/cartographic/tong17cartographic.pdf Supplementary PDF: http://cs.swan.ac.uk/~csbob/research/cartographic/tong17supplementary.pdf
Views: 63 DataVisBob Laramee
PDF Data Extraction and Automation 3.1
Learn how to read and extract PDF data. Whether in native text format or scanned images, UiPath allows you to navigate, identify and use PDF data however you need. Read PDF. Read PDF with OCR.
Views: 142433 UiPath
Integrating Power BI into Your Own Applications – Featuring Real World Demos
Visualizing data in applications is a powerful communications tool. Learn how to do this easily with Power BI
Spherical Layout and Rendering Methods for Immersive Graph Visualization
New video: https://www.youtube.com/watch?v=LQYamaU8OvA Spherical Layout and Rendering Methods for Immersive Graph Visualization Oh-Hyun Kwon, Chris Muelder, Kyungwon Lee, and Kwan-Liu Ma In Proc. IEEE Pacific Visualization Symposium, Visualization Note (Short Paper), Best Note Award, Apr 2015 PDF: http://vis.cs.ucdavis.edu/papers/ImmersiveGraphVis.pdf IEEE Xplore: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7156357
Views: 930 Oh-Hyun Kwon
Naïve Bayes Classifier -  Fun and Easy Machine Learning
Naive Bayes Classifier- 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 -------------------------------------------------------------------------------- Now Naïve Bayes is based on Bayes Theorem also known as conditional Theorem, which you can think of it as an evidence theorem or trust theorem. So basically how much can you trust the evidence that is coming in, and it’s a formula that describes how much you should believe the evidence that you are being presented with. An example would be a dog barking in the middle of the night. If the dog always barks for no good reason, you would become desensitized to it and not go check if anything is wrong, this is known as false positives. However if the dog barks only whenever someone enters your premises, you’d be more likely to act on the alert and trust or rely on the evidence from the dog. So Bayes theorem is a mathematic formula for how much you should trust evidence. So lets take a look deeper at the formula, • We can start of with the Prior Probability which describes the degree to which we believe the model accurately describes reality based on all of our prior information, So how probable was our hypothesis before observing the evidence. • Here we have the likelihood which describes how well the model predicts the data. This is term over here is the normalizing constant, the constant that makes the posterior density integrate to one. Like we seen over here. • And finally the output that we want is the posterior probability which represents the degree to which we believe a given model accurately describes the situation given the available data and all of our prior information. So how probable is our hypothesis given the observed evidence. So with our example above. We can view the probability that we play golf given it is sunny = the probability that we play golf given a yes times the probability it being sunny divided by probability of a yes. This uses the golf example to explain Naive Bayes. ------------------------------------------------------------ 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: 166174 Augmented Startups
Oracle DV - Custom map views on DV Desktop Analysis
Adding a Custom Layer (geoJSON): http://www.oracle.com/technetwork/middleware/downloads/custom-maplayer-in-va-2887099.pdf
Visual Clustering of High-dimensional Data  - August, 2011
By Wayne Oldford and Adrian Waddell University of Waterloo Visualisation of Complex Data Sets session at the ISI Dublin, Ireland. ISI 2011. Article: http://isi2011.congressplanner.eu/pdfs/650370.pdf Abstract: STS057.04 -- Visual Clustering of High-Dimensional Data by Navigating Low-Dimensional Spaces Wayne Oldford, Adrian Waddell Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada The structure of a set of high dimensional data objects (e.g. images, documents, molecules, genetic expressions, etc.) is notoriously difficult to visualize. In contrast, lower dimensional structure (esp. 3 or fewer dimensions) is natural to us and easy to visualize. A not unreasonable approach, then, is to explore one low dimensional visualization after another in the hope that, together, these will shed light on the higher dimensional structure. A familiar example is the parallel coordinate plot, where a sequence of one-dimensional projections are connected to provide insight into the structure of a high dimensional data set. In this talk, we describe the graph theoretic structure, recently proposed in Hurley and Oldford (2011, Comp. Stat.), that represents low-dimensional spaces as graph nodes and transitions between spaces as edges. Of interest, are walks along these graphs that reveal meaningful structure. If the nodes are one-dimensional, a walk corresponds to a parallel coordinate plot; if they are two dimensional and edges exist, say, only between 2d spaces which share a variate, then the walk could be represented dynamically as a series of scatterplots, one transitioning into the next via a 3d rigid transformation. We demonstrate how these graphs are constructed and dynamically explored via our R package, RnavGraph. These graphs, unfortunately, grow in size with the square of the number of dimensions. Fortunately, there are numerous means for constructing only the more interesting regions of each graph. Some restrictions are imposed by the statistical context, others by empirical measures on the data itself. Of the latter, scatterplot diagnostics (scagnostics) are especially valuable. When the objective is to visually cluster the data, nearest neighbour based dimension reduction methods are particularly effective in conjunction with appropriate scagnostics. We demonstrate these methods using RnavGraph. Keywords: Graph navigation, transition graphs; RnavGraph; High-dimensional data; Visual clustering Biography: Wayne Oldford is a Professor of Statistics and of Computer Science at the University of Waterloo, Canada. His research interests include data visualization, cluster analysis, quantitative programming environments, and statistical foundations. Prof. Oldford is a long-standing member of the International Statistical Institute, and even longer of the International Association for Statistical Computing.
Views: 3287 Wayne Oldford
Introduction to Data Visualization
Watch all our past and upcoming workshops on http://www.codeheroku.com In this workshop you will learn how to design custom data visualizations using JavaScript. We will use a popular JavaScript library called HighCharts to build our visualizations. What you'll learn: Choose the right tools to tell your story Fetch Data from an API Build visualizations using HighCharts Design and embed charts on your website Complete Code is here: http://www.codeheroku.com/static/workshop/code/chart.zip Sample HighCharts API used is: https://www.highcharts.com/samples/data/jsonp.php?filename=usdeur.json&callback=? Slides from the presentation are here: http://www.codeheroku.com/static/workshop/slides/Introduction-to-Data-Visualization.pdf
Views: 142 Code Heroku
What is a HashTable Data Structure - Introduction to Hash Tables , Part 0
This tutorial is an introduction to hash tables. A hash table is a data structure that is used to implement an associative array. This video explains some of the basic concepts regarding hash tables, and also discusses one method (chaining) that can be used to avoid collisions. Wan't to learn C++? I highly recommend this book http://amzn.to/1PftaSt Donate http://bit.ly/17vCDFx STILL NEED MORE HELP? Connect one-on-one with a Programming Tutor. Click the link below: https://trk.justanswer.com/aff_c?offer_id=2&aff_id=8012&url_id=238 :)
Views: 812319 Paul Programming
Data Cleaning Tutorial (2018) | Cleaning Data With Python and Pandas
This data cleaning tutorial will introduce you to Python's Pandas Library in 2018. Check out our website for the best Data Science tips in 2018: https://www.dataoptimal.com Subscribe for even more Data Science tutorials! https://bit.ly/2J2O5N8 Follow us on Twitter! https://twitter.com/DataOptimal **Video Resources** Full article: https://www.dataoptimal.com/data-cleaning-with-python-2018/ Dataset: https://github.com/dataoptimal/videos/tree/master/cleaning%20messy%20data%20with%20pandas Pandas link: http://pandas.pydata.org/pandas-docs/version/0.21/indexing.html#indexing-label Error handling in Python: https://docs.python.org/3/tutorial/errors.html Matt Brems material on missing values: https://github.com/matthewbrems/ODSC-missing-data-may-18/blob/master/Analysis%20with%20Missing%20Data.pdf It's the start of a new project and you're excited to apply some machine learning models. You take a look at the data and quickly realize it's an absolute mess. According to IBM Data Analytics you can expect to spend up to 80% of your time on a project cleaning data. There's all different types of messy data, but today we're going to focus on one of the most common, missing values. We'll take a look at standard types that Pandas recognizes out of the box. Next we'll take a look at some non-standard types. These are inputs that Pandas won't automatically recognize as missing values. After that we'll take a look at unexpected types. Let's say you have a column of names that contains a 12, technically that's a missing value. After we've finished detecting missing values we'll learn how to summarize and do simple replacements.
Views: 12144 DataOptimal
Visual analytics _ Amir Mosavi
To show how Grapheur provides the user an effective way to select best worker of the year with the help of 7D plot. References: 1Hewage K.N., Gannoruwa A., Ruwanpura J.Y. (2011), Current Status of Factors Leading to Team Performance of On-Site Construction Professionals in Alberta Building Construction Projects, Canadian Journal of Civil Engineering (in press). 2Battiti, Roberto; Andrea Passerini (2010). "Brain-Computer Evolutionary Multi-Objective Optimization (BC-EMO): a genetic algorithm adapting to the decision maker." (PDF). IEEE Transactions on Evolutionary Computation 14 (15): 671--687 3Roberto Battiti and Mauro Brunato, Reactive Business Intelligence. From Data to Models to Insight, Reactive Search Srl, Italy, February 2011.
Views: 202 Amir Mosavi
The best stats you've ever seen | Hans Rosling
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: 2913782 TED
Visual Diagnostics for More Informed Machine Learning Within and Beyond Scikit-Learn - PyCon 2016
"Speaker: Rebecca Bilbro Visualization has a critical role to play throughout the analytic process. Where static outputs and tabular data may render patterns opaque, human visual analysis can uncover volumes and lead to more robust programming and better data products. For Python programmers who dabble in machine learning, visual diagnostics are a must-have for effective feature analysis, model selection, and evaluation. Slides can be found at: https://speakerdeck.com/pycon2016 and https://github.com/PyCon/2016-slides"
Views: 2007 PyCon 2016
The Data Science Process - Data Visualization and D3.js
This video is part of an online course, Data Visualization and D3.js. Check out the course here: https://www.udacity.com/course/ud507. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 1348 Udacity
Shmoocon 2012: Malware Visualization in 3D
This video is part of the Infosec Video Collection at SecurityTube.net: http://www.securitytube.net Shmoocon 2012: Malware Visualization in 3D PDF :- http://www.shmoocon.org/2012/presentations/Danny_Quist-3dmalware-shmoocon2012.pdf Malware reverse engineering is greatly helped by visualization techniques. In this talk I will show you my 3D visualization enhancements to VERA for creating compelling, and useful displays of malware. This new tool provides a new method to visualize running code, show concurrent running threads of execution, visualize the temporal relationships of the code, and illustrate complicated packer original entry point detection. Real! Live! Reverse Engineering! of the past year of malware will show the utility of the program on in-the-wild samples. Danny Quist is a research scientist at Los Alamos National Laboratory and the founder of Offensive Computing, LLC. His research is in automated analysis methods for malware with software and hardware assisted techniques. He consults with both private and public sectors on system and network security. His interests include malware defense, reverse engineering, exploitation methods, virtual machines, and automatic classification systems. Danny holds a Ph.D. from the New Mexico Institute of Mining and Technology. He is the master of the Five Point Exploding Packer Technique. Danny has presented at several industry conferences including Blackhat, RSA, ShmooCon, Vizsec, and Defcon.
Views: 1727 SecurityTubeCons
Become an Excel Wizard With Python
In this talk, we will explore how the Python's openpyxl module allows your Python programs to read and modify Excel spreadsheet files. By using Python, you can take your Excel and data manipulation skills to the whole new level. PERMISSIONS: The original video was published on Six Feet Up Corp YouTube channel with the Creative Commons Attribution license (reuse allowed). CREDITS: Original video source: https://www.youtube.com/watch?v=ueq1iTWQU5U Additional recommended material for Python learners: https://amzn.to/2UMFhRt Python Programming: A Step By Step Guide From Beginner To Expert https://amzn.to/2JsiyZX A Smarter Way to Learn Python: Learn it faster. Remember it longer. https://amzn.to/2CwoGKu Python Crash Course: A Hands-On, Project-Based Introduction to Programming https://amzn.to/2Fi4cG9 Python Programming: An Introduction to Computer Science
Views: 304798 Coding Tech
Datawatch's New Visual Data Discovery solution
Rami Chahine, product manager at Datawatch demonstrates how Datawatch provides organizations with the ability to analyze and understand Any Data Variety, regardless of structure, at Real-time Velocity, through an unmatched Visual Data Discovery environment.
Intro to R Visualizations in Microsoft Power BI
Microsoft’s Power BI is a powerful technology for quickly creating rich visualizations. Power BI has many practical uses for the modern data professional including executive dashboards, operational dashboards, and visualizations for data exploration/analysis. Microsoft has also extended Power BI with support for incorporating R visualizations into Power BI projects, enabling a myriad of data visualization use cases across all industries and circumstances. As such, Power BI is an extremely valuable tool for any Data Analyst, Product/Program Manager, or Data Scientist to have in their tool belt. At this meetup presenter Dave Langer will provide a hands-on introduction to using R visualizations within Power BI using Microsoft’s free Power BI Desktop software. Both Power BI project and .CSV data files will be accessible via GitHub prior to the Meetup presentation. Overview of Power BI and where it fits in a data professional’s tool belt. · Understanding the nuances of how Power BI integrates with R, including necessary data preprocessing. · A brief introduction to the R ggplot2 visualization package. · Incorporating R visualizations into Power BI projects. · Making R visualizations dynamic by responding to Power BI UX controls. GitHub Files: https://github.com/datasciencedojo/meetup/tree/master/r_visualization_with_power_bi Find out more about David here: https://www.meetup.com/data-science-dojo/events/237941790/ -- Learn more about Data Science Dojo here: https://hubs.ly/H0hC3lg0 Watch the latest video tutorials here: https://hubs.ly/H0hC4c_0 See what our past attendees are saying here: https://hubs.ly/H0hC4d80 -- Like Us: https://www.facebook.com/datasciencedojo/ Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data-science-dojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo/ Vimeo: https://vimeo.com/datasciencedojo
Views: 6037 Data Science Dojo
SmartAdP: Visual Analytics of Large-scale Taxi Trajectories for Selecting Billboard Locations
The author's personal website: http://dongyu.tech More information please refer to the paper: http://dongyu.name/papers/tvcg_2016_dongyu_smartadp.pdf Online System: http://smartadp.chinacloudapp.cn/ Abstract: The problem of formulating solutions immediately and comparing them rapidly for billboard placements has plagued advertising planners for a long time, owing to the lack of efficient tools for in-depth analyses to make informed decisions. In this study, we attempt to employ visual analytics that combines the state-of-the-art mining and visualization techniques to tackle this problem using large-scale GPS trajectory data. In particular, we present SmartAdP, an interactive visual analytics system that deals with the two major challenges including finding good solutions in a huge solution space and comparing the solutions in a visual and intuitive manner. An interactive framework that integrates a novel visualization-driven data mining model enables advertising planners to effectively and efficiently formulate good candidate solutions. In addition, we propose a set of coupled visualizations: a solution view with metaphor-based glyphs to visualize the correlation between different solutions; a location view to display billboard locations in a compact manner; and a ranking view to present multi-typed rankings of the solutions. This system has been demonstrated using case studies with a real-world dataset and domain-expert interviews. Our approach can be adapted for other location selection problems such as selecting locations of retail stores or restaurants using trajectory data.
Views: 48 Dongyu Liu
Smart Brushing for Parallel Coordinates
Smart Brushing for Parallel Coordinates by Richard C. Roberts, Robert S. Laramee, Gary A. Smith, Paul Brookes, Tony D’Cruze in IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG), forthcoming Abstract: The Parallel Coordinates plot is a popular tool for the visualization of high-dimensional data. One of the main challenges when using parallel coordinates is occlusion and overplotting resulting from large data sets. Brushing is a popular approach to address these challenges. Since its conception, limited improvements have been made to brushing both in the form of visual design and functional interaction. We present a set of novel, smart brushing techniques that enhance the standard interactive brushing of a parallel coordinates plot. We introduce two new interaction concepts: Higher-order, sketch-based brushing, and smart, data-driven brushing. Higher-order brushes support interactive, flexible, n-dimensional pattern searches involving an arbitrary number of dimensions. Smart, data-driven brushing provides interactive, real-time guidance to the user during the brushing process based on derived meta-data. In addition, we implement a selection of novel enhancements and user options that complement the two techniques as well as enhance the exploration and analytical ability of the user. We demonstrate the utility and evaluate the results using a case study with a large, high-dimensional, real-world telecommunication data set and we report domain expert feedback from the data suppliers. PDF: http://cs.swan.ac.uk/~csbob/research/callCenter/brushing/roberts18smart.pdf
Views: 124 DataVisBob Laramee
Visualizing Data Using t-SNE
Google Tech Talk June 24, 2013 (more info below) Presented by Laurens van der Maaten, Delft University of Technology, The Netherlands ABSTRACT Visualization techniques are essential tools for every data scientist. Unfortunately, the majority of visualization techniques can only be used to inspect a limited number of variables of interest simultaneously. As a result, these techniques are not suitable for big data that is very high-dimensional. An effective way to visualize high-dimensional data is to represent each data object by a two-dimensional point in such a way that similar objects are represented by nearby points, and that dissimilar objects are represented by distant points. The resulting two-dimensional points can be visualized in a scatter plot. This leads to a map of the data that reveals the underlying structure of the objects, such as the presence of clusters. We present a new technique to embed high-dimensional objects in a two-dimensional map, called t-Distributed Stochastic Neighbor Embedding (t-SNE), that produces substantially better results than alternative techniques. We demonstrate the value of t-SNE in domains such as computer vision and bioinformatics. In addition, we show how to scale up t-SNE to big data sets with millions of objects, and we present an approach to visualize objects of which the similarities are non-metric (such as semantic similarities). This talk describes joint work with Geoffrey Hinton.
Views: 126940 GoogleTechTalks
Molecular Dynamics, The Shovel for Data Mining Neutron Scattering Data
Some of the uses of neutron scattering experiments of disordered, biologically relevant systems as a test for molecular dynamics simulations. Also covered are how molecular dynamics simulations can be used as interpretive tools for neutron scattering data.
Views: 2714 thunderf00tCC
Normal Distribution - Explained Simply (part 1)
*** IMPROVED VERSION of this video here: https://youtu.be/tDLcBrLzBos I describe the standard normal distribution and its properties with respect to the percentage of observations within each standard deviation. I also make reference to two key statistical demarcation points (i.e., 1.96 and 2.58) and their relationship to the normal distribution. Finally, I mention two tests that can be used to test normal distributions for statistical significance. normal distribution, normal probability distribution, standard normal distribution, normal distribution curve, bell shaped curve
Views: 1131177 how2stats
Data Mining with Weka (4.5: Support vector machines)
Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 5: Support vector machines http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/augc8F https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 46237 WekaMOOC
Visual Exploration of Smoking Cessation Data, presented by Polo Chau and Moushumi Sharmin
About the webinar Part 1: Discovery Dashboard Drs. Chau and Sharmin will present Discovery Dashboard [1], a visual analytics system for exploring large volumes of time series data from mobile medical field studies, in the web-browser and in real time. Discovery Dashboard offers interactive exploration tools and a data mining motif discovery algorithm to help researchers formulate hypotheses, discover trends and patterns, and ultimately gain a deeper understanding of their data. They will demonstrate their system visualizing data from a mobile sensor study conducted at the University of Minnesota that included 52 participants who were trying to quit smoking. Part 2: MyQuitPal The first step in designing effective smoking cessation systems is to objectively identify factors that contribute to lapse. To this end, we present MyQuitPal [2], a participant-centric cessation support system, which aims to assist individuals to better understand their smoking behavior. MyQuitPal combines an affective mobile application [2] and a web-based analytics tool [3] to support reflection. The design of MyQuitPal is informed by in-depth analysis of physiological data collected utilizing wearable sensors from a four day pre-quit, post-quit study (N=55). Visualizations presented in MyQuitPal are also grounded on theories of long term health-behavior change. About the presenters Polo Chau Ph.D., is an Assistant Professor at Georgia Tech’s School of Computational Science and Engineering, and an Associate Director of the MS Analytics program. He holds a Ph.D. in Machine Learning and a Masters in human-computer interaction (HCI). His Ph.D. thesis won Carnegie Mellon’s Computer Science Dissertation Award, Honorable Mention. His research group bridges data mining and HCI — innovates at their intersection — to synthesize scalable, interactive tools that help people understand and interact with big data. His group has created scalable deep learning visualization tools (deployed by Facebook), interactive graph querying system (SIGMOD'17 Best Demo, honrable mention), novel detection technologies for malware (patented with Symantec, protects 120M+ people), auction fraud (WSJ, CNN, MSN), comment spam (patented & deployed with Yahoo), fake reviews (SDM’14 Best Student Paper), insider trading (SEC), unauthorized mobile device access (Wired, Engadget); and fire risk prediction (KDD’16 Best Student Paper, runner up). He received faculty awards from Google, Yahoo, and LexisNexis. Dr. Chau also received the Raytheon Faculty Fellowship, Edenfield Faculty Fellowship, Outstanding Junior Faculty Award. He is the only two-time Symantec fellow. He leads the popular annual IDEA workshop that catalyzes cross-pollination across HCI and data mining. He served as general chair for ACM IUI 2015, and is a steering committee member of the conference. Moushumi Sharmin, Ph.D., is an Assistant Professor of Computer Science department at the Western Washington University. At Western, Dr. Sharmin co-directs the NEAT (Novel, Effective, Affective Technology) Research Lab, which focuses on designing participant-centric affective technology. Her research focuses on human-computer interaction, affective computing, and technology design. Currently she is investigating novel visualization techniques that support sense-making, pattern identification, and decision making of large scale data for behavioral health problems including autism spectrum disorder, and addiction, and harassment prevention. Students at the NEAT Lab have presented their work on addiction (GHC2017 - ACM SRC 2017 Runner-up (Undergraduate Category), CompSAC 2017), and autism (SIGCHI 2018). Dr. Sharmin is serving as the program committee chair for the Human Computing and Social Computing (HCSC) Symposium for IEEE CompSAC 2016, 2017 and 2018. She is a member of Google’s Women TechMakers and a fellow of the American Association of University Women. [1] mHealth Visual Discovery Dashboard. Dezhi Fang, Fred Hohman, Peter Polack, Hillol Sarker, Minsuk Kahng, Moushumi Sharmin, Mustafa al'Absi, Duen Horng (Polo) Chau. Demo, ACM International Joint Conference on Pervasive and Ubiquitous Computing (UBICOMP) Sept 11-15, 2017. Maui, USA. Paper: https://www.cc.gatech.edu/~dchau/papers/17-ubicomp-dashboard.pdf Video: https://youtu.be/vpvozWf1aCc [2] Opportunities and Challenges in Designing Participant-Centric Smoking Cessation System. Moushumi Sharmin, Theodore Weber, Hillol Sarker, Nazir Saleheen, Santosh Kumar, Shameem Ahmed, Mustafa al’ Absi. IEEE Computer Software and Applications Conference (COMPSAC), 2017, 835-844, Turin, Italy. Paper: https://www.academia.edu/35807876/Opportunities_and_Challenges_in_Designing_Participant-Centric_Smoking_Cessation_System Video: http://myweb.students.wwu.edu/webert3/
Views: 119 MD2K Center
Inductive Visual Miner
This video is part of a series showcasing the use of the ProM process mining framework. Each video focusses on a specific process mining task or algorithm. ProM is open-source and freely available at: http://www.promtools.org In this video we introduce the Inductive visual Miner, one of the process discovery algorithms available in ProM. The Inductive visual Miner provides an interactive visualization for process exploration, performance analysis and deviation detection. A brief overview of the Inductive visual Miner is also provided in: http://ceur-ws.org/Vol-1295/paper19.pdf For more information on process mining, please visit: http://www.processmining.org/ Created by: Sander Leemans, Elham Ramezani
Views: 3791 P2Mchannel
DEFCON 18: Social Networking Special Ops: Extending Data Visualization Tools for Faster Pwnage 3/4
Speaker: Chris "The Suggmeister" Sumner If you're ever in a position when you need to pwn criminals via social networks or see where Tony Hawk likes to hide skateboards around the world, this talk is for you. The talk is delivered in two parts, both of which are intended to shine a fun light on visual social network analysis. The first part introduces how you can extend the powerful data visualization tool, Maltego to speed up and automate the data mining and analysis of social networks. I'll show how I analyzed skateboard legend, Tony Hawk's twitter hunt and highlight how you could use the same techniques to set up your very own backyard miniature ECHELON. The second part illustrates how these techniques have been used to enumerate a 419 scam, infiltrate the scammers social network and expose deeper, more sinister links to organized crime. I focus specifically on Twitter and Facebook, demonstrating how you can graphically map and analyze social relationships using the Twitter API's, publicly available Facebook profiles, screen scraping and some clunky regex." Related to this talk is the DEF CON Twitter Hunt Each day at DEF CON you will have an opportunity to blag yourself a sweet limited edition DEF CON-ized skateboard deck. There may also be a couple of signed Tony Hawk decks slung in for good measure too... who knows. You will have to follow @TheSuggmeister during DEF CON to know where to look. He'll be tweeting clues which lead to prizes. Hashtag #DCTH' For presentations, whitepapers or audio version of the Defcon 18 presentations visit: http://defcon.org/html/links/dc-archives/dc-18-archive.html
Views: 946 Christiaan008
Jupyter Notebook Tutorial: Introduction, Setup, and Walkthrough
In this Python Tutorial, we will be learning how to install, setup, and use Jupyter Notebooks. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Let's get started. ✅ Support My Channel Through Patreon: https://www.patreon.com/coreyms ✅ Become a Channel Member: https://www.youtube.com/channel/UCCezIgC97PvUuR4_gbFUs5g/join ✅ One-Time Contribution Through PayPal: https://goo.gl/649HFY ✅ Cryptocurrency Donations: Bitcoin Wallet - 3MPH8oY2EAgbLVy7RBMinwcBntggi7qeG3 Ethereum Wallet - 0x151649418616068fB46C3598083817101d3bCD33 Litecoin Wallet - MPvEBY5fxGkmPQgocfJbxP6EmTo5UUXMot ✅ Corey's Public Amazon Wishlist http://a.co/inIyro1 ✅ Equipment I Use and Books I Recommend: https://www.amazon.com/shop/coreyschafer ▶️ You Can Find Me On: My Website - http://coreyms.com/ My Second Channel - https://www.youtube.com/c/coreymschafer Facebook - https://www.facebook.com/CoreyMSchafer Twitter - https://twitter.com/CoreyMSchafer Instagram - https://www.instagram.com/coreymschafer/ #Python
Views: 605839 Corey Schafer
MicroStrategy - Data Mining & Predictive Analytics - Online Training Video by MicroRooster
Source: MicroRooster.blogspot.com Format: A MicroStrategy Online Training Video blog. Description: An introduction to Data Mining & Predictive Analytics using MicroStrategy. This demo explains how to use MicroStrategy for performing advanced data science analysis. Must have some understanding of basic data mining to take advantage of this entry level demo.
Views: 17213 MicroRooster
Data Discovery and Visualization Template
SAP Design Studio's Data Discovery and Visualization template in action.
Views: 370 david stocker
Force-Directed Parallel Coordinates (with VoiceOver)
Rick Walker, Phillip A. Legg, Serban Pop, Zhao Geng, Robert S. Laramee, and Jonathan C. Roberts, Force-Directed Parallel Coordinates, Proceedings of the Conference on Information Visualization (IV) 2013, 15-18 July, University of London, London, UK Abstract: Parallel coordinates are a well-known and valuable technique for the analysis and visualization of high dimensional data sets. However, while Inselberg emphasizes that the strength of parallel coordinates as a methodology is rooted in exploration and interactivity, the set of interaction techniques is currently limited. Axes can be re-ordered and brushing (simple, angular or multi-dimensional) can be performed. In this paper, we propose a force-directed algorithm and related interaction techniques to support the exploration of parallel coordinate plots through a physical metaphor. Our parallel-coordinates visualization offers novel user interaction beyond the standard techniques by allowing the user to rotate the axis according to forcedirected polylines. The new interaction provides the user with a more immersive experience for data exploration that results in greater intuition of the data, especially in cases where many polylines overlap. We demonstrate our approach, then present the results of a qualitative evaluation of the system. PDF http://cs.swan.ac.uk/~csbob/research/parallelCoords/forceDirected/legg13force.pdf DOI http://dx.doi.org/10.1109/IV.2013.101
Views: 127 DataVisBob Laramee
Intro to Julia for data science
Join us on July 25 (10AM PDT/1PM EDT/19:00CET/10:30PM IST) for a tutorial with Huda Nassar! Huda is a PhD candidate at Purdue and author of `MatrixNetworks.jl`. In this tutorial, she will show how to work with your data in Julia, including data processing, algorithms, and visualizations You can follow along and interact with tutorial materials without installing anything at juliabox.com. See you on the 25th! Visit http://julialang.org/ to download Julia.
Views: 20921 The Julia Language
VISAGE:  Interactive Visual Graph Querying
Extracting useful patterns from large network datasets has become a fundamental challenge in many domains. We present VISAGE, an interactive visual graph querying approach that empowers users to construct expressive queries, without writing complex code (e.g.,finding money laundering rings of bankers and business owners).Our contributions are as follows: (1) we introduce graph-autocomplete, an interactive approach that guides users to construct and refine queries, preventing over-specification; (2) VISAGE guides the construction of graph queries using a data-driven approach, enabling users to specify queries with varying levels of specificity, from concrete and detailed (e.g., query by example), to abstract (e.g., with “wildcard” nodes of any types), to purely structural matching; (3)a twelve-participant, within-subject user study demonstrates VISAGE’s ease of use and the ability to construct graph queries significantly faster than using a conventional query language; (4) VISAGE works on real graphs with over 468K edges, achieving sub-second response times for common queries Robert's homepage: http://www.cc.gatech.edu/~rpienta3/ Paper: http://www.cc.gatech.edu/~dchau/papers/16-avi-visage.pdf VISAGE: Interactive Visual Graph Querying. Robert Pienta, Acar Tamersoy, Alex Endert, Shamkant B. Navathe, Hanghang Tong, Duen Horng (Polo) Chau International Working Conference on Advanced Visual Interfaces (AVI 2016). June 7-10, 2016. Bari, Italy.
How does a blockchain work - Simply Explained
What is a blockchain and how do they work? I'll explain why blockchains are so special in simple and plain English! 💰 Want to buy Bitcoin or Ethereum? Buy for $100 and get $10 free (through my affiliate link): https://www.coinbase.com/join/59284524822a3d0b19e11134 📚 Sources can be found on my website: https://www.savjee.be/videos/simply-explained/how-does-a-blockchain-work/ 🐦 Follow me on Twitter: https://twitter.com/savjee ✏️ Check out my blog: https://www.savjee.be ✉️ Subscribe to newsletter: https://goo.gl/nueDfz 👍🏻 Like my Facebook page: https://www.facebook.com/savjee
Views: 2929151 Simply Explained - Savjee
Cartographic Treemaps for the Visualization of Public Healthcare Data
Chao Tong, Richard Roberts, Robert S Laramee, Daniel Thayer, Damon Berridge, Cartographic Treemaps for the Visualization of Public Healthcare Data, The Computer Graphics and Visual Computing (CGVC) Conference 2017, forthcoming, 14-15 September 2017, Manchester, UK PDF http://cs.swan.ac.uk/~csbob/research/cartographic/tong17cartographic.pdf Abstract: The National healthcare Service (NHS) in the UK collects a massive amount of high-dimensional, region-centric data concerning individual healthcare units throughout Great Britain. It is challenging to visually couple the large number of multivariate attributes about each region unit together with the geo-spatial location of the clinical practices for visual exploration, analysis, and comparison. We present a novel multivariate visualization we call a cartographic treemap that attempts to combine the space-filling advantages of treemaps for the display of hierarchical, multivariate data together with the relative geo-spatial location of NHS practices in the form of a modified cartogram. It offers both space filling and geospatial error metrics that provide the user with interactive control over the space-filling versus geographic error trade-off. The result is a visualization that offers users a more space efficient overview of the complex, multivariate healthcare data coupled with the relative geo-spatial location of each practice to enable and facilitate exploration, analysis, and comparison. We evaluate the two metrics and demonstrate the use of our approach on real, large high-dimensional NHS data and derive a number of multivariate observations based on healthcare in the UK as a result. We report the reaction of our software from two domain experts in health science.
Views: 101 DataVisBob Laramee
Visual analytics _ Amir Mosavi
Here the idea for solving the multiple criteria decision making problems is to visually and effectively model the problem and clarify the whole dimension of it. We use Grapheur, the flexible and power full business intelligence and interactive visualization. We are dealing with the series of multiple criteria decision making problems related to construction workers of Canadian construction projects. For instance we want to figure out with which rate and how, should the workers' level of skills grow in order to maintain their performances with regard to team perceptions of supervision. In the figure, a 3D plot of the network is given by the 23 workers. The color code represents the specialization of the workers and the size of the bubbles is proportional to the idleness of each worker. As you see for a part of our project we are considering the 3D similarity map and on the left side of the screen, the parallel filters, optimizing the idleness characteristic of the workers with respect to our mentioned objectives. In our 3D similarity map of graphical visualization, the gray level of the edges and the generated clusters provide the valuable information for decision makers. The capability of the Similarity Map in clustering the workers into different clusters is illustrated. Parallel filters are other useful tools for optimization. The usefulness of parallel filters in reducing the complexity of decision making is evaluated. References: 1Hewage K.N., Gannoruwa A., Ruwanpura J.Y. (2011), Current Status of Factors Leading to Team Performance of On-Site Construction Professionals in Alberta Building Construction Projects, Canadian Journal of Civil Engineering (in press). 2Battiti, Roberto; Andrea Passerini (2010). "Brain-Computer Evolutionary Multi-Objective Optimization (BC-EMO): a genetic algorithm adapting to the decision maker." (PDF). IEEE Transactions on Evolutionary Computation 14 (15): 671--687 3Roberto Battiti and Mauro Brunato, Reactive Business Intelligence. From Data to Models to Insight, Reactive Search Srl, Italy, February 2011.
Views: 582 Amir Mosavi
Visual Analysis of Historic Hotel Visitation Patterns
This video describes an interactive visual tool for exploring the visitation patterns of guests at two hotels in central Pennsylvania from 1894 to 1900. It is implemented as a coordinated multiple view visualization in Improvise, a a desktop application developed by Chris Weaver for building and browsing visual interfaces that perform highly interactive querying of multidimensional data sets. To read a full paper about this work, see: http://www.cs.ou.edu/~weaver/academic/publications/weaver-2007b.pdf For more about Improvise, visit http://www.cs.ou.edu/~weaver/improvise/index.html Please cite as: Chris Weaver, David Fyfe, Anthony Robinson, Deryck Holdsworth, Donna Peuquet, Alan M. MacEachren 2006. Visual Analysis of Historic Hotel Visitation Patterns, Video Posted on Youtube, Sept 24, 2010 (produced to accompany C. Weaver, D. Fyfe, A.C. Robinson, D. Holdsworth, D. Peuquet, A.M. MacEachren, "Visual Analysis of Historic Hotel Visitation Patterns," IEEE Symposium on Visual Analytics Science and Technology 2006, Baltimore, MD, pp. 35-42 2006.)
Views: 945 GeoVISTACenter
Datawatch and Tableau
In today’s high speed analytics marketplace it is no surprise that data volumes and sources are expanding at an accelerating rate. On a daily basis, analysts spend up to 80 percent of their time collecting data from numerous sources such as the web, pdf’s, text reports, log files and many more to prepare it for analysis. Analysts are further challenged to make this data actionable with the use of data discovery and business analytics. The alliance between Datawatch and Tableau offers businesses the fastest and most easy-to-use applications which significantly reduce time spent on data extraction, data preparation, and data analytics Datawatch Monarch works with a wide range of report formats including PDF, XML, HTML, text, spool and ASCII files. Analysts can easily access data from invoices, sales reports, balance sheets, customer lists, inventory, logs and more. Data is then cleansed and consolidated into a single Tableau Data Extract TDE file for immediate use in Tableau. Analysts can now focus on translating their data into business value, using Tableau’s world-wide recognized self-service tools for visualization and analytics. Tableau enables business users to create and deliver relevant and up-to-date decision support in the form of visual data discovery dashboards and reports to their entire organization. The combination of Datawatch data preparation and Tableau means that all available data is included. Let us help you provide your business with the fastest and easiest tools for data acquisition, preparation, and business analytics.
10.1: Introduction to Data and APIs in JavaScript - p5.js Tutorial
This video introduces the idea of using external data in a p5.js sketch. What are the various formats -- JSON, tabular data, XML? What is an API? Wind map: http://hint.fm/wind/ All examples: https://github.com/shiffman/Video-Lesson-Materials Contact: https://twitter.com/shiffman Next video: https://youtu.be/_NFkzw6oFtQ JavaScript basics: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6Zy51Q-x9tMWIv9cueOFTFA HTML/CSS basics: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6bI1SlcCRfLH79HZrFAtBvX Full Data playlist: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6a-SQiI4RtIwuOrLJGnel0r Help us caption & translate this video! http://amara.org/v/QbuI/
Views: 98791 The Coding Train
DelViz - Deep exploration and lookup of Visualizations
Weitere Informationen unter: www.delviz.com
Views: 641 MediaDesignTUD