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Introduction to data mining and architecture  in hindi
 
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Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho  :  https://goo.gl/85HQGm for full notes   please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made  notes of data warehouse and data mining  its only 200rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 136498 Last moment tuitions
Extracting and Mining Of Data From PDF and WEB
 
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ABSTRACT In most of the Universities, results are published on web or send via PDF files. Currently many of the colleges use manual process to analyze the results. Sadly the college staff has to manually fill the student result details and then analyze the rankings accordingly. Our proposed system will extract the data automatically from PDF and web, create dynamic database and analyze data, for this system make use of PDF Extractor, Pattern matching techniques, data mining, Web mining technique and sorting technique.
How to predict students' performance?
 
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Talk presented at SSCI2014, in Orlando. Download paper from: http://personal.ee.surrey.ac.uk/Personal/Norman.Poh/data/poh_gradcert.pdf Abstract: Student performance depends upon factors other than intrinsic ability, such as environment, socio-economic status, personality and familial-context. Capturing these patterns of influence may enable an educator to ameliorate some of these factors, or for governments to adjust social policy accordingly. In order to understand these factors, we have undertaken the exercise of predicting student performance, using a cohort of approximately 8,000 South African college students. They all took a number of tests in English and Maths. We show that it is possible to predict English comprehension test results from (1) other test results; (2) from covariates about self-efficacy, social economic status, and specific learning difficulties there are 100 survey questions altogether; (3) from other test results + covariates (combination of (1) and (2)); and from (4) a more advanced model similar to (3) except that the covariates are subject to dimensionality reduction (via PCA). Models 1-4 can predict student performance up to a standard error of 13-15%. In comparison, a random guess would have a standard error of 17%. In short, it is possible to conditionally predict student performance based on self-efficacy, socio-economic background, learning difficulties, and related academic test results.
Views: 4884 Norman Poh
KEEL Data mining tool demo
 
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KEEL Data minig tool Demo of installation and Working
Views: 3462 Manukumar K J
Topic 2: Mineral Exploration
 
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In the second installment of the 'Technically Speaking' series, Tom Bruington, mining engineer with Sandstorm Gold, discusses the tools and concepts behind mineral exploration. Tom’s lesson covers both simple and more complicated exploration techniques ranging from mapping to geochemistry to drilling.
PDF Data Extraction and Automation 3.1
 
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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: 93353 UiPath
Weka Data Mining Tutorial for First Time & Beginner Users
 
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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 415684 Brandon Weinberg
INTRODUCTION TO DATA MINING IN HINDI
 
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Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 98288 LearnEveryone
More Data Mining with Weka (3.6: Evaluating clusters)
 
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More Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 6: Evaluating clusters http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/nK6fTv https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 19178 WekaMOOC
Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: inf[email protected]
Views: 5062 ClickMyProject
Topic Detection with Text Mining
 
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Meet the authors of the e-book “From Words To Wisdom”, right here in this webinar on Tuesday May 15, 2018 at 6pm CEST. Displaying words on a scatter plot and analyzing how they relate is just one of the many analytics tasks you can cover with text processing and text mining in KNIME Analytics Platform. We’ve prepared a small taste of what text mining can do for you. Step by step, we’ll build a workflow for topic detection, including text reading, text cleaning, stemming, and visualization, till topic detection. We’ll also cover other useful things you can do with text mining in KNIME. For example, did you know that you can access PDF files or even EPUB Kindle files? Or remove stop words from a dictionary list? That you can stem words in a variety of languages? Or build a word cloud of your preferred politician’s talk? Did you know that you can use Latent Dirichlet Allocation for automatic topic detection? Join us to find out more! Material for this webinar has been extracted from the e-book “From Words to Wisdom” by Vincenzo Tursi and Rosaria Silipo: https://www.knime.com/knimepress/from-words-to-wisdom At the end of the webinar, the authors will be available for a Q&A session. Please submit your questions in advance to: [email protected] This webinar only requires basic knowledge of KNIME Analytics Platform which you can get in chapter one of the KNIME E-Learning Course: https://www.knime.com/knime-introductory-course
Views: 1535 KNIMETV
Import Data and Analyze with MATLAB
 
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Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip
Views: 324890 APMonitor.com
Data Mining with Weka (1.6: Visualizing your data)
 
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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: 61051 WekaMOOC
Introduction to Datawarehouse in hindi
 
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Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho  :  https://goo.gl/85HQGm for full notes   please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made  notes of data warehouse and data mining  its only 200 rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 200286 Last moment tuitions
Data Mining: How You're Revealing More Than You Think
 
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Data mining recently made big news with the Cambridge Analytica scandal, but it is not just for ads and politics. It can help doctors spot fatal infections and it can even predict massacres in the Congo. Hosted by: Stefan Chin Head to https://scishowfinds.com/ for hand selected artifacts of the universe! ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters: Lazarus G, Sam Lutfi, Nicholas Smith, D.A. Noe, سلطان الخليفي, Piya Shedden, KatieMarie Magnone, Scott Satovsky Jr, Charles Southerland, Patrick D. Ashmore, Tim Curwick, charles george, Kevin Bealer, Chris Peters ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://www.aaai.org/ojs/index.php/aimagazine/article/viewArticle/1230 https://www.theregister.co.uk/2006/08/15/beer_diapers/ https://www.theatlantic.com/technology/archive/2012/04/everything-you-wanted-to-know-about-data-mining-but-were-afraid-to-ask/255388/ https://www.economist.com/node/15557465 https://blogs.scientificamerican.com/guest-blog/9-bizarre-and-surprising-insights-from-data-science/ https://qz.com/584287/data-scientists-keep-forgetting-the-one-rule-every-researcher-should-know-by-heart/ https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853 http://dml.cs.byu.edu/~cgc/docs/mldm_tools/Reading/DMSuccessStories.html http://content.time.com/time/magazine/article/0,9171,2058205,00.html https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all&_r=0 https://www2.deloitte.com/content/dam/Deloitte/de/Documents/deloitte-analytics/Deloitte_Predictive-Maintenance_PositionPaper.pdf https://www.cs.helsinki.fi/u/htoivone/pubs/advances.pdf http://cecs.louisville.edu/datamining/PDF/0471228524.pdf https://bits.blogs.nytimes.com/2012/03/28/bizarre-insights-from-big-data https://scholar.harvard.edu/files/todd_rogers/files/political_campaigns_and_big_data_0.pdf https://insights.spotify.com/us/2015/09/30/50-strangest-genre-names/ https://www.theguardian.com/news/2005/jan/12/food.foodanddrink1 https://adexchanger.com/data-exchanges/real-world-data-science-how-ebay-and-placed-put-theory-into-practice/ https://www.theverge.com/2015/9/30/9416579/spotify-discover-weekly-online-music-curation-interview http://blog.galvanize.com/spotify-discover-weekly-data-science/ Audio Source: https://freesound.org/people/makosan/sounds/135191/ Image Source: https://commons.wikimedia.org/wiki/File:Swiss_average.png
Views: 132254 SciShow
DEFCON 17: Dangerous Minds: The Art of Guerrilla Data Mining
 
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Speaker: Mark Ryan Del Moral Talabis Senior Consultant, Secure-DNA Consulting It is not a secret that in today's world, information is as valuable or maybe even more valuable that any security tool that we have out there. Information is the key. That is why the US Information Awareness Office's (IAO) motto is "scientia est potential", which means "knowledge is power". The IAO just like the CIA, FBI and others make information their business. Aside from these there are multiple military related projects like TALON,ECHELON, ADVISE, and MATRIX that are concerned with information gathering and analysis. The goal of the Veritas Project is to model itself in the same general threat intelligence premise as the organization above but primarily based on community sharing approach and using tools, technologies, and techniques that are freely available. Often, concepts that are part of artificial intelligence, data mining, and text mining are thought to be highly complex and difficult. Don't mistake me, these concepts are indeed difficult, but there are tools out there that would facilitate the use of these techniques without having to learn all the concepts and math behind these topics. And as sir Isaac Newton once said, "If I have seen further it is by standing on the shoulders of giants". The combination of all the techniques presented in this site is what we call "Guerrilla Data Mining". It's supposed to be fast, easy, and accessible to anyone. The techniques provides more emphasis on practicality than theory. For example, these tools and techniques presented can be used to visualize trends (e.g. security trends over time), summarize large and diverse data sets (forums, blogs, irc), find commonalities (e.g. profiles of computer criminals) gather a high level understanding of a topic (e.g. the US economy, military activities), and automatically categorize different topics to assist research (e.g. malware taxonomy). Aside from the framework and techniques themselves, the Veritas Project hopes to present a number of current ongoing studies that uses "guerilla data mining". Ultimately, our goal is to provide as much information in how each study was done so other people can generate their own studies and share them through the project. The following studies are currently available and will be presented: For more information visit: http://bit.ly/defcon17_information To download the video visit: http://bit.ly/defcon17_videos
Views: 3901 Christiaan008
Advanced Data Mining with Weka (1.4: Looking at forecasts)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 4: Looking at forecasts http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/JyCK84 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 4200 WekaMOOC
Scales of Measurement - Nominal, Ordinal, Interval, Ratio (Part 1) - Introductory Statistics
 
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This video reviews the scales of measurement covered in introductory statistics: nominal, ordinal, interval, and ratio (Part 1 of 2). Scales of Measurement Nominal, Ordinal, Interval, Ratio YouTube Channel: https://www.youtube.com/user/statisticsinstructor Subscribe today! Lifetime access to SPSS videos: http://tinyurl.com/m2532td Video Transcript: In this video we'll take a look at what are known as the scales of measurement. OK first of all measurement can be defined as the process of applying numbers to objects according to a set of rules. So when we measure something we apply numbers or we give numbers to something and this something is just generically an object or objects so we're assigning numbers to some thing or things and when we do that we follow some sort of rules. Now in terms of introductory statistics textbooks there are four scales of measurement nominal, ordinal, interval, and ratio. We'll take a look at each of these in turn and take a look at some examples as well, as the examples really help to differentiate between these four scales. First we'll take a look at nominal. Now in a nominal scale of measurement we assign numbers to objects where the different numbers indicate different objects. The numbers have no real meaning other than differentiating between objects. So as an example a very common variable in statistical analyses is gender where in this example all males get a 1 and all females get a 2. Now the reason why this is nominal is because we could have just as easily assigned females a 1 and males a 2 or we could have assigned females 500 and males 650. It doesn't matter what number we come up with as long as all males get the same number, 1 in this example, and all females get the same number, 2. It doesn't mean that because females have a higher number that they're better than males or males are worse than females or vice versa or anything like that. All it does is it differentiates between our two groups. And that's a classic nominal example. Another one is baseball uniform numbers. Now the number that a player has on their uniform in baseball it provides no insight into the player's position or anything like that it just simply differentiates between players. So if someone has the number 23 on their back and someone has the number 25 it doesn't mean that the person who has 25 is better, has a higher average, hits more home runs, or anything like that it just means they're not the same playeras number 23. So in this example its nominal once again because the number just simply differentiates between objects. Now just as a side note in all sports it's not the same like in football for example different sequences of numbers typically go towards different positions. Like linebackers will have numbers that are different than quarterbacks and so forth but that's not the case in baseball. So in baseball whatever the number is it provides typically no insight into what position he plays. OK next we have ordinal and for ordinal we assign numbers to objects just like nominal but here the numbers also have meaningful order. So for example the place someone finishes in a race first, second, third, and so on. If we know the place that they finished we know how they did relative to others. So for example the first place person did better than second, second did better than third, and so on of course right that's obvious but that number that they're assigned one, two, or three indicates how they finished in a race so it indicates order and same thing with the place finished in an election first, second, third, fourth we know exactly how they did in relation to the others the person who finished in third place did better than someone who finished in fifth let's say if there are that many people, first did better than third and so on. So the number for ordinal once again indicates placement or order so we can rank people with ordinal data. OK next we have interval. In interval numbers have order just like ordinal so you can see here how these scales of measurement build on one another but in addition to ordinal, interval also has equal intervals between adjacent categories and I'll show you what I mean here with an example. So if we take temperature in degrees Fahrenheit the difference between 78 degrees and 79 degrees or that one degree difference is the same as the difference between 45 degrees and 46 degrees. One degree difference once again. So anywhere along that scale up and down the Fahrenheit scale that one degree difference means the same thing all up and down that scale. OK so if we take eight degrees versus nine degrees the difference there is one degree once again. That's a classic interval scale right there with those differences are meaningful and we'll contrast this with ordinal in just a few moments but finally before we do let's take a look at ratio.
Views: 257674 Quantitative Specialists
M.Tech CS VTU Solution of Data Mining Concepts and Techniques
 
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Find all the resources related to M.Tech CS VTU at http://student-friendly.blogspot.in link to download http://downloads.ziddu.com/download/23673008/solution-manual-kamber.pdf.html
Views: 266 rajshekar targar
Introduction to Data Science with R - Data Analysis Part 1
 
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Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 805753 David Langer
Data Mining with Weka (1.1: Introduction)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 1: Introduction 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: 115078 WekaMOOC
Advanced Data Mining with Weka (3.5: Using R to preprocess data)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 5: Using R to preprocess data http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/8yXNiM https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 1700 WekaMOOC
Excel Data Analysis: Sort, Filter, PivotTable, Formulas (25 Examples): HCC Professional Day 2012
 
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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: 1448959 ExcelIsFun
data mining tutorial for beginners
 
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data mining tutorial for beginners Join Free course here: https://peakget.com/offer/list-building-course/ Affiliate Marketing Google Strategy: https://www.udemy.com/affiliate-marketing-with-google-editor/?couponCode=YOUTUBE Welcome to my YouTube channel! My name is Juri and I am the owner of http://www.tips-digital.com I am professional in social media and in audience building, also I am doing affiliate marketing and SEO. You can hire me if you want to build a website and grow your sales or maybe you need professional advice or consultation?! It does not matter what business you have: online shop or small cafe... What you need to understand is: if you want to increase your sales - you need to be social and use right strategies! I will teach you step by step how to attract visitors from social media. If you do not have any business you can still receive profit by promoting affiliate links. Please check my blog, subscribe to my YouTube channel. For any business inquires you can contact me. data mining tutorial video data mining tutorial ppt data mining tutorial pdf download data mining tools data mining youtube data mining techniques data mining tutorial for beginners in hindi data mining edureka
Views: 2287 Juri Fab
Advanced Data Mining with Weka (4.6: Application: Image classification)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 6: Application: Image classification http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/msswhT https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 5977 WekaMOOC
Best books on Data Mining
 
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Best books on Data Mining
Views: 240 Books Magazines
More Data Mining with Weka (4.6: Cost-sensitive classification vs. cost-sensitive learning)
 
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More Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 6: Cost-sensitive classification vs. cost-sensitive learning http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/I4rRDE https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 7619 WekaMOOC
pdftotxt - converting pdf files in a folder to txt files using R
 
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How to convert pdf files in a folder to txt files using R
Views: 8894 YiRu Li
Data Mining with Weka (2.4: Baseline accuracy)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 4: Baseline accuracy http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 32526 WekaMOOC
Text Mining for Beginners
 
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This is a brief introduction to text mining for beginners. Find out how text mining works and the difference between text mining and key word search, from the leader in natural language based text mining solutions. Learn more about NLP text mining in 90 seconds: https://www.youtube.com/watch?v=GdZWqYGrXww Learn more about NLP text mining for clinical risk monitoring https://www.youtube.com/watch?v=SCDaE4VRzIM
Views: 71765 Linguamatics
More Data Mining with Weka (3.1: Decision trees and rules)
 
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More Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 1: Decision trees and rules http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/nK6fTv https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 9374 WekaMOOC
Data Mining with Weka (4.5: Support vector machines)
 
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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: 41721 WekaMOOC
Data Mining using R | R Tutorial for Beginners | Data Mining Tutorial for Beginners 2018 | ExcleR
 
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Data Mining Using R (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. Data Mining Certification Training Course Content : https://www.excelr.com/data-mining/ Introduction to Data Mining Tutorials : https://youtu.be/uNrg8ep_sEI What is Data Mining? Big data!!! Are you demotivated when your peers are discussing about data science and recent advances in big data. Did you ever think how Flip kart and Amazon are suggesting products for their customers? Do you know how financial institutions/retailers are using big data to transform themselves in to next generation enterprises? Do you want to be part of the world class next generation organisations to change the game rules of the strategy making and to zoom your career to newer heights? Here is the power of data science in the form of Data mining concepts which are considered most powerful techniques in big data analytics. Data Mining with R unveils underlying amazing patterns, wonderful insights which go unnoticed otherwise, from the large amounts of data. Data mining tools predict behaviours and future trends, allowing businesses to make proactive, unbiased and scientific-driven decisions. Data mining has powerful tools and techniques that answer business questions in a scientific manner, which traditional methods cannot answer. Adoption of data mining concepts in decision making changed the companies, the way they operate the business and improved revenues significantly. Companies in a wide range of industries such as Information Technology, Retail, Telecommunication, Oil and Gas, Finance, Health care are already using data mining tools and techniques to take advantage of historical data and to create their future business strategies. Data mining can be broadly categorized into two branches i.e. supervised learning and unsupervised learning. Unsupervised learning deals with identifying significant facts, relationships, hidden patterns, trends and anomalies. Clustering, Principle Component Analysis, Association Rules, etc., are considered unsupervised learning. Supervised learning deals with prediction and classification of the data with machine learning algorithms. Weka is most popular tool for supervised learning. Topics You Will Learn… Unsupervised learning: Introduction to datamining Dimension reduction techniques Principal Component Analysis (PCA) Singular Value Decomposition (SVD) Association rules / Market Basket Analysis / Affinity Filtering Recommender Systems / Recommendation Engine / Collaborative Filtering Network Analytics – Degree centrality, Closeness Centrality, Betweenness Centrality, etc. Cluster Analysis Hierarchical clustering K-means clustering Supervised learning: Overview of machine learning / supervised learning Data exploration methods Basic classification algorithms Decision trees classifier Random Forest K-Nearest Neighbours Bayesian classifiers: Naïve Bayes and other discriminant classifiers Perceptron and Logistic regression Neural networks Advanced classification algorithms Bayesian Networks Support Vector machines Model validation and interpretation Multi class classification problem Bagging (Random Forest) and Boosting (Gradient Boosted Decision Trees) Regression analysis Tools You Will Learn… R: R is a programming language to carry out complex statistical computations and data visualization. R is also open source software and backed by large community all over the world who are contributing to enhancing the capability. R has many advantages over other tools available in the market and it has been rated No.1 among the data scientist community. Mode of Trainings : E-Learning Online Training ClassRoom Training --------------------------------------------------------------------------- For More Info Contact :: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com
Data Mining with Weka (4.4: Logistic regression)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 4: Logistic regression 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: 29712 WekaMOOC
Data Mining with Weka (3.4: Decision trees)
 
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Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 4: Decision trees http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/1LRgAI https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 63467 WekaMOOC
Data Mining with Weka (4.6: Ensemble learning)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 6: Ensemble learning 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: 20320 WekaMOOC
Packet Hacking Village 2017 - YALDA - LARGE SCALE DATA MINING FOR THREAT INTELLIGENCE - GITA ZIABARI
 
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YALDA - LARGE SCALE DATA MINING FOR THREAT INTELLIGENCE GITA ZIABARI, SENIOR THREAT RESEARCH ENGINEER AT FIDELIS CYBERSECURITY PRESENTATION SLIDES: PHV2017-GZIABARI.PDF SLIDES AVAILABLE ON: https://www.wallofsheep.com Every SOC is deluged by massive amounts of logs, suspect files, alerts and data that make it impossible to respond to everything. It is essential to find the signal in the noise to be able to best protect an organization. This talk will cover techniques to automate the processing of data mining malware to derive key indicators to find active threats against an enterprise. Techniques will be discussed covering how to tune the automation to avoid false positives and the many struggles we have had in creating appropriate whitelists. We'll also discuss techniques for organizations to find and process intelligence for attacks targeting them specifically that no vendor can sell or provide them. Audiences would also learn about method of automatically identifying malicious data submitted to a malware analysis sandbox. Gita Ziabari (Twitter: @gitaziabari) is working at Fidelis Cybersecurity as a Senior Threat Research Engineer. She has more than 13 years of experience in threat research, networking, testing and building automated frameworks. Her expertise is writing automated tools for data mining. She has unique approaches and techniques in automation. Brought to you by Aries Security - https://www.ariessecurity.com
Views: 110 Wall of Sheep
Bitcoin Mining Complete Guide & Tutorial (EASIEST METHOD Working 2018)
 
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Video tutorial/guide showing how to start mining Bitcoins from home super simple and easily, for beginners, or advanced users, using NiceHash. Bitcoins are a cryptocurrency, like Litecoins and Ethereum which offer an anonymous form of digital currency. This video will show you how to setup a bitcoin wallet, download and install the mining program (nicehash), link it up to your wallet using the bitcoin address, and use the optimal algorithm for mining the most bitcoins and earning the money. Mining these Bitcoins is a way to introduce new bitcoins into the world, as well as verify transactions occurring in the blockchain. You can earn bitcoins and money, by mining at home from your very own PC computer. I also include some tips which helped me mine! Techlore Website: http://www.techlore.tech My Video Equipment (Affiliate Link): https://www.amazon.com/shop/influencer20170928875 Instagram: @techlemur Discord: https://discord.gg/sdMv9Zj Minds: https://www.minds.com/Techlore DTube: https://d.tube/#!/c/techlore *ATTENTION* Nicehash has updated their program and the UI is very different from this video. I made an updated video so you can all follow along. Here is the link. Use the new program with Nvidia Cards: UPDATED VIDEO: https://www.youtube.com/watch?v=XnAjCMb_uEg Coinbase Link: https://www.coinbase.com/join/5942e0b5d26ede03db311893 NiceHash Link: https://www.nicehash.com/?p=nhmintro You should mine with a powerful Graphics Card (GPU), and only on desktop computers (Not laptops). You need a bitcoin wallet like Coinbase, and you will need NiceHash. Make sure to benchmark your algorithms to get the most amount of bitcoins. Bitcoin mining is the process of adding transaction records to Bitcoin's public ledger of past transactions or blockchain. This ledger of past transactions is called the block chain as it is a chain of blocks. The block chain serves to confirm transactions to the rest of the network as having taken place. Bitcoin nodes use the block chain to distinguish legitimate Bitcoin transactions from attempts to re-spend coins that have already been spent elsewhere.
Views: 265511 Techlore
Web Scraping, Screen Scraping, Web Data Mining, Data Extractor
 
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Input FORMAT SUPPORT [website, webpage, Text, pdf, CSV, database] Output FORMAT SUPPORT [ excel, csv, tsv, pdf, xml, html, sql, MySql ] » Hotel website [ hotel name, address, images, reviews, latitude-longitude, price ] Scraping » Hotel price scraping for marketing intelligence [againast to your competitor] » Real Estate Data Extraction » Extract Store Details » University's Web Data Scraping » Extract Product Description » Web Information Extractor » Craigslist Email Extractor » Metadata Extraction » Website Email Extraction » Scraping Business Directory » Yellow Pages Scraping » PriceGrabber Data Extraction » Scraping Property Information » Amazon Product Extraction » Download Product Images » Automate osCommerce Product Upload » Scraping Business Contact » Craigslist Posting Service » Imdb Data Extraction » Meta Data Extraction » Scraping From Dynamic Pages » Extract Lyrics Data » Email Scraping & Extraction » Scraping Customer List » Scraping Data From WebSite ----------------------------------- Expertise In -------------------------- » Hotel Website Scraping [expedia.com, hotels.com, booking.com, orbitz.com, airasia.com, easybook.com, laterooms.com, travelocity.com, thomascook.com, activehotels.com, priceline.com, lastminute.com, yatra.com, makemytrip.com etc.] » Ads Classifieds Scraping [gumtree.com, olx.com, craigslist.com etc] » Real Estate Scraping [99acres.com, www.zillow.com, www.trulia.com, www.realtor.com, www.agentimage.com, www.realtysoft.com, www.realestate.com.au etc.] » Product catalog Scraping [amazon.com , ebay.com, yellowpage, whitepage etc.] Contact if any service require [ [email protected]m ]
Views: 61736 vickyrathee2005
Introduction to Text Analysis with NVivo 11 for Windows
 
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It’s easy to get lost in a lot of text-based data. NVivo is qualitative data analysis software that provides structure to text, helping you quickly unlock insights and make something beautiful to share. http://www.qsrinternational.com
Views: 104391 NVivo by QSR
text mining research papers pdf
 
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More info: https://goo.gl/TIo1T2?49476
Advanced Data Mining with Weka (2.3: The MOA interface)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 3: The MOA interface http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3167 WekaMOOC
Katharina Rasch - What every data scientist should know about data anonymization
 
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PyData Berlin 2016 There are numerous examples of data anonymization gone horribly wrong - the most prominent one might be the netflix prize, where researchers were able to uniquely identify users by combining netflix user data with imdb reviews. Let's learn from their mistakes and look at some of the measures you can take to better anonymize data before you share it with others. Outline: - Look at some of the examples where data anonymization was broken and identify what went wrong - What is the state of the art for data anonymization and can you be sure to be safe if you follow it? - How does anonymization affect the possibilities for data mining/machine learning on the data? This talk is aimed at people who want release open data or want to share sensitive data between departments. Slides: https://github.com/krasch/presentations/blob/master/pydata_Berlin_2016.pdf
Views: 2067 PyData
✊ Support and Resistance: support resistance trading, technical analysis tools, best online trading
 
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"Binary options are not promoted or sold to retail EEA traders. If you are not a professional client, please leave this page." ✅✅✅ Recommended Broker ►https://goo.gl/7BZ7Rh [GET $10,000 FREE] "RISK WARNING: YOUR CAPITAL MIGHT BE AT RISK" This video is not an investment advice. "CFDs are complex instruments and come with a high risk of losing money rapidly due to leverage. Between 74-89% of retail investor accounts lose money when trading CFDs. You should consider whether you understand how CFDs work and whether you can afford to take the high risk of losing your money." Binary Options Turbo Trader (#BOTT) https://www.youtube.com/channel/UCCCvcQe-BFeBhm5Nlgp0p-Q?sub_confirmation=1 Forex (FX) Turbo Trader (FOTT) https://www.youtube.com/channel/UCmk8OVaeu2G0FS0hhjhzTeQ?sub_confirmation=1 DO (Digital Options) Turbo Trader (DOTT) https://www.youtube.com/channel/UCI0KK-afoTHqjEj5f62F1vQ?sub_confirmation=1 BO Turbo Trader Price Action Guide for Binary Options Trader PDF https://goo.gl/VmcKjJ 👉 SMASH THE LIKE BUTTON 👈 👉 HIT THE SUBSCRIBE BUTTON 👈 👉 LEAVE A COMMENT 👈 👉 SHARE 👈 ★ CONTACT ME https://goo.gl/uvt3xJ ★ Facebook-Group: https://www.facebook.com/groups/319493918456624/ Twitter: https://twitter.com/boturbotrader Binary Option Win Rate and Net Profit Calculator + Simulator https://goo.gl/NeUyCp Money Management Masaniello Program + Excel File https://goo.gl/9pNRhs Start Mining Cryptocurrency http://goo.gl/1mJLbU Risk Warning: Your invested capital may be at risk. This video is not an investment advice. Indicators: EMA 3 (blue) EMA 20 (yellow) EMA 50 (orange) EMA 100 (red) EMA 200 (purple) Bollinger Band Period 20 Deviation 2 (green) Bollinger Band Period 20 Deviation 1 (white) Volume is a simple yet powerful way for traders and investors to increase profits and minimize risks. This technical pattern may signal a bearish turn in Amazon’s stock price. Technical indicators determine the direction of an asset's momentum and whether that direction will continue. Here are seven used most. These include 200-day moving average, relative strength index, moving average convergence divergence, or MACD, Fibonacci retracement and candle stick price chart. The terms may sound daunting, but software available nowadays makes technical analysis easy. One of the widely used tools is the 200-day moving average. There are several categories of technical analysis - Price indicators, Support and Resistance levels, Momentum indicators, Volume indicators, Oscillators and Statistical price movement indicators. Hope you enjoy this comprehensive suite of 10 very important Technical analysis tools. In finance, technical analysis is an analysis methodology for forecasting the direction of prices through the study of past market data, primarily price and volume. The essence of such a technical analysis software is to study charts of financial instruments using technical indicators and analytical tools. Technical analysis is the interpretation of the price action of a company's underlying stock (or any tradable financial instrument). It utilizes various charts and statistical indicators to determine price support/resistance, range and trends. Trading. The core purpose of technical analysis is to carry out stock price forecast by looking at past data. While fundamental analysis is one of the most effective methods of determining the long-term movements of a stock, technical analysis provides a similar tool for short-term traders. Technical analysis employs models and trading rules based on price and volume transformations, such as the relative strength index, moving averages, regressions, inter-market and intra-market price correlations, business cycles, stock market cycles or, classically, through recognition of chart patterns. What is 'Technical Analysis of Stocks and Trends' Technical analysis of stocks and trends is the study of historical market data, including price and volume. Using both behavioral economics and quantitative analysis, technical analysts aim to use past performance to predict future market behavior. Using Technical Analysis Indicators. Technical analysis is a method of examining past market data to help forecast future price movements. Technical analysis is based around a market's price history, rather than the fundamental data like earnings, dividends, news, and events. swing trading tips, best technical analysis technical analysis tools, best online trading technical trading, online trading courses swing trading tips, best technical analysis online trading academy, online trading account technical trading, trading courses, internet trading momentum trading strategies, OHLC chart online trading academy, online trading account trading on line, trading account, online trading technical analysis tools and techniques free online binary option trading course binary options video course, active trader #supportandresistance #snr
Views: 208 BO Turbo Trader
Excel - Weka İlişkisi
 
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Excel, Excel - Weka, Excel Weka İlişkisi, Excel Weka Eğitim Seti, Excel Weka Eğitim Serisi TAGS veri madenciliği yöntemleri, veri madenciliği nedir, microsoft excell, makro nedir, makrolarla excell dersleri, mining data, mining, datamining, what is data, data mining pdf, data mining techniques, data analysis, mineria, mineria de datos, data warehouse, data warehousing, database, data mining algorithm, data mining ppt, database mining, data mining software, big data, clustering, data mining tools, google data mining, classification, big data mining, smite, datamining smite, smite data mining, coursera, smite reddit, smite patch notes, smite wiki,0 gw2 data mining, big data analytics, bigdata, big data mining, big data, slideshare, kaggle, data scientist, hadoop,0 data mining meaning, jurnal data mining, python data mining, data mining adalah, nptel, python, data mining pdf TAGS http://kodkolik.net/ Machine Learning Group at the University of Waikato Project Software Book Publications People Related Weka 3: Data Mining Software in Java Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this. Weka is open source software issued under the GNU General Public License. We have put together several free online courses that teach machine learning and data mining using Weka. Check out the website for the courses for details on when and how to enrol. The videos for the courses are available on Youtube. Yes, it is possible to apply Weka to big data!
Spatial Data Mining II: A Deep Dive into Space-Time Analysis
 
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Space and time are inseparable, and integrating the temporal aspect of your data into your spatial analysis leads to powerful discoveries. This workshop will build on the cluster analysis methods discussed in Spatial Data Mining I by presenting advanced techniques for analyzing your data in the context of both space and time. We will cover space-time pattern mining techniques including aggregating your temporal data into a space-time cube, emerging hot spot analysis, local outlier analysis, best practices for visualizing your space-time cube, and strategies for interpreting and sharing your results. Come learn how to use these new techniques to get the most out of your spatiotemporal data.
Views: 5781 Esri Events