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ZK Live Demo - Grid. Interactive Graphics for Data Analysis ––––––––– Principles and Examples. Data Mining Course. Here are the teaching modules for a one-semester introductory course on Data Mining, suitable for advanced undergraduates or first-year graduate students. The teaching modules were created by : Introductions | Course materials | Data Mining Course Modules | Assignments & Datasets | Extra Publications | Additional Lectures | Acknowledgments Introductions Course introduction | For prospective students | For faculty Course materials Syllabus Detailed Course Outline Please be patient and wait for the entire file to load ! References Data Mining Course Modules To get the presentations, add in front of ppt files below DM1: Introduction: Machine Learning and Data Mining, updated May 31, 2006.

DM2: Machine Learning and Classification, updated June 7, 2006. DM3: Input: Concepts, Instances, Attributes. DM4: Output: Knowledge Representation, updated June 7, 2006. DM5: Classification - Basic Methods. Wallpapering Fog: Losing touch... or why Excel and VBA won't cut it any more. Thinking through this post is making me feel old.

Wallpapering Fog: Losing touch... or why Excel and VBA won't cut it any more

There's going to be a lot of 'in my day' type reminiscing and I'm only 34. It's all this new fangled technology that's doing it. The world's changing fast. I hate people who say that the world's changing fast, but this time it's true. I got my first proper job twelve years ago this month, as a junior analyst with a small econometrics consultancy and although the statistical techniques I use are roughly the same as back then, I've started to realise that our software tools are going through a revolution.

Fairly quickly after starting that first job, I discovered that data processing in Excel was a hell of a lot faster and easier if you learned Visual Basic for Applications (VBA), so I did. Up until fairly recently, if an aspiring analyst asked what they should do to get ahead at work, I'd say get good in Excel. The trouble is, VBA's getting left behind. Fast Analytics and Rapid-fire Business Intelligence from Tableau Software.

The R Project for Statistical Computing. How to read an excel file (dot xls and dot xlsx) into a data frame with r. RStudio. R twotorials: how to build a for loop in r. Beginner to advanced resources for the R programming language.

R Programming - Wikibooks, collection of open-content textbooks. Welcome to the 'R' programming Wikibook This book is designed to be a practical guide to the R programming language[1].

R Programming - Wikibooks, collection of open-content textbooks

R is free software designed for statistical computing. There is already great documentation for the standard R packages on the Comprehensive R Archive Network (CRAN)[2] and many resources in specialized books, forums such as Stackoverflow[3] and personal blogs[4], but all of these resources are scattered and therefore difficult to find and to compare. The aim of this Wikibook is to be the place where anyone can share his or her knowledge and tricks on R. It is supposed to be organized by task but not by discipline[5]. How can you share your R experience ? Explain the syntax of a commandCompare the different ways of performing each task using R.Try to make unique examples based on fake data (ie simulated data sets).As with any Wikibook please feel free to make corrections, expand explanations, and make additions where necessary. An online tool to convert UK post codes into longitude and latitude mapping coordinates.

Author: mike foskett incept: 8th November 2008 last modified: 28th October 2011 Postcode validation script updated 28th October 2011 An online tool to convert UK postcodes into longitude and latitude mapping coordinates.

An online tool to convert UK post codes into longitude and latitude mapping coordinates

The form utilises Microsoft's Virtual Earth to get the conversion. <p><strong>Sorry, JavaScript is required to run this conversion. GPS Visualizer map input form: Plot quantitative data. This is a special version of the GPS Visualizer map form that's designed for plotting quantifiable data on a map.

GPS Visualizer map input form: Plot quantitative data

You can colorize and/or resize the points according to a generic frequency field named "N", or you can use a more typical field, such as altitude, population, or category. If you have track data, or if you don't need to automatically colorize/resize your data points by a particular parameter, you'll probably be better served by the normal Google Maps form, the Google Earth KML form, or the JPEG/PNG/SVG form. If you want your map to load markers dynamically (e.g., from a Google Docs spreadsheet), you definitely need to use the standard Google form. The coordinates of your data can be given as latitude/longitude, as geographic places (city-state pairs, states, or countries), U.S. ZIP codes, or Canadian postal codes. Please contact Adam Schneider, using the address on the bottom of this page, if you have any questions about how this form works! Smart Data Collective. The maps library for R is a powerful tool for creating maps of countries and regions of the world.

Smart Data Collective

For example, you can create a map of the USA and its states in just three lines of code: library(maps) map("state", interior = FALSE) map("state", boundary = FALSE, col="gray", add = TRUE) The coordinate system of the graph is latitude and longitude, so it's easy to overlay other spatial data on this map. Unfortunately, the data for the maps library isn't sufficient for some applications. GADM is a spatial database of the location of the world's administrative boundaries, and as Claudia Engel discovered the map information is available as native R objects that can be plotted directly with the spplot function (from the sp package). Think Stats: Probability and Statistics for Programmers. By Allen B.

Think Stats: Probability and Statistics for Programmers

Downey, published by O'Reilly Media. Order Think Stats from Amazon.com Download this book in PDF. Read this book online. Code examples and solutions are available fromthis subversion repository or this zip file. Download data files for use with the book. Read the related blog Probably Overthinking It. Description Think Stats is an introduction to Probability and Statistics for Python programmers. Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. R Programming. R. HTML5 Mobile Mapping. Simple Storage Service (Amazon S3) MongoDB.