Professional

  • Big data analysis is explained in this online course that introduces the user to the tools Hadoop and Mapreduce. These tools allow for the parallel computing necessary to analyze large amounts of data.
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  • This tutorial on SQL teaches the most used commands. There is a short explanation, then the user is asked a simple question. If the typed answer is correct, the user continues to the next lesson.
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  • This site did a lot of data visualization on many hot button topics. They provide the raw data that they used to create their graphs at this page. These data sets are kept in Google Doc spreadsheets.
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  • The Census Bureau has made many data visualizations of the data it collects. It is a good collections of maps, treemaps, an age/sex pyramid, and of course more familiar graphs, like bar graphs.
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  • Rseek.org is a search engine for R resources. Type any topic in the search box, and get resources that are R specific. You can further narrow your search to just articles, books, packages, support, or "for beginners."
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  • This online software allows you to load data and make professional-looking graphs with it. Graph types are basic (scatterplot, line plot, bar charts, etc.), statistical (histograms, box plots), scientific (error bars, heat map, contour), 3D charts, and financial (e.g. time series). Other graphs are available with the paid pro version. Log in is required, which allows you to upload data and save it for next use.
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  • This online application allows the user to import data from online resources such as Facebook, Google Analytics, GitHub, as well as spreadsheets on their own computers. They can then drag-and-drop variables to make graphs automatically. The basic version is free, but you can upgrade to a paid version which allows combining data across services and, if the data come from an online resource, the user has the choice to have Data Hub keep the graphs updated as the data changes.
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  • This software makes it easier to use the R language. It includes a code debugger, editing, and visualization tools.
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  • This is a web application framework for R, in which you can write and publish web apps without knowing HTML, Java, etc. You create two .R files: one that controls the user interface, and one that controls what the app does. The site contains examples of Shiny apps, a tutorial on how to get started, and information on how to have your apps hosted, if you don't have a server.
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  • This online booklet comes out of the Mosaic project. It is a guide aimed at students in an introductory statistics class. After a chapter on getting started, the chapters are grouped around what kind of variable is being analyzed. One quantitative variable; one categorical variable; two quantitative variables; two categorical variables; quantitative response, categorical predictor; categorical response, quantitative predictor; and survival time outcomes.
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