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Which is the most useful and easy to learn data analysis tool? I tried PAW, but is there anything better? Thanks in advance. |
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Here are the tools that may help you analyze your data: Machine Learning and Data Mining Tools
Statistical Tools
Tools for exploratory data analysis
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R is great for machine learning as well. http://cran.r-project.org/web/views/MachineLearning.html
(Mar 22 '11 at 12:43)
larrydag 1 ♦
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definitevely R (not the simplest, but on the long term you'll never regret it). I couldn't agree more. Here is a blog post I did for R Beginners. http://industrialengineertools.blogspot.com/2010/10/r-links-for-beginner-on-world.html
(Mar 21 '11 at 12:35)
larrydag 1 ♦
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If "data analysis" = "statistical analysis", I agree that R is a good choice. If "data analysis" = "data mining", I'd probably look at RapidMiner or Weka. You can do a fair bit of data mining in R, but RapidMiner and Weka are easier to learn. |
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I generally assume everybody knows excel (might be a bad assumption). If you don't know excel, start there. Excel has the VB scripting language which is pretty easy to pick up to do more complicated data mashing, and lots of built in functionality to make it easy to deal with dates, sequences, series and formulas. Excel has basic statistical analysis built in, histograms, regression, standard deviation etc. You can do linear programming problems in excel and graphing is really easy. Pivot table functionality in excel is invaluable (seriously, I don't know what I would do without pivot tables). I've settled on a combo of tools for my professional life: SAS (to prepare and clean data), R (to do analysis and graphing) and Excel for smaller problems or problems with lots of little pieces and parts that become overly cumbersome to load into SAS or R data structures. IANAE, but whenever someone mentions Excel I feel compelled to add: Excel is well-known for its computational inaccuracies (of all kinds) since, well, ever. And little has changed since then, e.g. Floating-point arithmetic may give inaccurate results in Excel.
(Feb 24 at 06:23)
fbahr ♦
I've never really looked into that, very interesting. Thanks.
(Mar 02 at 17:36)
austinboston
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