![]() …………One pitfall to watch out for when evaluating Minitab is that there are scammers out there selling counterfeit copies on some popular, above-board online shops. If given the opportunity and the need, I’d incorporate this into my workflows for the kind of hypothesis testing routines I spoke of in Outlier Detection with SQL Server, preliminary testing of statistical code, formula validation and certain data mining problems, when one of Minitab’s specialized algorithms is called for. It doesn’t provide neural nets, sequence clustering or some of my other favorite SSDM algorithms from the A Rickety Stairway to SQL Server Data Mining series, but it does deliver dozens of alternatives for lower-level data mining methods like regression and clustering which SSDM doesn’t provide. This was also the case with WEKA, but Minitab can at least perform calculations on hundreds of thousands of rows rather than a paltry few thousand. One of Minitab’s shortcomings is that it simply doesn’t have the same “Big Data”-level processing capabilities as SQL Server. Minitab has some nice out-of-the-box visualizations which can be done with more pizzazz in Reporting Services, provided one has the need, skills and time to code them. ![]() I’m not a big Excel user, so I can’t speak at length on whether or not it compares favorably, but I personally found Minitab much easier to work with for statistical tasks like these. If I someday had enough clients with needs for activities like Analysis of Variance (ANOVA), experiment design or dozens of specific statistics that aren’t easily calculable in SQL Server, Minitab would be at the top of my shopping list (with the proviso that I’d also evaluate their competitors, which I have yet to do). Like most other analysis tools, Minitab only competes with SQL Server Data Mining (SSDM) tangentially most of its functionality is devoted to statistical analysis, which neither SSDM nor SQL Server Analysis Services (SSAS) directly addresses. Minitab is useful for a much wider range of scenarios than WEKA, but the same principles apply to both: it is best to use SQL Server for any functionality Microsoft has provided out-of-the-box, but to use these third-party tools when their functionality can’t be coded quickly and economically in T-SQL and. I didn’t know what to expect going into the trial, since I had zero experience with it to that point, but I immediately realized how analysts could recoup the costs in a matter of weeks, provided that they encountered some specific use cases often enough. In a recent trial with Minitab 17.1 I encountered many of the same limitations, but at much less serious levels – which really ought to be the case, given that WEKA is a free open source tool and Minitab costs almost $1,500 for a single-user license. …………WEKA occupies a very small place in that toolbox, due to various shortcomings, including an inability to handle datasets that many SQL Server users would consider microscopic. These are intended less as formal reviews than preliminary answers to the question, “How would these fit in a SQL Server data miner’s toolbox?” ![]() This was one the caveats I also observed when appraising WEKA in the first installments of this occasional series, in which I’ll pass on my misadventures with using various third-party data mining tools to the rest of the SQL Server community. …………It may be called Minitab, but SQL Server users can derive maximum benefits from the Windows version of this professional data mining and statistics tool – provided that they use it for tasks that SQL Server doesn’t do natively.
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