Analysis Tools Comparison
Analysis Tools Comparison: R Language, Matlab, esProc, SAS, SPSS, Excel, and SQL
The interactive analysis is a circular analytics procedure comprising assumption, verification, and calibration by the analyst to achieve the fuzzy computation goal. For specifics and details, please refer to another article I composed: Interactive Analysis and Related Tools.
Because there are so many tools for interactive analysis that not everyone can know all of them, I will only focus on and compare the most common 7 tools: R, Matlab, esProc, SAS, SPSS, Excel, and SQL. These tools and languages are quite distinctive. In fact, I think there are also quite a few good tools, such as BMDP, Eview, Stata, S-Plus, Octave, Scilab, Mathematica, and minitab, just to name a few. We will discuss them in other essays.
Since it is the comparison on the interactive analysis tools, the project to compare must be also typical and pertinent correspondingly. I listed 5 metrics, and each of them is rated by 5 ★ at the highest. The more ★, the more advantages this tool has regarding this metric.
For example,
UI friendliness: The more ★, the friendlier interface and the easier operations will be.
Technical competence requirement: The more ★, the lower requirements on the technical background like the mathematic algorithm and programming skills.
Stepwise computation: The more ★, the easier to decompose and solve the complex problem.
Support for structured data: The more ★, the easier to perform the analysis on the structured data.
Fixed algorithm: The more ★, the greater number of fixed algorithms and the stronger functionality are available.
These 5 metrics surely cannot present the ins and outs of interactive analysis tools, not mention all computational tools. In the practical use, there are many metrics deserving considerations, which are too many to be discussed here and we will further explore them later, for example, price, number of enhanced documents, after-sales technical
The interactive analysis is a circular analytics procedure comprising assumption, verification, and calibration by the analyst to achieve the fuzzy computation goal. For specifics and details, please refer to another article I composed: Interactive Analysis and Related Tools.
Because there are so many tools for interactive analysis that not everyone can know all of them, I will only focus on and compare the most common 7 tools: R, Matlab, esProc, SAS, SPSS, Excel, and SQL. These tools and languages are quite distinctive. In fact, I think there are also quite a few good tools, such as BMDP, Eview, Stata, S-Plus, Octave, Scilab, Mathematica, and minitab, just to name a few. We will discuss them in other essays.
Since it is the comparison on the interactive analysis tools, the project to compare must be also typical and pertinent correspondingly. I listed 5 metrics, and each of them is rated by 5 ★ at the highest. The more ★, the more advantages this tool has regarding this metric.
For example,
UI friendliness: The more ★, the friendlier interface and the easier operations will be.
Technical competence requirement: The more ★, the lower requirements on the technical background like the mathematic algorithm and programming skills.
Stepwise computation: The more ★, the easier to decompose and solve the complex problem.
Support for structured data: The more ★, the easier to perform the analysis on the structured data.
Fixed algorithm: The more ★, the greater number of fixed algorithms and the stronger functionality are available.
These 5 metrics surely cannot present the ins and outs of interactive analysis tools, not mention all computational tools. In the practical use, there are many metrics deserving considerations, which are too many to be discussed here and we will further explore them later, for example, price, number of enhanced documents, after-sales technical