MATLAB vs. Posit vs. Wolfram Mathematica

MATLAB really does best for solving computational problems in math and engineering. Especially when you have to use a lot of functions in your solving process, or if you have a nonlinear equation that must be iteratively solved. [MATLAB] can also perform things like integration and derivation on your equations that you put into it.

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In my humble opinion, if you are working on something related to Statistics, RStudio is your go-to tool. But if you are looking for something in Machine Learning, look out for Python. The beauty is that there are packages now by which you can write Python/SQL in R. Cross-platform functionality like such makes RStudio way ahead of its competition. A couple of chinks in RStudio armor are very small and can be considered as nagging just for the sake of argument. Other than completely based on programming language, I couldn't find significant drawbacks to using RStudio. It is one of the best free software available in the market at present.

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We are the judgement that Wolfram Mathematica is despite many critics based on the paradigms selected a mark in the fields of the markets for computations of all kind. Wolfram Mathematica is even a choice in fields where other bolide systems reign most of the market. Wolfram Mathematica offers rich flexibility and internally standardizes the right methodologies for his user community. Wolfram Mathematica is not cheap and in need of a hard an long learner journey. That makes it weak in comparison with of-the-shelf-solution packages or even other programming languages. But for systematization of methods Wolfram Mathematica is far in front of almost all the other. Scientist and interested people are able to develop themself further and Wolfram Matheamatica users are a human variant for themself. The reach out for modern mathematics based science is deep and a unique unified framework makes the whole field of mathematics accessable comparable to the brain of Albert Einstein. The paradigms incorporated are the most efficients and consist in assembly on the market. The mathematics is covering and fullfills not just education requirements but the demands and needs of experts.

Mathematica is incompatible with other systems for mCAx and therefore the borders between the systems are hard to overcome. Wolfram Mathematica should be consider one of the more open systems because other code can be imported and run but on the export side it is rathe incompatible by design purposes. A better standard for all that might solve the crisis but there is none in sight. Selection of knowledge of what works will be in the future even more focussed and general system might be one the lossy side. Knowledge of esthetics of what will be in the highest demand in necessary and Wolfram is not a leader in this field of science. Mathematics leves from gathering problems from application fields and less from the glory of itself and the formalization of this.

• It has a very user friendly library which helps users learn this software fairly quickly in a short span of time.
• The graphical user interface provided by the software is really good.
• The code that a person writes allows options for debugging.
• One can visualize the flow of control of their code inside MATLAB.

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• The support is incredibly professional and helpful, and they often go out of their way to help me when something doesn't work.
• The one-click publishing from RStudio Connect is absolutely amazing, and I really like the way that it deploys your exact package versions, because otherwise, you can get in a terrible mess.
• Python doesn't feel quite as native as R at the moment but I have definitely deployed stuff in R and Python that works beautifully which is really nice indeed.

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• It allows straightforward integration of analytic analysis of algebraic expressions and their numerical implemented.
• Supports varying programmatic paradigms, so one can choose what best fits the problem or task: pure functions, procedural programming, list processing, and even (with a bit of setup) object-oriented programming.
• The extensive and rich tools for graphical rendering make it very easy to not just get 2D and 3D renderings of final output, but also to do quick-and-dirty 2D and 3D rendering of intermediate results and/or debugging results.

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• MatLab is pricier than most of its competitors and because of this reason, many organizations are moving towards cheaper alternatives - mostly Python.
• MatLab is inefficient when it comes to performing a large number of iterations. It gets laggy and often crashes. Python is better in this regard.
• There is a limited number of hardware options (mostly NI) that can be connected directly to the data acquisition toolbox.

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• Python integration is newer and still can be rough, especially with when using virtual environments.
• RStudio Connect pricing feels very department focused, not quite an enterprise perspective.
• Some of the RStudio packages don't follow conventional development guidelines (API breaking changes with minor version numbers) which can make supporting larger projects over longer timeframes difficult.

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• Should include more libraries and functions.
• Should include more functions that can be used in Machine Learning.
• Should include more functions that can be used in Data Science.

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There is no viable alternative right now. The toolset is good and the functionality is increasing with every release. It is backed by regular releases and ongoing development by the RStudio team. There is good engagement with RStudio directly when support is required. Also there's a strong and growing community of developers who provide additional support and sample code.

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MATLAB is pretty easy to use. You can extend its capabilities using the programming interface. Very flexible capabilities when it comes to graphical presentation of your data (so many different kinds of options for your plotting needs). Anytime you are working with large data sets, or with matrices, MATLAB is likely to be very helpful.

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For someone who learns how to use the software and picks up on the "language" of R, it's very easy to use. For beginners, it can be hard and might require a course, as well as the appropriate statistical training to understand what packages to use and when

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RStudio is very available and cheap to use. It needs to be updated every once in a while, but the updates tend to be quick and they do not hinder my ability to make progress. I have not experienced any RStudio outages, and I have used the application quite a bit for a variety of statistical analyses

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The built-in search engine is not as performing as I wish it would be. However, the YouTube channel has a vast library of informative video that can help understanding the software. Also, many other software have a nice bridge into MATLAB, which makes it very versatile. Overall, the support for MATLAB is good.

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Since R is trendy among statisticians, you can find lots of help from the data science/ stats communities. If you need help with anything related to RStudio or R, google it or search on StackOverflow, you might easily find the solution that you are looking for.

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Wolfram Mathematica is a nice software package. It has very nice features and easy to install and use in your machine. Besides this, there is a nice support from Wolfram. They come to the university frequently to give seminars in Mathematica. I think this is the best thing they are doing. That is very helpful for graduate and undergraduate students who are using Mathematica in their research.

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We did it at the individual level: anyone willing to code in R can use it. No real deployment involved.

How MATLAB compares to its competition or similar open access tools like R (programming language) or SciLab is that it's simply more powerful and capable. It embraces a wider spectrum of possibilities for far more fields than any other environment. R, for example, is intended primarily for the area of statistical computing. SciLab, on the other hand, is a similar open access tool that falls very short in its computing capabilities. It's much slower when running larger scripts and isn't documented or supported nearly as well as MATLAB.

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RStudio was provided as the most customizable. It was also strictly the most feature-rich as far as enabling our organization to script, run, and make use of R open-source packages in our data analysis workstreams. It also provided some support for python, which was useful when we had R heavy code with some python threaded in. Overall we picked Rstudio for the features it provided for our data analysis needs and the ability to interface with our existing resources.

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We have evaluated and are using in some cases the Python language in concert with the Jupyter notebook interface. For UI, we using libraries like React to create visually stunning visualizations of such models. Mathematica compares favorably to this alternative in terms of speed of development. Mathematica compares unfavorably to this alternative in terms of license costs.

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RStudio is very scalable as a product. The issue I have is that it doesn't necessarily fit in nicely with the mainly Microsoft environment that everybody else is using. Having RStudio for us means dedicated servers and recruiting staff who know how to manage the environment. This isn't a fault of the product at all, it's just part of the data science landscape that we all have to put up with. Having said that RStudio is absolutely great for running on low spec servers and there are loads of options to handle concurrency, memory use, etc.

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• MATLAB helps us quickly sort through large sets of data because we keep the same script each time we run an analyzation, making it very efficient to run this whole process.
• The software makes it super easy for us to create plots that we can then show to investors or clients to display our data.
• We are also looking to create an app for our product, and we will not be able to do that on MATLAB, therefore creating a limiting issue and a new learning curve for a programming language.

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• Using it for data science in a very big and old company, the most positive impact, from my point of view, has been the ability of spreading data culture across the group. Shortening the path from data to value.
• Still it's hard to quantify economic benefits, we are struggling and it's a great point of attention, since splitting out the contribution of the single aspects of a project (and getting the RStudio pie) is complicated.
• What is sure is that, in the long run, RStudio is boosting productivity and making the process in which is embedded more efficient (cost reduction).

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• Easy to solve huge mathematical equations, so it saved time there
• Doing analysis and plotting graphs is also another plus point
• Learning is very slow, and it took lot of time to learn its scripting language

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