It does not have a command line feature but rather has a user interface that is entirely point-and-click and somewhat resembles Microsoft Excel. Although it looks a lot like Excel, it can handle larger data sets faster and with more ease. To make up for how expensive it is, it is incredibly easy to learn.
As a non-technical person I learned how to use it in under an hour by following an online tutorial from the University of Illinois Library.
However, my take on this software is that unless you really need a more powerful tool just stick to Excel. They are too similar to justify seeking out this specialized software. Thanks for reading!
Let us know in the comments if you have any thoughts or questions about any of these data analysis software programs. We love hearing from our readers! As you do research with larger amounts of data, it becomes necessary to graduate from doing your data analysis in Excel and find a more powerful software. It can seem like a really daunting task, especially if you have never attempted to analyze big data before.
There are a number of data analysis software systems out there, but it is not always clear which one will work best for your research. The nature of your research data, your technological expertise, and your own personal preferences are all going to play a role in which software will work best for you. This is a very simple but relevant questions for me to proceed my work. Indeed, I would like to know more deeply about Stata since it is required in the current working environment.
Would you please make any comments on the questions above? Python, R, TensorFlow as data analytics tools. I have also utilized SQL to manipulate commercial databases. Tags: analytics , python , software , stata. Joro Kolev. Different people will give you different answers. Stata is comparable to R, but they are very different. R is based on some extinct now I think statistical language called S, and has very different syntax.
I rarely write programs that other people are supposed to use. For my own research, Stata has the advantage over R that everything in Stata is simple and fast to do, and the syntax of Stata is natural to me, while in R everything is hard and slow to do, and the syntax of R is unnatural to me.
But I am saying these things as somebody who has used Stata constantly from year till now, and as somebody who has 4 months of experience with R. I did my Ph. Professor Satorra was very excited about R and he was talking about R oftentimes in our conversations, and one year I decided to see for myself the greatness of R.
I took Econometrics in R by Grant V. Farnsworth, and over 4 months slowly went through everything described in this guide to R. At the end I think it all comes to be a matter of taste. Comment Post Cancel. Leonardo Guizzetti. For my own view, I find questions of the sort "Is software X better than software Y? I also disagree that it is a simple question. Any such question implicitly assumes thatyou — the reader — understand and have some proficiency in X and Y.
Otherwise, the discussion is moot. As a result, every answer is subjective and couched in the relative abilities of the responder. Whether that comparison means anything to you depends on unknown assumptions about you.
For example, how fluent are you in each language? Is there a crucial aspect that needs to be optimized, like memory or speed, or will any reasonable solution to the problem at hand be sufficient? It was designed in the s and has been used by the majority of statistical communities worldwide. The primary motive behind the development of R was to allow academic statisticians to perform complex data statistical analyses. Both of them were associated with the University of Auckland when they developed R.
It is pretty hard for anyone to learn a new programming language without having a programming background. R is an open-source programming language and has a community for developers where anyone can showcase their expertise. In their community support, you can find other users who can help you while facing Stata problems.
Stata also offers extensive learning support to users in the form of blogs, tutorials, webinars, training, journals, etc. As we have already discussed, R is an open-source programming language, which means that it is free to use for anyone. On the other hand, Stata is a paid software, and every paid software is known for its online support or after-sales support.
Stata offers extensive support to its users, from online support to FAQs, documentation, video tutorials, web resources, Stata news, and webinars. R is free to use for everyone. You need to install it from the internet, and you can run it without paying a single penny to anyone.
The license is also divided into two categories, i. R offers a variety of updates at regular intervals, and you can get the latest update of R on its official site.
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