Anaconda vs Gephi

• First of all it is very easy to install and it is user friendly. You just download a Anaconda from its official site and you can start using it for coding (I usually code using Jupiter Notebook) Compared to Pycharm it is easier navigate in Anaconda(Jupiter Notebook)
• For me it is a best environment to use if I have small projects. Jupiter Notebook is running tasks much faster compared to Pycharm and other IDE's.
• In my work I usually need different scientific packages that are not commonly used. As Anaconda have thousands of libraries it helps me making my job easier

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• In comparison to other tools such as GraphWiz or Circos, Gephi comes with an intuitive, easy-to-use interface that makes it easy to load your data, and quickly start building all sorts of different graphs. There's absolutely no code that needs to be written for either loading or modeling. And without downloading additional plug-ins, Gephi ships with quite a few standard graph models, as well as some "fun" extras such as the Sierpinski triangle, and a variety of force atlas types.
• Most of the layout types (maybe all) are highly configurable, which can make for extremely customized and unique displays of your data. Again, none of this requires the user to write any code. That said, it is possible to script custom functionality for your models, or even update the Java source code yourself, if you feel like getting technical. Gephi builds are available on GitHub, and the developers encourage people to contribute ideas, improvements, and plug-ins.
• There's a plug-in for Gephi that allows for streaming data to update your model. This essentially allows you to create near realtime graphs of your data in motion. This plug-in was by far the biggest reaston we invested time in the product; to create animated data visualizations without exhaustive hours in development.

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• Anaconda could greatly benefit by integrating with Git and other versioning software.
• The software's default installation is relatively bloated, slower on older machines, and could be improved by allowing for a lean default installation environment.
• Anaconda has an issue with supporting the current version of Computer-Vision, a commonly used machine learning package.

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• I (and many others) have had to expand Gephi's memory manually by experimenting with the configuration file. I'm glad it's possible, but it should be easier.
• Gephi sometimes crashes inexplicably and loses your work, so I have developed a habit of explicitly exporting versions of my graphs as csv's, but I think this should be handled automatically in Gephi.
• Because it is prone to crash, ideally, Gephi would help the user manage his/her use, by estimating processing and memory for very large tasks and prompting the user to confirm their requests before executing. Instead, I just tend to avoid certain functions.

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• We can get any new employee set-up on Python for Machine learning in minutes, without any assistance from IT. That's real $ savings.
• We started to experiment with Machine Learning a lot more, which leads to creating new projects which can have a tremendous impact on the business.

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• I have only used the product for education purposes. I will not be the best person to provide details about ROI and business efficiency and customer service. I was personally very excited about the tool and am continuing my work on the tool.

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