Anaconda provides access to the foundational open-source Python and R packages used in modern AI, data science, and machine learning. These enterprise-grade solutions enable corporate, research, and academic institutions around the world to harness open-source for competitive advantage and research. Anaconda also provides enterprise-grade security to open-source software through the Premium Repository.
I have asked all my juniors to work with Anaconda and Pycharm only, as this is the best combination for now. Coming to use cases: 1. When you have multiple applications using multiple Python variants, it is a really good tool instead of Venv (I never like it). 2. If you have to work on multiple tools and you are someone who needs to work on data analytics, development, and machine learning, this is good. 3. If you have to work with both R and Python, then also this is a good tool, and it provides support for both.
It is well suited for projects that are more discovery related. If this is a one-time project that we create a visual for, this would definitely make sense to use. If this is an ongoing analysis (monthly for example), we might look to another software that we would be able to automate a little further in how the visualization comes together
Anaconda is a one-stop destination for important data science and programming tools such as Jupyter, Spider, R etc.
Anaconda command prompt gave flexibility to use and install multiple libraries in Python easily.
Jupyter Notebook, a famous Anaconda product is still one of the best and easy to use product for students like me out there who want to practice coding without spending too much money.
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.
I used R Studio for building Machine Learning models, Many times when I tried to run the entire code together the software would crash. It would lead to loss of data and changes I made.
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.
It's really good at data processing, but needs to grow more in publishing in a way that a non-programmer can interact with. It also introduces confusion for programmers that are familiar with normal Python processes which are slightly different in Anaconda such as virtualenvs.
While Gephi isn't perfect, it's a powerful tool for mathematical graph modelling that's hard to find in other products, particularly by way of its interface. It grants non-software developers access to a point-and-click way of creating accurate, beautiful visualizations that would normally take hours in other applications. The fact that it allows for live streaming data is also something that's hard to come by, at least for visualization software
I am giving this rating because I have been using this tool since 2017, and I was in college at that time. Initially, I hesitated to use it as I was not very aware of the workings of Python and how difficult it is to manage its dependency from project to project. Anaconda really helped me with that. The first machine-learning model that I deployed on the Live server was with Anaconda only. It was so managed that I only installed libraries from the requirement.txt file, and it started working. There was no need to manually install cuda or tensor flow as it was a very difficult job at that time. Graphical data modeling also provides tools for it, and they can be easily saved to the system and used anywhere.
Gephi is very intuitive and the fact that it shows its process helps the user understand what's going on. However, the animation features can really slow it down and there isn't a way to shut them off. Furthermore, the failures on saving mean you frequently have to start over. These problems disrupt the workflow and can be frustrating.
Anaconda provides fast support, and a large number of users moderate its online community. This enables any questions you may have to be answered in a timely fashion, regardless of the topic. The fact that it is based in a Python environment only adds to the size of the online community.
I have experience using RStudio oustide of Anaconda. RStudio can be installed via anaconda, but I like to use RStudio separate from Anaconda when I am worin in R. I tend to use Anaconda for python and RStudio for working in R. Although installing libraries and packages can sometimes be tricky with both RStudio and Anaconda, I like installing R packages via RStudio. However, for anything python-related, Anaconda is my go to!
The interactivity in Gephi and the quality of the output figures are impressive. However, the selling point was the fact that we were able to link Gephi into our pipeline using Java's interface. Other products were less customizable and lacking of the sophistication Gephi provided without too much pain during the liking process.
It has helped our organization to work collectively faster by using Anaconda's collaborative capabilities and adding other collaboration tools over.
By having an easy access and immediate use of libraries, developing times has decreased more than 20 %
There's an enormous data scientist shortage. Since Anaconda is very easy to use, we have to be able to convert several professionals into the data scientist. This is especially true for an economist, and this my case. I convert myself to Data Scientist thanks to my econometrics knowledge applied with Anaconda.
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.
