Anaconda vs. WebStorm

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.

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Groups that use a [variety] of development environments (.net, python, web), Jebrain's products and Webstorm, in particular, are a great choice for productivity. If your developers are accustom to another IDE it might be better for all to sync on the same one. I wouldn't take the cost aspect as a consideration. A productive developer is far more important.

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• 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.

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• Includes a built in spell checker to eliminate time wasted searching for small mistakes.
• Provides a drop down menu of options complete with keyboard shortcuts that allow for streamlined programming.
• Identifies when variables are named differently, allowing for cohesive work without the scrolling.

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• It can have a cloud interface to store the work.
• Compatible for large size files.
• 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.

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• The popup file search sometimes frustrates me. It caps the number of results, and sometimes it isn't clear that it simply stopped looking. I also used to have trouble finding string occurrences that I knew were in my project, but I think they've made improvements in this area recently.
• Being a AAA IDE, WebStorm can be a memory hog. If I don't kill it every few days, it can get really slow. I would love to see performance improvements.
• Speaking of performance, WebStorm can take a long time to launch. I'd like to see improvements in launch times.

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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.

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It's an irreplaceable part of my toolkit and isn't that expensive per year.

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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.

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Great in debugging, testing, developing and maintaining softwares in a number of languages. Great support for many languages and their syntaxes. Great support of many third party extensions and plugins like GIT and html plugins. The RAM usage of WebStorm however could be really improved, it literally takes almost all of the RAM of my machine with 16GB RAM

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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.

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I gave this rating because I have never needed their customer support, which is the highest level of support I suppose. When a product works just fine out of the box and everything you may need is well documented, it's a paradise for the customer. But I've seen some questions asked on their portal, and I've seen thorough answers given to the questions and the willingness to support the customer with follow-ups and everything else.

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Highly recommend the JetBrains Toolbox, it remembers your recent projects and launches from the task bar.

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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!

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I think WebStorm is way ahead of open-source editors. Please don't get me wrong, I love open-source. But the other free editors have a lot of configuration which blocks my whole coding experience. Take "Atom" for example. I used it for about 3 months, but in that time I had to update the plugins hundreds of time, and for every little thing (e.g. linter) I had to download a community plugin, and with each plugin atom would get a little slow. Take "Eclipse" for another example, which is very suited for Java, but not so suited for JavaScript. Sublime was good, but WebStorm comes with many exciting features that I did not find in sublime/notepad.

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• 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.

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• Saves lot of time when we are finding the text or files.
• Easy to work with for support projects.
• Nice code completion feature.
• It's all in one solution for both front end, back end and database solution to work with.
• Initial learning curve is required to use the workbench.
• Documentation can be improved better probably adding videos, would be helpful for visual person.

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