The tool's most valuable feature is its cloud-based nature, allowing accessibility from anywhere. Additionally, using Jupyter Notebook makes it easy to handle bugs and errors.

The notebook feature is an improvement over RStudio.

The most valuable feature is the Jupyter notebook that allows us to write the Python code, compile it on the fly, and then look at the results.

I found the navigators and the other features to be very good for quick development.

There is a system that is similar to a marketplace where we can search for and install all the tools that we need, and they have been collated in one place.

The solution's most valuable aspects include the repositories, the way we log, the way we install that repository management, and how we can easily integrate the solution with Jupyter Notebook.

The virtual environment is very good.

There are several things that I think are valuable in the product. My first impressions were the product was fairly responsive, sleek and had a beautiful interface that was pleasant to use. It helped me to be able to easily share code between me and my colleagues.  

I had R installed at that time as well. It worked with R as well as Python. R is good for statistics and visualization. I've used R with Tableau as well and for my situation at the time, Anaconda was a bit superior in respect to this integration.  

When I try to develop an implementer model, I can just do some trial and error and see the output immediately. Then later I can move them onto the Python file. Until that happens, I can see that it's there. That's the most useful feature for me in Anaconda.

We use Jupyter notebooks and it was all available in one package when we installed Python.

The most advantageous feature is the logic building.

The Python libraries are all readily available and there is no need to install anything separately.

There are many good things that are pre-built, and including even more of these would be a great benefit to the developer community. It would allow us to try specific models and use cases, then customize them as per a particular activity.

The best part of Anaconda is the media distribution that comes as part of it. It gets us started very quickly. 

We extensively use TensorFlow and Pytorch as the modules. 

The best part of the solution is the virtualization. You can use Python within the virtual environment. It gives us more than the local environment. In there you can do lots of useful things. 

The documentation is excellent and the solution has a very large and active community that supports it.

I mainly used the product for the libraries. 

The most valuable feature is the set of libraries that are used to support the functionality that we require. We use different libraries for finance and numbers, and we use the scikit-learn library for machine learning. A few of these libraries are very helpful and there is a very long list of them.

It's interesting. It's user friendly. That's what makes it outstanding among the other competitors.

It has a collection of R, Python, and others. Their platform strategy has a collection of many other visualization tools, apart from Spyder and RStudio, which is really helpful for data science. For any data science professional, Anaconda is really handy. It has almost all the tools for data science.

Many data science applications on one single platform. This provides a platform for learning and development in the data science domain.