Anaconda Reviews and Ratings

Use Cases and Deployment Scope

We're using Anaconda for software further software for our clients. Earlier, I used both R and Python, but now I am mainly using it for Python. As we have multiple applications running on multiple Python versions ranging from Python 2.x to 3.x. and with Anaconda, this becomes relatively easy with its environments. I am actively using Spyder, PyCharm, and Jupyter Notebook. Apart from this, we are actively using Anaconda on our servers to deploy any machine learning applications.

Likelihood to Recommend

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.

Use Cases and Deployment Scope

Since my beginnings in the programming path I have been using it, and it is very difficult to do without it. It provided me with some software that I needed such as Spyder Editor and its scientific script because you will need it in most of your projects such as NumPy, Dusk, Pandas, Matplotlib, and others.

Likelihood to Recommend

We said Anaconda for Python does data science activities, Anaconda for Python does it perfectly, and it's open-source too, Anaconda includes many very suitable for beginners standard data science packages and science libraries inside. It is easy to install on any operating system you want, and it is considered the best data science version control tool at present.

Use Cases and Deployment Scope

I use Spyder for developing my Python codes a lot. And Anaconda provides an excellent IDE to accomplish my tasks.

Likelihood to Recommend

It is an excellent IDE for writing Python scripts. It supports too many libraries and APIs, and provides very good help.

Use Cases and Deployment Scope

Anaconda is the best tool for the data scientist to [develop] the machine learning project [under] a single umbrella. It is used [throughout] the whole organization. We are using the Anaconda for Python [and] R to do the data science activities end-end process, i.e. importing the statistical/ML/Visualization libraries to train and visualize the data/reports.

Likelihood to Recommend

Anaconda is well suited for data science projects. If you are working with multiple projects, it [is] easy to build different environments for the requirements of the project. Easy interaction with [the] notebook for data collection, pre-processing, transforming, training, and visualizing. Sometimes, we are unable to update the libraries due to some security patches.

Use Cases and Deployment Scope

Anaconda is mainly used for Python programming like data science and computer vision script.

Advance mathematics operation is easily done by Anaconda.

I mostly used Jupyter Notebook and Spyder.

It makes it easy to script in python through the user interface of Anaconda software.

Accessing libraries of python through Anaconda is easy and efficient.

Likelihood to Recommend

Started with learning Python through Jupytor Notebook.

I have used Anaconda for image processing application making.

In which I have used Spyder and include many libraries of image processing.

Debugging of code made easy through it.

Auto suggestions in spyder are great to write code fast and efficiently.

You can observe the memory space required for your file through it.

Use Cases and Deployment Scope

My previous organization used Anaconda, Jupyter Notebook specifically to run sales forecasting codes in Python. At the time, it was specifically used by the E-Commerce and Buying team to make buying decisions. The ease of using Anaconda Navigator was a very big plus point for my organization as they could save a ton of time and money that was needed behind the training.

Likelihood to Recommend

As a Data Analyst, it is my job to analyze large datasets using complex mathematical models. Anaconda provides a one-stop destination with tools like PyCharm, Jupyter, Spyder, and RStudio. One case where it is well suited is for someone who has just started his/her career in this field. The ability to install Anaconda requires zero to little skills and its UI is a lot easier for a beginner to try. On the other hand, for a professional, its ability to handle large data sets could be improved. From my experience, it has happened a lot that the system would crash with big files.

Use Cases and Deployment Scope

We used Anaconda to develop solutions to analyze blocks of information from customer tickets in order to gather information about our monthly resource x workforce relation.

Likelihood to Recommend

Anaconda is a good choice when you have to build different environments to perform different tasks (for example, one environment with Python 3.7 + TensorFlow and the other with Julia + TensorFlow.jl or even Flux). The fact that it supports an easy switch between different environments (if you ignore the part about your installation getting bigger and bigger) is a big win situation.

Use Cases and Deployment Scope

The teams which are working on analytics-based projects are using Anaconda/Anaconda Navigator for various other tools. Like building small applications using python on Spyder is used in it. Also for reporting and visualization R and Orange has used it.

Used department and Teams wise as per the requirement from the stakeholders. Not every team is using this navigator.

Likelihood to Recommend

This will suit to any kind of work now days. We have built many data science applications using Anaconda Navigator. This is very easy to use and can be used for any work. We have used it for Image processing projects and worked very much accurately as we were able to install all the latest packages.

Use Cases and Deployment Scope

Anaconda is being used by the Business Intelligence and Data Science department at my organization. It is used widely for analysis, forecasting and answering questions. For example, the commercial department wanted to know which of their three products was most affected by COVID-19. So using Anaconda Jupyter Notebook and data from surveys conducted with customers we could come to a conclusion. It was easy to represent the findings in visual forms.

Likelihood to Recommend

Being a Data Science and Analyst professional Anaconda is the go to place for all the softwares.

Easy to access Jupyter, RStudio and gives direct access to your files in your PC. It is compatible to install as many libraries required for the work you do. I have worked with large live data for a project on RStudio and it let me easily connect to it, though the system crashed sometimes when I tried to execute the entire code but it always created a recovered file of the changes I made. So that was one of the features I really liked.

Use Cases and Deployment Scope

Anaconda is currently used as the complete python environment setup tool. It has been easy for us to automate the process of setting up the coding environment for all of our employees. We have created separate environments for separate purposes. Anaconda has been very useful. Not only [do] we have every requirement at one place but we can also manage it more efficiently and debug problems more easily

Likelihood to Recommend

[Anaconda] is appropriate if you have a employee force of more than ten people it helps in automating the work of setting up the systems so that people can work. It is very helpful and reduces a lot of time which is wasted on doing something which is not productive.