Anaconda Reviews and Ratings

Rating: 8.2 out of 10

Score

8.2 out of 10

Community insights

TrustRadius Insights for Anaconda are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.

Pros

Anaconda as a one-stop destination: Many users have found Anaconda to be a convenient and comprehensive solution for data science and programming tools. It has been praised by multiple reviewers for providing important tools such as Jupyter, Spyder, and R in one platform.

User-friendly interface: The simplicity and ease of use of Anaconda's user interface have been appreciated by many reviewers. They have found it intuitive and easy to navigate through files in Jupyter, as well as install new libraries.

Flexibility in working with Python environments: Users have highlighted the flexibility of Anaconda in working with multiple Python environments based on their requirements. This feature has been found useful for different use cases by several reviewers.

Reviews

38 Reviews

Must Have for ML/DL, Data Analytics, Software Development and Deployment.

Rating: 10 out of 10

Incentivized

December 18, 2024

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.

Pros

Data Analysis.
Software Development in Python.
Machine Learning/Deep Learning model training and testing.
Code Deployments.

Cons

Sometimes, I have reached a situation where I am unable to download dependency using pip or conda, and I have to create whole new environments.
Once, I faced a very weird issue where I was unable to update or Launch Spyder and tried everything, and it didn't work.

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.

Ranu Singh

Technology Manager in Research & Development at Kainoos (51-200 employees)

Vetted Review

7 years of experience

The best and easiest data analysis tool

Rating: 10 out of 10

Incentivized

July 5, 2022

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.

Pros

Ease of downloading anaconda
Open source, anyone can download it
it used in data science and big data analysis.
Extensive community support on social media and the internet.

Cons

I wish to add several times in cases when downloading Anaconda such as Spyder.

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.

Ammar Aboalrub

Freelancer in Customer Service at Self Employed (1-10 employees)

Vetted Review

3 years of experience

View profile

Anaconda: Best IDE for Python

Rating: 9 out of 10

Incentivized

November 5, 2021

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.

Pros

Profiling
Several IDEs
User-friendly

Cons

Using better graphics for spyder

Likelihood to Recommend

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

Verified User

Engineer in Engineering (1-10 employees)

Vetted Review

3 years of experience

Best IDE for Data Science Projects

Rating: 8 out of 10

Incentivized

October 7, 2021

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.

Pros

Almost all required libraries are available in it.
Easy to create a notebook for a data science project.
[It is] flexible to work on multiple Python environments based on your requirements.
In [the] community, [it is] easy to find the forum [and] events.

Cons

[The] application [takes a lot of] time to load the first time.
Sometimes, it [stops working because it] consumes more ram.
[I would like it to] add some ready-made use case environments.

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.

Zayed Rais

Program Analyst in Information Technology at Cognizant (10,001+ employees)

Vetted Review

2 years of experience

View profile

Awesome tool for Data Scientists

Rating: 8 out of 10

Incentivized

September 9, 2021

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.

Pros

User interface is simple and easy to use.
Making the Jupyter notebook is great because that is a very great tool to run python script line by line for learning purposes.
We can easily access files and folder through it.
Auto suggesting in code is great of Spyder.

Cons

Anaconda is taking much RAM of device which needs improvements.
Spyder is sometime crashing while running the application.
Git integration is not there which is require in Anaconda.

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.

Jay Thakkar

Student in Engineering at PP Savani University (201-500 employees)

Vetted Review

2 years of experience

View profile

One stop data science destination - Anaconda

Rating: 9 out of 10

Incentivized

June 25, 2021

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.

Pros

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.

Cons

It'd be great to see some good data visualization tools on Anaconda Navigator.
Its ability to handle large data source.
I'd like to see some themes for night coders like myself. Some good UI would be appreciated.

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.

Rounak Verma

Data Analyst in Engineering at Shipt (5001-10,000 employees)

Vetted Review

3 years of experience

Powerful environment to work on what you want with what you want (not ironic!)

Rating: 8 out of 10

Incentivized

June 22, 2021

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.

Pros

Open source, leading to zero sketchy things running in the background.
Easy to install packages.
Multiple environments are easy to configure and also encouraged.

Cons

Anaconda gets bigger and bigger with each package or dependency that you own, making it a huge pain to move environments around.

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.

Gabriel Krahn

Analista de Suporte de Sistemas in Information Technology at T-Systems do Brasil (10,001+ employees)

Vetted Review

1 year of experience

View profile

Complete Data Science software suit.

Rating: 10 out of 10

Incentivized

June 15, 2021

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.

Pros

Complete package to build or work on data science projects.
All the latest modules/packages are installed very easy just with anaconda prompt.
We can use Jupyter notebook from it very easily and together we can work on Spyder as well.
It works very fast, if the system has 16GB ram then its data processing speed is also very high.

Cons

More graphics need in Spyder book. If you work for couple of years then you will be bored with the graphics.
Extra tools are required for making it secure. We uses extra tools for adding Username /Password to Jupyter.
R Studio Hangs a lot when open from Anaconda 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.

Verified User

Consultant in Information Technology (10,001+ employees)

Vetted Review

3 years of experience

Anaconda for Data Science!

Rating: 10 out of 10

Incentivized

June 12, 2021

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.

Pros

It provides easy access to software like Jupyter, Spyder, R and QT Console etc.
Easy installation of Anaconda even without much technical knowledge.
Easy to navigate through files in Jupyter and also to install new libraries.
R Studio in Anaconda is easy to use for complex machine learning algorithms.

Cons

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.

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.

Anomita Chandra

Business Analyst at University of Texas at Dallas (1001-5000 employees)

Vetted Review

4 years of experience

Hemant's review of Anaconda

Rating: 7 out of 10

Incentivized

May 21, 2021

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

Pros

Anaconda has support for many different things like spyder idle, jupyter notebook, vs code, r studio
It has both graphical and command line interface available
The community is also very good and supporting

Cons

It can improve the time of loading all the contents
It can also improve its memory and ram requirements
Some softwares should also be integrated like pycharm

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.

Verified User

C-Level Executive in Product Management (1-10 employees)

Vetted Review

4 years of experience

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Reviews and Ratings

Awesome tool for Data Scientists

Rating: 8 out of 10

Incentivized

September 9, 2021

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.

Pros

User interface is simple and easy to use.
Making the Jupyter notebook is great because that is a very great tool to run python script line by line for learning purposes.
We can easily access files and folder through it.
Auto suggesting in code is great of Spyder.

Cons

Anaconda is taking much RAM of device which needs improvements.
Spyder is sometime crashing while running the application.
Git integration is not there which is require in Anaconda.

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.

Jay Thakkar

Student in Engineering at PP Savani University (201-500 employees)

Vetted Review

2 years of experience

View profile