Anaconda

Anaconda

Top Rated
About TrustRadius Scoring
Score 8.7 out of 100
Top Rated
Anaconda

Overview

Recent Reviews

Anaconda Review

8 out of 10
June 28, 2021
I am a machine learning engineer and certified data scientist who is solving some real-world problems and used to teach students. I …
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Hemant's review of Anaconda

7 out of 10
May 21, 2021
Anaconda is currently used as the complete python environment setup tool. It has been easy for us to automate the process of setting up …
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My Anaconda Review

8 out of 10
May 21, 2021
Anaconda is not just a tool it is a complete package to build and deployment of the project related go machine learning , neural networks, …
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Awards

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Popular Features

View all 16 features

Data Transformations (25)

8.8
88%

Extend Existing Data Sources (23)

8.7
87%

Interactive Data Analysis (23)

8.6
86%

Visualization (24)

8.6
86%

Reviewer Pros & Cons

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Video Reviews

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Pricing

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Commercial Edition

$14.95

Cloud
per month

Team Edition

10,000

Cloud

Enterprise Edition

Contact for quote

Cloud

Entry-level set up fee?

  • No setup fee

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting / Integration Services

Features Scorecard

Platform Connectivity

8.4
84%

Data Exploration

8.6
86%

Data Preparation

8.5
85%

Platform Data Modeling

8.6
86%

Model Deployment

8.2
82%

Product Details

What is Anaconda?

Anaconda is an open source Python distribution / data discovery & analytics platform.

Anaconda Video

Anaconda Introduction

Anaconda Technical Details

Deployment TypesSaaS
Operating SystemsUnspecified
Mobile ApplicationNo

Comparisons

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Reviews and Ratings

 (113)

Ratings

Reviews

(1-25 of 37)
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Zayed Rais | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
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.
Score 7 out of 10
Vetted Review
Verified User
Review Source
A must-try for smaller data analytics teams who seek project reproducibility, multiple language support and extensive community support. For bigger teams, consider the enterprise version, which makes it easy for app, API deployment, authentication, custom repository, and sharing of work spaces.
Jay Thakkar | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
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.
June 28, 2021

Anaconda Review

Dilip Jain | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
To design an end-to-end solution or machine learning model, Anaconda is the one that can easily manage all the libraries and we can set the environment according to the project requirement. Anaconda is the best data science version control tool in the present time. This is the best solution that is packed with lots of ideas and good features. But in the case of designing the analytics dashboards and all then we give less priority to Anaconda but we can use analytics tools like Tableau or PowerBI.
Score 9 out of 10
Vetted Review
Verified User
Review Source
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.
Gabriel Krahn | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
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.
Score 10 out of 10
Vetted Review
Verified User
Review Source
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.
Score 10 out of 10
Vetted Review
Verified User
Review Source
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.
Score 7 out of 10
Vetted Review
Verified User
Review Source
[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.
Fernanda Ministerio | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source

When choosing Python or R for software development, you choose a large language ecosystem with a wide variety of packages covering all programming needs. But in addition to libraries for everything from GUI development to machine learning, you can also choose from a variety of tool runtimes and their libraries; some runtimes may be more suited to the use case you have at hand than others.

Anaconda has versions optimized for special use cases. Anaconda was designed for Python developers who need a distribution supported by a commercial provider and with support plans for companies. The main use cases for Anaconda Python are mathematics, statistics, engineering, data analysis, machine learning, and related applications.

Anaconda groups together many of the most common libraries for commercial and scientific work in Python--SciPy, NumPy, Numba, and so on--and makes it much more personalized through a package management system.

Anaconda stands out from the other distributions for the way it integrates all these pieces. When installed, Anaconda offers a desktop application--Anaconda Navigator--that makes all aspects of the Anaconda environment available through a convenient user interface. Finding components, customizing them, and working with them is much easier with Anaconda than with CPython.

Another benefit is the way Anaconda handles components from outside the Python ecosystem, if they are prioritized for a specific package. Conda conda packages, created specifically for Anaconda, deal with the installation of Python packages and external third-party software requirements.

Since Anaconda includes so many useful libraries and can install even more with just a few keys, the size of an Anaconda installation can be much larger than that of other competitors. This can be an issue in situations where you have resource constraints.

Score 7 out of 10
Vetted Review
Verified User
Review Source
Anaconda is definitely good when it comes to large-scale projects in python requiring different versions of python as a dependency on project packages and use cases. It consumes heavy memory and is not suitable for smaller projects and is likely overkill for the same. If the user is new to anaconda, it takes time to get comfortable with it.
Score 9 out of 10
Vetted Review
Verified User
Review Source
Anaconda is great for setting a standard development environment for beginners. it is a very complete base deployment and does not require anything else to start running some basic datascience packages. while it allows you to install the packages via 'conda' the packages are not always the latest compared to pip.
April 16, 2021

Review for Anaconda

Tigran Petrosyan | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
Anaconda have a lot of scientific libraries of Python which I use in my everyday work (Pandas, Numpy, Seaborn, matplotlib, etc). Jupiter Notebook is a best option for me if I have small tasks or small projects which I must do using Python. However if I have large projects I prefer to use PyCharm.
Score 9 out of 10
Vetted Review
Verified User
Review Source
Anaconda (specifically Jupyter Notebook) is well suited for sharing code with other teams that are less programming-focused. It is very easy for me to open a pre-written code notebook, enter some values in the initial inputs box, press Run, and see my results. It is simple enough to open the notebook, and run the code, so we can use it in our production area and train the assembly technicians on how to operate it. If the code is properly written, it is easy to know when something went wrong.
Juande Santander-Vela | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
Anaconda, and Miniconda, are easy to deploy, scientifically-ready Python distributions, especially well-suited for the fields of science, astronomy, and engineering. We are using Miniconda for finer-grained customization of environments in containers for deployment.

We are not using the customer supported version of Anaconda, and instead, we are relying on the community edition, based on the Open Source of all of our software. Hence, I am not evaluating Anaconda's support. Also, we are not making use as a company of the multi-language support in Anaconda, but I have tried the SciJava, R, and Julia support in Anaconda.
Score 10 out of 10
Vetted Review
Verified User
Review Source
Anaconda is the best data science version control tool in the market. With Anaconda, you can easily create, remove, and switch environments to run different scripts. What is more, you can also use it to export the current environment automatically into yaml file that can be used to recreate the same environment.
Score 7 out of 10
Vetted Review
Verified User
Review Source
Anaconda is very suitable for a research team/lab/department which has many data scientists who want to apply some Python-based analytic programming and want to cooperate in sharing the results easily. It is not very well suited for final production environment deployment.
Score 10 out of 10
Vetted Review
Verified User
Review Source
Anaconda is an all-in-one package. It is free and easy to install in any operating system. I'm using Anaconda in physics and engineering-related results. It is very suitable in data analysis too. You can work with big data in a very simple way. It has Spyder, Jupyter and many more installed in one platform. Jupyter Notebook is very nice feature in Anaconda. I believe it is appropriate for every problem and in every field.
Score 10 out of 10
Vetted Review
Verified User
Review Source
For anyone who's starting data analytics, Anaconda is great because you don't have to deal with installing and knowing all these Python and R packages yourself. Moreover, you learn them as you start using them via Anaconda. If you're going to read, manipulate, preprocess, and write data, Anaconda is great. If you need data visualization, Anaconda has Jupyter Notebook, as well as Matplotlib, and Seaborn. If you need forecasting and prediction, whether it is classification or regression or even unsupervised learning, Anaconda provides the Sci-kit Learn library. Furthermore, you can install Catboost, XGBoost, LGBM via Anaconda, which uses the Sci-kit Learn interface.