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Anaconda

Score8.1 out of 10

143 Reviews and Ratings

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What is Anaconda?

Anaconda provides access to the foundational open-source Python and R packages used in modern AI, data science, and machine learning. These enterprise-grade solutions enable corporate, research, and academic institutions around the world to harness open-source for competitive advantage and research. Anaconda also provides enterprise-grade security to open-source software through the Premium Repository.


Categories & Use Cases

Key Features

Learn about the best (and worst!) features Anaconda has to offer, as determined by TrustRadius' reviewers.
Based on 143 ratings of Anaconda's features
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Top Performing Features

  • Single platform for multiple model development

    Single place to build, validate, deliver, and monitor many different models

    Category average: 9.5

  • Flexible Model Publishing Options

    Publish models as REST APIs, hosted interactive web apps or as scheduled jobs for generating reports or running ETL tasks.

    Category average: 9.2

  • Connect to Multiple Data Sources

    Ability to connect to a wide variety of data sources including data lakes or data warehouses for data ingestion

    Category average: 8.8

Areas for Improvement

  • Extend Existing Data Sources

    Use R or Python to create custom connectors for any APIs or databases

    Category average: 8.9

  • Interactive Data Analysis

    Ability to analyze data interactively using Python or R Notebooks

    Category average: 8.8

  • Data Transformations

    Use visual tools for standard transformations

    Category average: 9.1

Reviews

What users are saying about Anaconda.
View all reviews

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

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

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.

Return on Investment

  • We're using Anaconda as open source, so it has only given us returns/profits, so there is no negative here.

Usability

Alternatives Considered

Docker

Other Software Used

Metabase, RabbitMQ, Camunda, Amazon Bedrock, Amazon CloudFront

The best and easiest data analysis tool

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

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.

Most Important Features

  • Easy and flexible to use.
  • Almost all widely used scientific libraries are inside it.
  • With Anaconda, your data science team can find the right visualization tool for any data set.
  • Open source , it's free.
  • The user interface is very simple and you can handle it easily.

Return on Investment

  • Increase the program space with the increase of installed packages.
  • User interfaces get bad when increasing lines of code.

Alternatives Considered

PyCharm

Other Software Used

Jupyter Notebook, Microsoft Visual Studio Code, PyCharm

One stop data science destination - Anaconda

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

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.

Most Important Features

  • As a Data Analyst in the team, my department concerns primarily with data and Anaconda provides all the major data science tools at one place.
  • The ability to install libraries using the anaconda command prompt.
  • Lots of resources available online to help beginners and those with less technical expertise.

Return on Investment

  • As a recent graduate, Anaconda was a great free tool available for me to practice programming on Jupyter, RStudio, etc.
  • It makes it a lot easier to download and export codes and share with your colleagues.
  • Its easy-to-use functionality helped a lot towards achieving good accuracy on our sales price predictor model.

Other Software Used

DataGrip, Tableau Online, Amazon S3 (Simple Storage Service)

Best IDE for Data Science Projects

Incentivized
Zayed RaisView profile
Program Analyst in Information Technology at Cognizant (10,001+ employees employees)

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.

Most Important Features

  • Supports multiple environments
  • All kinds of data science libraries found easily
  • Doesn't stop development [on] the ML project

Return on Investment

  • Anaconda is [a] leading platform in [the] data science industry.
  • It [has] good impact [across my] organization.
  • [It] provides all tools [under a] single umbrella.

Alternatives Considered

Microsoft Visual Studio Code

Other Software Used

Jupyter Notebook, Microsoft Visual Studio Code, PyCharm

Anaconda for Data Science!

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

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.

Most Important Features

  • Easy Access to Jupyter, RStudio and Spyder.
  • Permit to work on multiple projects and files simultaneously.
  • Ease of automating many processes which helped the non technical people.

Return on Investment

  • Positive impact - Multiple options for data presenting , visualizing and sharing. (Eg: R-Markdown).
  • Positive impact - Ease of access to build complex machine learning models. (I work in NLP, it has multiple built in models to analyze the various contexts).
  • Positive impact - Conda package let's to deal with external packages which can be used in Jupyter.

Alternatives Considered

SAP Business Warehouse (SAP BW), formerly SAP NetWeaver Business Warehouse and Microsoft Power BI

Other Software Used

Tableau Desktop, Microsoft Power BI

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