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Anaconda

Anaconda

Overview

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.…

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Recent Reviews

TrustRadius Insights

Anaconda is a versatile tool that has found widespread use across various departments and teams within organizations. It is highly …
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Awards

Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards

Popular Features

View all 16 features
  • Data Transformations (25)
    9.6
    96%
  • Visualization (24)
    9.6
    96%
  • Extend Existing Data Sources (23)
    8.9
    89%
  • Interactive Data Analysis (23)
    8.9
    89%
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Pricing

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Free Tier

$0

Cloud
per month

Starter Tier

$9

Cloud
per month

Business Tier

$50

Cloud
per month per user

Entry-level set up fee?

  • No setup fee
For the latest information on pricing, visitwww.anaconda.com/pricing

Offerings

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

Introducing Anaconda Distribution for Python in Excel

YouTube

Introducing: Anaconda Assistant

YouTube

Anaconda for Open-Source Security with Python and R

YouTube

AI Development in the Enterprise with Anaconda's Data Science Platform

YouTube
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Features

Platform Connectivity

Ability to connect to a wide variety of data sources

9.4
Avg 8.5

Data Exploration

Ability to explore data and develop insights

9.2
Avg 8.4

Data Preparation

Ability to prepare data for analysis

9.4
Avg 8.2

Platform Data Modeling

Building predictive data models

9.3
Avg 8.5

Model Deployment

Tools for deploying models into production

9.5
Avg 8.6
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Product Details

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.


Anaconda Features

Platform Connectivity Features

  • Supported: Extend Existing Data Sources

Data Exploration Features

  • Supported: Visualization
  • Supported: Interactive Data Analysis

Data Preparation Features

  • Supported: Data Transformations
  • Supported: Data Encryption

Platform Data Modeling Features

  • Supported: Multiple Model Development Languages and Tools
  • Supported: Automated Machine Learning
  • Supported: Single platform for multiple model development
  • Supported: Self-Service Model Delivery

Model Deployment Features

  • Supported: Flexible Model Publishing Options
  • Supported: Security, Governance, and Cost Controls

Anaconda Integrations

Anaconda Technical Details

Deployment TypesOn-premise, Software as a Service (SaaS), Cloud, or Web-Based
Operating SystemsWindows, Linux, Mac
Mobile ApplicationNo
Supported CountriesGlobal

Frequently Asked Questions

Dataiku, Domino Enterprise MLOps Platform, and Posit are common alternatives for Anaconda.

Reviewers rate Connect to Multiple Data Sources and Data Encryption highest, with a score of 9.7.

The most common users of Anaconda are from Enterprises (1,001+ employees).
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Comparisons

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Reviews and Ratings

(145)

Community Insights

TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!

Anaconda is a versatile tool that has found widespread use across various departments and teams within organizations. It is highly regarded by users for its ability to import libraries, train predictive algorithms, and estimate value sources. This makes it an invaluable tool for data scientists and machine learning engineers who rely on it for real-world problem-solving and project development. Anaconda's package management system is particularly appreciated as it helps maintain up-to-date Python libraries, saving time and avoiding installation issues. Additionally, its cross-platform program facilitates seamless collaboration among Mac/PC/Linux users.

One of the key use cases of Anaconda is in the field of business intelligence and data science. Professionals in this domain utilize Anaconda for analysis, forecasting, and answering critical questions. Organizations also leverage Anaconda to identify the impact of COVID-19 on different products by analyzing customer survey data. The software's robust capabilities make it an ideal choice for managing large-scale projects with multiple dependencies, ensuring reproducibility of analysis, and providing a standardized working environment. Furthermore, Anaconda serves as a comprehensive data analysis environment, particularly when coupled with the user-friendly Jupyter Notebook.

In addition to its applications in data science and business intelligence, Anaconda finds utility in other areas such as banking departments for coding complex tasks like risk prediction and evaluation. It also supports software development objectives by enabling quick setup of development environments for employees. The product is widely used in analytics-based projects, including building small applications with Spyder and reporting and visualization with R and Orange. Moreover, researchers in fields like engineering and geoscience often turn to Anaconda as a research platform for prototyping custom algorithms and sharing progress with teammates.

Overall, Anaconda proves itself as an indispensable tool that streamlines coding workflows, ensures version control, enhances collaboration among teams, simplifies package management, enables efficient scripting in Python, offers a wide range of libraries and packages for various domains, automates routine tasks like excel sheet modifications, and provides a robust environment for data analysis and visualization.

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.

  1. Slow performance and high resource consumption: Several users have expressed frustration with the slow performance of Anaconda, particularly when it comes to bootstrapping the software and loading its contents. Additionally, some reviewers have mentioned that Anaconda can consume a significant amount of RAM, making it unsuitable for large projects or older machines.

  2. Difficulty in installing packages and libraries: Many users have encountered challenges when installing specific Python libraries using Anaconda's package manager, conda. Some reviewers had to uninstall and reinstall Anaconda multiple times to resolve issues with library installation. Others found it confusing to work with Anaconda alongside other Python packages and versions on their machine.

  3. Lack of support and technical troubleshooting difficulties: A number of users have mentioned the lack of support for the free version of Anaconda, making it difficult to troubleshoot issues without technical assistance. Reviewers felt frustrated when encountering software crashes while running code in Anaconda, leading to data loss. They also expressed dissatisfaction with the irregular security updates and the lack of integration with version control tools.

Users commonly recommend Anaconda as an excellent IDE tool for Python developers. They appreciate its user-friendly interface and the positive coding experience it provides. Users also find it easy to manage libraries in different programming languages. Additionally, they value the availability of helpful training materials and tutorials, particularly for beginners in data science and machine learning. As a result, users suggest starting with Anaconda for beginners and using it for projects involving Python programming. Furthermore, they recommend considering PyCharm as a more sophisticated IDE alternative.

Attribute Ratings

Reviews

(1-1 of 1)
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Matthew Deakyne | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Incentivized
Anaconda is being used for data analysis purposes. We use it to mainly manage python distributions and to preload scientific packages that make working with data very easy. It's used by pockets on campus, mostly those who have research needs. It relieves us from having to purchase expensive software like SAS or SPSS and uses both Python and R.
  • Clear install story. There are a lot of ways to install python. There's only one way to install anaconda. This makes teaching and standardizing much easier.
  • Batteries included. It's easy to install things in python, but anaconda ships with most of what you need out of the box. This helps with standardization and reproducibility.
  • Good integrations with Jupyter and other visual tools. Jupyter is really convenient when learning various python packages. Anaconda makes these tools easy to launch and to use.
  • Doesn't play well with other Python. I use python for more than data science, and whenever I have multiple versions of python on my machine —some using conda, some using Pipenv, some using poetry— it can get really confusing. If Anaconda is all you use, then it works really well.
  • Not all packages are available in Anaconda. Conda install doesn't always work for all PyPI packages. This adds to the frustration above - as you have to install some packages outside of conda, and then figure out how to use them internally.
  • Visualizations don't always work like you'd hope. This is getting better, but creating interactive graphics doesn't always work well in this context.
Anaconda is excellent if all you do is data science. If you are already a python developer, then it may be more frustrating having multiple ways to manage your distribution and your packages. It has very clear use cases and makes starting off in data science much easier than figuring out all you'd need to install in Vanilla Python.
Platform Connectivity (3)
70%
7.0
Connect to Multiple Data Sources
100%
10.0
Extend Existing Data Sources
80%
8.0
Automatic Data Format Detection
30%
3.0
Data Exploration (2)
75%
7.5
Visualization
90%
9.0
Interactive Data Analysis
60%
6.0
Data Preparation (3)
83.33333333333334%
8.3
Interactive Data Cleaning and Enrichment
90%
9.0
Data Transformations
100%
10.0
Data Encryption
60%
6.0
Platform Data Modeling (4)
95%
9.5
Multiple Model Development Languages and Tools
100%
10.0
Automated Machine Learning
90%
9.0
Single platform for multiple model development
100%
10.0
Self-Service Model Delivery
90%
9.0
Model Deployment (2)
85%
8.5
Flexible Model Publishing Options
90%
9.0
Security, Governance, and Cost Controls
80%
8.0
  • Anaconda has helped us analyze academic data to inform business decisions.
  • Anaconda has helped us visualize complex data sets into understandable graphics.
  • Anaconda has helped us explore machine learning abilities and limitations.
I prefer Anaconda due to the control I have at every level over the data and the visualizations. Power BI does a better job at guessing what graphics to use, but these usually aren't the most helpful. Anaconda and the slew of Python extensions that add incredible functionality, make it much more powerful without such a steep learning curve as Power BI.
15
Most of the users are researchers or analysts in some capacity. It is featured in Software Carpentry workshops, and so the usage has expanded to Academics and some Staff members.
2
The two supporters in an ongoing basis are based in the Libraries, and their positions are to support data practices in research. They have functional programming skills, such as version control and unix command line expertise, as well as scientific programming skills such as R and Python. They instruct on how to install and use Anaconda in practical workshops, and also have consulting services.
  • Data Analytics. Evaluating data sets.
  • Data Exploration. Visualizing relationships within data sets.
  • Reporting. Creating reproducible reports.
  • Introduction to programming environment. It works well as an introduction, as the install story is really clear.
  • Data literacy. Helping non-programmers and non-statisticians better understand the capabilities of machine learning and other computing techniques.
  • Data pipelines. Moving data from one system to another is handled by many different systems - anaconda and python make this fairly easy.
  • Replacing programs such as Excel.
  • Generating interactive websites.
  • Automating reporting across the University.
It's really good at data processing, but needs to grow more in publishing in a way that a non-programmer can interact with. It also introduces confusion for programmers that are familiar with normal Python processes which are slightly different in Anaconda such as virtualenvs.
  • Installation. This is the highlight of anaconda. It's really easy to install.
  • Launching interactive consoles. It integrates with Jupyter really well.
  • Getting started. All the libraries you'd need are included.
  • Managing Python virtual environments.
  • Version control. You can use git, but better integration would be nice.
  • Developing interactive websites. It's not really intended for this.
No
It's really good at installing and getting started. It's less usable and configurable after that. If you stay in the ecosystem, and don't know how to Python any other way - it works really well.
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