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Anaconda is a versatile tool that has found widespread use across various departments and teams within organizations. It is highly …
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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.…
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Popular Features
- Data Transformations (25)9.696%
- Visualization (24)9.696%
- Extend Existing Data Sources (23)8.989%
- Interactive Data Analysis (23)8.989%
Pricing
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
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
Features
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Product Details
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- Integrations
- Competitors
- Tech Details
- FAQs
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 Competitors
Anaconda Technical Details
| Deployment Types | On-premise, Software as a Service (SaaS), Cloud, or Web-Based |
|---|---|
| Operating Systems | Windows, Linux, Mac |
| Mobile Application | No |
| Supported Countries | Global |
Frequently Asked Questions
Reviewers rate Connect to Multiple Data Sources highest, with a score of 9.8.
The most common users of Anaconda are from Enterprises (1,001+ employees).
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Reviews and Ratings
(143)
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!
- Business Problems Solved
- Pros
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- Recommendations
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.
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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.
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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.
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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
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July 05, 2022
The best and easiest data analysis tool
Score 10 out of 10
Vetted Review
Verified User
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.
November 05, 2021
Anaconda: Best IDE for Python
I use Spyder for developing my Python codes a lot. And Anaconda provides an excellent IDE to accomplish my tasks.
October 07, 2021
Best IDE for Data Science Projects
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.
September 11, 2021
Anaconda - a platform made for Py.
Anaconda open-source distribution is a flexible platform enabling users to work with several popular data analytics languages such as R and Python.
It is being used by Engineering and Geoscience teams to prototype custom algorithms for use in solving use cases in the oil and gas industry, including subsurface, operations and other relevant functional area such as health, safety and environment.
It is being used by Engineering and Geoscience teams to prototype custom algorithms for use in solving use cases in the oil and gas industry, including subsurface, operations and other relevant functional area such as health, safety and environment.
September 09, 2021
Awesome tool for Data Scientists
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.
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.
June 28, 2021
Anaconda Review
I am a machine learning engineer and certified data scientist who is solving some real-world problems and used to teach students. I generally used to work on the project that is beneficial for me as well as the society to make life easier. I used to create machine learning models and host them on the cloud. I used Anaconda as my primary software to work on my projects. Best for setting your Python environment. 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. With anaconda, you can easily create, remove, and switch environments to run.
June 25, 2021
One stop data science destination - Anaconda
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.
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.
June 15, 2021
Complete Data Science software suit.
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.
Used department and Teams wise as per the requirement from the stakeholders. Not every team is using this navigator.
June 12, 2021
Anaconda for Data Science!
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.
May 21, 2021
Hemant's review of Anaconda
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
May 16, 2021
Data Science and Anaconda
The company has several departments and distributed units that are adopting the use of data science to improve institutional performance. Anaconda has been used as a tool to support professionals who improve data and their results for the management of the organization. We still have a lot to evolve in data management, integration, standardization, and data improvement; but the continued use of Anaconda will allow us to identify our bottlenecks and make better decisions.
May 10, 2021
Big guns for big scale projects: Anaconda
Anaconda is a great package manager for large-scale projects with multiple dependencies and support for multiple versions of python. It offers us out-of-the-box capabilities for major common data science use cases and projects. Really robust in terms of switching execution environment and offers granular control over the Conda virtual environments. It is used across our organization as it has really great community support and they offer solutions in case we are stuck as well.
Our team is using Anaconda as a python distributions tool for running python code. It contains and supports the maximum number of python libraries and packages. It has helped us to set up a complete data analysis environment with the help of Jupyter Notebook.
April 30, 2021
Anaconda for beginner data scientists!
Anaconda is a standard installation for our python coders. Our python coders stretch across multiple departments so it should be considered being used by the whole organization. Anaconda makes it easy for us to standardize getting a base working environment ready for everyone. From there it makes is easy for users to install required packages.
April 16, 2021
Review for Anaconda
Anaconda is mainly used only by some of the departments in our bank. Specifically in our department it is mainly used as a environment for coding in python although some of my colleagues use it as a environment for coding in R. As risk managers we usually do some complex tasks using python (to predict exchange rate fluctuations and evaluate market and credit risks).
May 02, 2020
Easily share code with other departments
Anaconda Navigator is used across a few departments as a way to share code that is used to analyze data from our products. The data is stored in the cloud. Some engineers write the code to analyze and print results for manufacturing tests. The manufacturing team can then easily run the code to receive the results of the tests.
The usage of Anaconda is not yet standard through the organization, but many people at my organization use it as the best way to create a standardized Python environment. In particular, the Miniconda distribution is preferred for deployment of Python-based containers, as it allows for a better, finer-grained installation in containers. For desktop users, the full Anaconda distribution is used, as it comes with several packages that are used throughout the organization: Astropy, NumPy, Matplotlib, Pandas, and others.
March 05, 2020
Anaconda provides a vast array of compilers and tools for every developer - and is easy enough for non-developers to setup.
We use Anaconda to support software development objectives for our staff. It helps us reduce "time waste" by quickly onboarding employees and setting up the majority of their development environments, so they have all of their necessary tools.
March 03, 2020
Anaconda turned a group into a team
Anaconda is being used in the entire organization of my company. It ensures that data science teams across the whole organization manage our python environment and make sure the repeatability of the packages that we built internally as well as the notebooks and projects created by different teams.
February 28, 2020
Anaconda: A data scientist's best friend
Anaconda is widely used in my organization to set up the python environment and perform version control. By setting up the environment yaml file, you can ensure the other users can run the analysis based on the same environment. Also, Anaconda provides other tools such as RStudio/spyder via the navigator.
February 18, 2020
Anaconda - The tool to master for Python based data analytic tasks
The data science and operation research team in our company majorly uses Python as the programming language. So Anaconda was chosen to provide one research platform, allowing the data scientists to work on one unified environment, across different OS, using the same language while being able to share the work progress as well as results and promote the team efforts.
February 18, 2020
Anaconda for Python
Anaconda is used mainly for handling different Python versions and packages and being used for data analysis tasks on collaborative projects in the ECE department.
February 14, 2020
Anaconda is the best distribution which includes all in one
We use the Anaconda package for physics and engineering research. We get large data in accelerator physics experiments. We use Anaconda for many purpose, but especially for its Python libraries. We have mainly used this platform for data analysis and making a nice plot. Many faculty, staff and students are using it in their research.
January 22, 2020
All-in-one data science package
Score 10 out of 10
Vetted Review
Verified User
We're using it in our department for data-related business needs, data retrieval, data manipulation, data preprocessing, visualization, forecasting, and prediction. So whether the business problem is a simple data analytics problem or complex modeling, Anaconda is used in our department. We use Anaconda for its Python libraries that come as a package, which is great. Not to mention that it eases the pain of updating all packages that would otherwise be carried out one by one for each. We use Panda's library within Anaconda to read, manipulate, preprocess and write the data. We use Numpy for mathematical functions. We use Matplotlib and Seaborn for data analysis and visualization. Finally and most importantly, we use Sci-kit Learn to create predictive models because it contains almost all the algorithms we need. Sadly, it does not contain XGBoost, CatBoost or LBGM however it is easy to install those with Anaconda because that's what Anaconda is for - helping managing all these packages, whether it is installation or simply updating.