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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.
- 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.
- 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.
The main purpose of the Anaconda is to test and execute python libraries. [The] main purpose of using Anaconda is the ease of building machine learning models to train and test them. It also provides the aid to set up a complete data analysis setup using various IDE like jupyter notebooks and spyder notebooks.
- Easy to code
- Easy to debug
- Best visualization tools
- No support of git
- It uses a lot of ram
- Installing libraries and creating environment takes time
June 21, 2021
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.
- Open source, leading to zero sketchy things running in the background.
- Easy to install packages.
- Multiple environments are easy to configure and also encouraged.
- Anaconda gets bigger and bigger with each package or dependency that you own, making it a huge pain to move environments around.
May 25, 2021
Anaconda is one of the powerful tools to explore python libraries, develop an app, analyze the data and preprocess the data from various formats. It also provides an option to set up various environments and IDE for different projects for example spyder for visual work, Jupyter for data processing, and much more. The installing and tracing history of the libraries is much easier and helpful.
- Keeping track of the installed libraries.
- Working on different environments at the same time.
- Provides Ides like Jupyter, Spyder, and Eclipse.
- Working with Python is much easier with Command Line.
- A cloud backup of the environments so the user can easily install the same env on different machine.
- Share same libraries for different environments when new environment is created.
- Increase the start time of an IDE by redusing the RAM and device resource usage.
- Less battery consumption.
- Add Direct Git support.
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.
- Set environment for particular use cases.
- Comes with all the libraries that we require.
- One stop solution for data scientist.
- Best in all the tools.
- Built In data analysis tool.
- Students should have some extra benefits to exploring the advanced options that can be beneficial for them to have some real-world experience.
- Automation tool.
- Some predefined environment according to use case.
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.
- Multiplatform (multiple operating systems)
- It aggregates several important systems in the same visualization, facilitating the work of new professionals in data analysis and science
- Anaconda makes programming easier on Jupyter Notebook
- Needs to be optimized to consume less RAM on machines
- It is a great tool for the development of small projects but not for large projects
- Anaconda could have more documentation translated into other languages, facilitating the entry of users from non-English-speaking countries
May 04, 2021
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.
- Support for multiple opensource libraries
- Easy to deploy and develop
- Responsive
- Containerization of code is fast and easy
- Irregular security updates
- No support for integration with version control tools
Anaconda is used to help us build some machine learning models, neural networks, data visualization and everything about datascience. After building these models on Anaconda, we are also able to deploy these models and integerate them in our products. the powershell component of this product helps us to execute commands in bash or python.
- Anaconda helps us to realize computer solutions
- Anaconda helps us with Jupyter Notebooks in programming
- Anaconda helps us to visualize workflows.
- The variety distributions of Anaconda helps us in running on multiple operating systems
- Anaconda can be upgraded to consume less Ram when in usage
- it's installation can take less time on ubuntu operating systems
- Anaconda can improve on their user interface, making it interactive.
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).
- First of all it is very easy to install and it is user friendly. You just download a Anaconda from its official site and you can start using it for coding (I usually code using Jupiter Notebook) Compared to PyCharm it is easier navigate in Anaconda(Jupiter Notebook)
- For me it is a best environment to use if I have small projects. Jupiter Notebook is running tasks much faster compared to PyCharm and other IDE's.
- In my work I usually need different scientific packages that are not commonly used. As Anaconda have thousands of libraries it helps me making my job easier
- As I use Anaconda mainly for Jupyter Notebook I will provide cons of Jupiter Notebook, First of all it consumes a lot of RAM.
- Jupyter Notebook is a good tool for small projects. However it can not handle large projects very well as it is not structured(whereas in PyCharm you can create a project and have all files related to that project in 1 place)
- It takes some time to load Anaconda. Sometimes it even makes computer to freeze
Anaconda is used by our data science department and fulfills our many needs. As a service provider of technology incorporating data modeling and analysis, our engineers need a lot of libraries in a single tool. This requirement is fully satisfied by Anaconda, with its support to most of data science libraries.
- Support to multiple libraries
- Support to multiple source-code editors
- Very fast response without latency
- Takes time on startup
- Adding missing libraries is little difficult
- Anaconda should be a source-code editor instead of opening other editors
June 15, 2021
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.
- 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.
- 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.
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
- 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
- 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
May 09, 2021
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.
- Python environment management.
- Package management.
- Out of the box installed with commonly used packages.
- Support for R as well.
- Has a learning curve before getting comfortable.
- Pretty heavy installation due to included packages.
- Only great for larger projects.
- Requires a lot of memory to run kernels.
May 03, 2021
This has been the primary tool I used to handle our Python environment in my data analytical tasks, the biggest comparative advantage of this tool has been its user-friendliness, which makes it easy to run and allows to keep the python's environment tidy. Its efficient integration, augmentation, flexibility, and openness are also great features to improve my overall working efficiency.
- Encompasses cutting-edge technologies including AI, governance and native cloud.
- It offers users free demo and tests on the platform.
- Allows the usage of multiple languages and contains comprehensive support for libraries.
- It does not offer an inbuilt error correction mechanism, in order to pinpoint the actual errors, need to run the code line by line.
- Some additional libraries can only be manually installed through pip from external web sources.
- The loading of updates to Conda is quite time-consuming.
April 30, 2021
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.
- makes installation of python very easy
- great environment manager
- very easy to install python packages
- pricing could be improved to allow better entry for team usage
- some of the packages in pip not available via 'conda'
- the package manager is kind of slow
Anaconda is one of the largest Data Science Applications and I've been using it since my graduation. I am working on Anaconda and using its new packages which help me as a data analyst to arrange and simplify the data required for many of our projects, there are many editions of Anaconda in the market right now and users can choose the edition he/she wants to get started with for their organisation.
- Anaconda helps to code better with Jupyter Notebook.
- Easy to deploy and implement the packages.
- Better data visualization.
- There is a need to improve on documentation of Anaconda as it should be made in a more simple way.
- User interface of Anaconda need to be upgraded.
- Application startup time and its RAM utilization should be done better as it takes time to load.
Anaconda is not just a tool it is a complete package to build and deployment of the project related go machine learning , neural networks, artificial intelligence. It is loaded with the pre built library. Anaconda provide the facilities for the data visualization. Starting with the normal python script you can even create the whole data science project along with the deployment. In my second year of college I use the Anaconda [to] build my machine learning project which is based on predicting the food item in the given plate. For that i done all the computing using this software only.
Currently we are working on predicting the client requirements in our company. So we are using there preference and choice or decision made by them and according to that we will provide the recommendation.
Currently we are working on predicting the client requirements in our company. So we are using there preference and choice or decision made by them and according to that we will provide the recommendation.
- Provide support for multiple liberary and have pre loaded functionality.
- It has the support for the python and many other languages.
- It's automatically install the main function.
- It has multiplatform support
- Anaconda consume almost every resources of the computer
- It is very heavy software.
- Suitable for the small projects more.
March 17, 2020
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.
- Management of custom environments
- Support for standardizing deployments
- Deployment in containers using Miniconda
- Update of Conda packages is becoming slower. The 4.7 update was welcome, but seems to be regressing again.
March 04, 2020
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.
- It provides a smooth, intuitive GUI to automate setting up a development environment.
- Helps install new compilers without user input
- Assists with finding and installing necessary dependencies.
- Anaconda could greatly benefit by integrating with Git and other versioning software.
- The software's default installation is relatively bloated, slower on older machines, and could be improved by allowing for a lean default installation environment.
- Anaconda has an issue with supporting the current version of Computer-Vision, a commonly used machine learning package.
March 03, 2020
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.
Read Xiaotong Song's full review
May 01, 2020
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.
- User interface is easy enough for a layman to navigate.
- User interface has all the tools required to write code.
- Jupyter Notebook is easy to get lost in when there is lots of code. A way to minimize the sections to watch the progress would be a lot better.
February 18, 2020
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.
- Anaconda itself already carries the most popular Python packages so for most developers it is sufficient enough to deal with the normal work requirements.
- The Jupyter Notebook is a very encouraging feature which allows the researcher to apply the data analysis in an intuitive way. It provides step by step understanding the data, processing the data, visualizing the data and trying out the different methodology and algorithm
- Both the old version of Python and the new version of Python are supported, giving a very good backward compatibility of some old Python codes developed beforehand.
- Although some other users mentioned the installation is "simple", we did encounter some challenge in a highly controlled environment (due to security reasons).
- Jupyter Notebook is extremely slow when the client/server side of the network's speed/bandwidth is not balanced.
- Bootstrapping Anaconda takes too long, sometimes I even started doubting it would respond any more.
- If there are extra python packages you need but are not by default installed by Anaconda, then some efforts will be required to figure out how to put them in the right place.
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.
- Handle different environments with different versions of python and its libraries. This is a handy feature because some tools like PSSE run only with Python 2.7.
- Anaconda preinstalls the most useful libraries and packages.
- It's a little slow at startup. If it were a little faster, that would add significantly to the experience.
January 22, 2020
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.
- Contains every fundamental package about data analytics and machine learning.
- It is very easy to install further packages.
- It's great that it contains a lot of stuff but it is very slow to boot and is a heavy product.
February 28, 2020
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.
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Anaconda Scorecard Summary
Feature Scorecard Summary
What is Anaconda?
Anaconda is an open source Python distribution / data discovery & analytics platform.
Anaconda Video
Anaconda Introduction
Anaconda Pricing
- Does not have featureFree Trial Available?No
- Does not have featureFree or Freemium Version Available?No
- Does not have featurePremium Consulting/Integration Services Available?No
- Entry-level set up fee?No
| Edition | Pricing Details | Terms |
|---|---|---|
| Commercial Edition | 14.95 | per month |
| Team Edition | 10,000 | |
| Enterprise Edition | Contact for quote |
Anaconda Technical Details
| Deployment Types: | SaaS |
|---|---|
| Operating Systems: | Unspecified |
| Mobile Application: | No |
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Reviewers rate Multiple Model Development Languages and Tools highest, with a score of 8.9.
Who uses Anaconda?
The most common users of Anaconda are from Enterprises and the Information Technology & Services industry.