Overall Satisfaction with Anaconda
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.
- Anaconda has more than 1500 packages for Python/R.
- UniqueeEnvironment setup.
- Collecting data from the source.
- Building machine learning models for ML and deep learning models with Sci-kit-learn or TensorFlow.
- Anaconda has really good impact in market.
- Lead in the market.
- Provides one stop solution.
On top of all the software that I have used, Anaconda is the best because in Anaconda we have built-in packages that provide no headache to install packages and we can design a separate environment for different projects. Anaconda has versions made for special use cases. Anaconda is designed for Python developers who need distribution supported by a commercial provider and corporate sponsorship programs. Major cases of Anaconda Python usage statistics, statistics, engineering, data analysis, machine learning, and related applications. Another advantage is the way Anaconda handles items from outside the Python ecosystem when they are prioritized in a particular package. Conda packages, designed specifically for Anaconda, address the installation of Python packages and external software requirements.
Do you think Anaconda delivers good value for the price?
Are you happy with Anaconda's feature set?
Did Anaconda live up to sales and marketing promises?
Did implementation of Anaconda go as expected?
Would you buy Anaconda again?
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.