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- Data Transformations (26)8.989%
- Extend Existing Data Sources (24)8.787%
- Visualization (25)8.787%
- Interactive Data Analysis (24)8.787%
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Entry-level set up fee?
- No setup fee
- Free Trial
- Free/Freemium Version
- Premium Consulting / Integration Services
- 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.
- Easy to use
- Supports multiple environments
- All kinds of data science libraries found easily
- Doesn't stop development [on] the ML project
- Debugging of code is good feature.
- Library installation is easy in Anaconda.
- Memory management of files in Anaconda.
- 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.
- 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.
- Different environments.
- Open source.
- Jupyter and Spyder
- Package/Modules installation
- Statistical Programming using R
- Data Visualization using Orange
- 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.
- Ease of use
- Its automatically install the main library
- It has the tool like numpy, pandas for the data visualization.
- Applications, libraries, and concepts designed for the development of data science
- Automatic installation of the main packages
- It has tools such as Numpy, Pandas, and Numba to analyze data and allow you to view data with Bokeh, Datashader, Holoviews, or Matplotlib
- Different code editors like Spyder, Jupyter Lab, R studio.
- Centralized package management for projects.
- Inbuilt packages save time for installation.
- Support for Python and R programming language
- Easy to build projects using multiple libraries at single place
- very easy installation
- easy to manage packages
- the default packages installed has most of the required stuff our data scientists needs
- Jupiter Notebook
- large amount of scienetific libraries
- fast & easy to install