Overall Satisfaction with 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.
- 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
- 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
- Positive: Lower maintenance cost compared to other tools on the market
- Positive: Ease in hiring professionals already accustomed to the tool in the job market
- Positive: Projects are portable, allowing you to share projects with others and execute projects on different platforms, reducing deployment costs
Some analyzed tools, such as PyCharm and Spyder, are simpler to use but still do not have all the libraries needed for those starting out in data science--or in institutions that need to grow in that direction. Anaconda is more robust but stable, more complete, and the usability is very good for professionals. Anaconda is also more popular and user groups often exchange information and codes generated in Anaconda. This makes it easier to find information, other libraries, and learning in general for companies that are starting their data science processes.
Do you think Anaconda delivers good value for the price?
Yes
Are you happy with Anaconda's feature set?
Yes
Did Anaconda live up to sales and marketing promises?
Yes
Did implementation of Anaconda go as expected?
Yes
Would you buy Anaconda again?
Yes