Item: Anaconda
Rating: 10
Author: Daniel Blazquez

Overall Satisfaction with Anaconda

Use Cases and Deployment Scope

As part of an overall strategic assessment, we decided to test the possibilities of several data lakes by conducting a collection of data science experiments. Anaconda was instrumental to get the data science experiments up and running in record time, including testing different data analysis packages, generation of notebooks, and the sharing of the results to a larger team.

Pros and Cons

Pros

Integration of the most popular and useful Python packages
Managing multiple execution environments
Management of package dependencies

Cons

Easier migration to cloud sharing

Return on Investment

Extremely quick turnaround time to set up data science experiments
Reduction of troubleshooting time when deploying new packages and dependencies
Low risk environment due to the community edition

Other Software Used

Asana, Zoho Books, Eclipse

Likelihood to Recommend

Anaconda shines if you need to set up a data analysis or data science lab in no time. Newcomers to Python or Jupiter can be up and running in minutes and playing with the most popular packages. I think the Anaconda Cloud package could benefit from some UX improvements to clarify the migration process. Integration with external data sources could be improved as well.

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Data science experiments in no time