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Anaconda: A data scientist's best friend
February 28, 2020

Anaconda: A data scientist's best friend

Anonymous | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User

Overall Satisfaction with Anaconda

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.
  • Virtual environment
  • Version control
  • One spot for data science tools
  • Increase efficiency
  • Improve cooperation
  • Reduce debugging time
Anaconda is very strong in the environment and version control that make data science work much easier. The only thing that might be comparable to Anaconda would be using Kubernetes to control Docker. Another potential improvement would be replacing spyder with PyCharm and Atom in the software selection under Anaconda navigation.
Actually the cheatsheet of Anaconda is comprehensive enough for any users at any level to use. You can easily find the support that you need by searching through the document, from creating environment to removing environment, from exporting current environment into yaml file to installing different version packages or Python.

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

Anaconda is the best data science version control tool in the market. With Anaconda, you can easily create, remove, and switch environments to run different scripts. What is more, you can also use it to export the current environment automatically into yaml file that can be used to recreate the same environment.

Anaconda Feature Ratings

9.0
Connect to Multiple Data Sources
9
Extend Existing Data Sources
10
Automatic Data Format Detection
8
MDM Integration
9
8.0
Visualization
7
Interactive Data Analysis
9
9.0
Interactive Data Cleaning and Enrichment
9
Data Transformations
9
Data Encryption
9
Built-in Processors
9
9.3
Multiple Model Development Languages and Tools
10
Automated Machine Learning
8
Single platform for multiple model development
10
Self-Service Model Delivery
9
10.0
Flexible Model Publishing Options
10
Security, Governance, and Cost Controls
10