Best IDE for Data Science Projects
October 07, 2021

Best IDE for Data Science Projects

Zayed Rais | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User

Overall Satisfaction with Anaconda

Anaconda is the best tool for the data scientist to [develop] the machine learning project [under] a single umbrella. It is used [throughout] the whole organization. We are using the Anaconda for Python [and] R to do the data science activities end-end process, i.e. importing the statistical/ML/Visualization libraries to train and visualize the data/reports.

Pros

  • Almost all required libraries are available in it.
  • Easy to create a notebook for a data science project.
  • [It is] flexible to work on multiple Python environments based on your requirements.
  • In [the] community, [it is] easy to find the forum [and] events.

Cons

  • [The] application [takes a lot of] time to load the first time.
  • Sometimes, it [stops working because it] consumes more ram.
  • [I would like it to] add some ready-made use case environments.
  • Supports multiple environments
  • All kinds of data science libraries found easily
  • Doesn't stop development [on] the ML project
  • Anaconda is [a] leading platform in [the] data science industry.
  • It [has] good impact [across my] organization.
  • [It] provides all tools [under a] single umbrella.
In Anaconda, [it is easy] to find and install the required libraries. Here, we can work on multiple projects with different sets of the environment. [It is] easy to create the notebook for developing the ML model and deployment. Right now, it is the best data science version control tool in the IT software market.

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 well suited for data science projects. If you are working with multiple projects, it [is] easy to build different environments for the requirements of the project. Easy interaction with [the] notebook for data collection, pre-processing, transforming, training, and visualizing. Sometimes, we are unable to update the libraries due to some security patches.

Anaconda Feature Ratings

7.8
Connect to Multiple Data Sources
8
Extend Existing Data Sources
9
Automatic Data Format Detection
7
MDM Integration
7
7.5
Visualization
8
Interactive Data Analysis
7
7.5
Interactive Data Cleaning and Enrichment
8
Data Transformations
8
Data Encryption
7
Built-in Processors
7
7.3
Multiple Model Development Languages and Tools
7
Automated Machine Learning
7
Single platform for multiple model development
8
Self-Service Model Delivery
7
7.0
Flexible Model Publishing Options
7
Security, Governance, and Cost Controls
7

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