Data Science and Anaconda
Updated May 16, 2021

Data Science and Anaconda

Fernanda Ministerio | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User

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?


Are you happy with Anaconda's feature set?


Did Anaconda live up to sales and marketing promises?


Did implementation of Anaconda go as expected?


Would you buy Anaconda again?


When choosing Python or R for software development, you choose a large language ecosystem with a wide variety of packages covering all programming needs. But in addition to libraries for everything from GUI development to machine learning, you can also choose from a variety of tool runtimes and their libraries; some runtimes may be more suited to the use case you have at hand than others.

Anaconda has versions optimized for special use cases. Anaconda was designed for Python developers who need a distribution supported by a commercial provider and with support plans for companies. The main use cases for Anaconda Python are mathematics, statistics, engineering, data analysis, machine learning, and related applications.

Anaconda groups together many of the most common libraries for commercial and scientific work in Python--SciPy, NumPy, Numba, and so on--and makes it much more personalized through a package management system.

Anaconda stands out from the other distributions for the way it integrates all these pieces. When installed, Anaconda offers a desktop application--Anaconda Navigator--that makes all aspects of the Anaconda environment available through a convenient user interface. Finding components, customizing them, and working with them is much easier with Anaconda than with CPython.

Another benefit is the way Anaconda handles components from outside the Python ecosystem, if they are prioritized for a specific package. Conda conda packages, created specifically for Anaconda, deal with the installation of Python packages and external third-party software requirements.

Since Anaconda includes so many useful libraries and can install even more with just a few keys, the size of an Anaconda installation can be much larger than that of other competitors. This can be an issue in situations where you have resource constraints.

Anaconda Feature Ratings

Connect to Multiple Data Sources
Extend Existing Data Sources
Automatic Data Format Detection
MDM Integration
Not Rated
Interactive Data Analysis
Interactive Data Cleaning and Enrichment
Data Transformations
Data Encryption
Built-in Processors
Multiple Model Development Languages and Tools
Automated Machine Learning
Single platform for multiple model development
Self-Service Model Delivery
Flexible Model Publishing Options
Security, Governance, and Cost Controls


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