All in one workspace for Data Sciences
May 01, 2021

All in one workspace for Data Sciences

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

Overall Satisfaction with Jupyter Notebook

We currently use Juptyer Notebooks across our organization. Jupyter Notebooks are our go-to experimenting environments for data pre-processing, creation, training and evaluation of machine learning and deep learning models. We also heavily use Jupyter for data visualization and exploratory data analysis. It also provides a great interactive interface which can be used for story telling to our clients and consumers.
  • Easy and interactive Python environment.
  • Latex markdown for explanations.
  • Terminal access through cell itself.
  • Fast Intellisense.
  • Documentation access through cell commands.
  • Intuitive Key Bindings.
  • Creating and installing a virtual environment can be tricky.
  • Conda environment can be granular to work with.
  • Intellisense (Code Completion).
  • Terminal Access through cell interface.
  • Support for virtual environments.
  • All in one solution for planning to development.
  • Positive impact if using local notebook servers.
  • Presenting notebooks to clients with markdowns for better understanding helps positively.
An interesting thing is that Jupyter Notebook is run on browser environments which may or may not be a positive feature according to cases. VS Code on [the] other hand doesn't use any interface and can run Jupyter Notebooks too. Sometimes my browser consumes too much RAM due to which I shifted to VS Code for development purposes.

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Are you happy with Jupyter Notebook's feature set?


Did Jupyter Notebook live up to sales and marketing promises?

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Did implementation of Jupyter Notebook go as expected?


Would you buy Jupyter Notebook again?


If you want to do exploratory data analysis, ML and DL model training, and evaluation with data pre-processing then Jupyter Notebook is the best. It has a good community support as well. If you want to develop Python API or scripting or backend development, other open source code editors are a better fit.

Jupyter Notebook Feature Ratings

Connect to Multiple Data Sources
Extend Existing Data Sources
Interactive Data Analysis
Interactive Data Cleaning and Enrichment
Data Transformations
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