All in one workspace for Data Sciences
May 01, 2021
All in one workspace for Data Sciences
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.
Do you think Jupyter Notebook delivers good value for the price?
Yes
Are you happy with Jupyter Notebook's feature set?
Yes
Did Jupyter Notebook live up to sales and marketing promises?
I wasn't involved with the selection/purchase process
Did implementation of Jupyter Notebook go as expected?
Yes
Would you buy Jupyter Notebook again?
Yes