Item: Jupyter Notebook
Rating: 10
Author: Rita Lo

Use Cases and Deployment Scope

Jupyter notebooks are widely used by our Science and Analytics departments to analyze data, make forecasts, clean/wrangle data, graph visualizations, create machine learning models, and perform a suite of analyses to best understand our business landscape.

Pros and Cons

Sharing/showcasing work in a step by step manner
Exploratory data analysis/viewing code in-line
Data exploration/visualization
Switch between different coding languages

No IDE integration/linting
No testing integrations
Difficult to view changes in GitHub
Notebook harder to productionize than scripts

Most Important Features

Data Visualization
Machine Learning
Statistical Modeling

Return on Investment

Positive understanding of where to invest next
Greater exposure to current business trends and forecasts
Pinpoint market leaders/laggers

Alternatives Considered

PyCharm

Jupyter is still the most well known and widely used platform I've seen. Using it over other competition like Zeppelin simply because of its availability, and my familiarity with its functionality.

Key Insights

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?

Yes

Did implementation of Jupyter Notebook go as expected?

I wasn't involved with the implementation phase

Would you buy Jupyter Notebook again?

Yes

Other Software Used

Jira Software, Mode Analytics, Sublime Text

Likelihood to Recommend

Jupyter notebooks are great for data science, especially if you want to clean and transform data, and explore outcomes/visualize/model in real-time. Once you have a successful logic built out, though, it's best to move the code away from a notebook for production.

Jupyter Pros and Cons

Overall Satisfaction with Jupyter Notebook