Chapter 2. Python and Jupyter Notebooks to Power your Data Analysis
"The Best Line of Code is the One You Didn't Have to Write!"
– Unknown
In the previous chapter, I gave a developer's perspective on data science based on real experience and discussed three strategic pillars required for successful deployment with in the enterprise: data, services, and tools. I also discussed the idea that data science is not only the sole purview of data scientists, but rather a team sport with a special role for developers.
In this chapter, I'll introduce a solution—based on Jupyter Notebooks, Python, and the PixieDust open source library—that focuses on three simple goals:
- Democratizing data science by lowering the barrier to entry for non-data scientists
- Increasing collaboration between developers and data scientists
- Making it easier to operationalize data science analytics
Note
This solution only focuses on the tools pillar and...
