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The Step After Notebooks

The quality of an analysis is measured by impact. Analytics exists to solve client problems, and with the volume of client data continuing to explode, data science represents a vital opportunity for impact. People realize this: IBM forecasts that there will be almost 3,000,000 data science jobs by the end of this year. Unfortunately, data science is a complex domain, and what I’ll call notebook-driven development, the popular technique of conducting an analysis primarily in Jupyter notebooks, does little to manage that complexity.