Data Analysis with Python

What this book covers

The book contains two logical parts of roughly equal length. In the first half, I lay down the theme of the book which is the need to bridge the gap between data science and engineering, including in-depth details about the Jupyter + PixieDust solution I'm proposing. The second half is dedicated to applying what we learned in the first half, to four industry cases.

Chapter 1, Programming and Data Science – A New Toolset, I attempt to provide a definition of data science through the prism of my own experience, building a data pipeline that performs sentiment analysis on Twitter posts. I defend the idea that it is a team sport and that most often, silos exist between the data science and engineering teams that cause unnecessary ...