Book Image

Python Data Science Essentials

By : Alberto Boschetti, Luca Massaron
Book Image

Python Data Science Essentials

By: Alberto Boschetti, Luca Massaron

Overview of this book

The book starts by introducing you to setting up your essential data science toolbox. Then it will guide you across all the data munging and preprocessing phases. This will be done in a manner that explains all the core data science activities related to loading data, transforming and fixing it for analysis, as well as exploring and processing it. Finally, it will complete the overview by presenting you with the main machine learning algorithms, the graph analysis technicalities, and all the visualization instruments that can make your life easier in presenting your results. In this walkthrough, structured as a data science project, you will always be accompanied by clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.
Table of Contents (13 chapters)

Chapter 3. The Data Science Pipeline

Until now, we explored how to load data into Python and process it up to a point to create a dataset as a bidimensional NumPy array of numeric values. At this point, we are ready to get fully immersed into data science and extract meaning from data and potential data products. This chapter and the next chapter on machine learning are the most challenging sections of the entire book.

In this chapter, you will learn how to:

  • Briefly explore data and create new features

  • Reduce the dimensionality of data

  • Spot and treat outliers

  • Decide on the score or loss metrics that are the best for your project

  • Apply the scientific methodology and effectively test the performance of your machine learning hypothesis

  • Select the best feature set

  • Optimize your learning parameters