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
