Summary

In this chapter, we covered the basics of data science and explored the process of extracting underlying information from data using scientific methods, processes, and algorithms. We then moved on to data pre-processing, which includes data cleaning, data integration, data transformation, and data discretization.

We saw how pre-processed data is split into train and test sets when building a model using a machine learning algorithm. We also covered supervised, unsupervised, and reinforcement learning algorithms.

Lastly, we went over the different metrics, including confusion matrices, precision, recall, and accuracy.

In the next chapter, we will cover data visualization.