When dealing with large datasets for machine learning, have you ever come across an address column that looks like this? Location data can be very messy and difficult to process. It is difficult to encode addresses, since they are of very high cardinality. If you try to encode a column like this with a technique like one-hot encoding, it will lead to high dimensionality, and your machine learning model might not perform well. The easiest way to overcome this problem is to geocode these columns. Geocoding is the process of converting addresses into geographical coordinates.

This course will introduce students to the basics of the Structured Query Language (SQL) as well as basic database design for storing data as part of a multi-step data gathering, analysis, and processing effort. The course will use SQLite3 as its database. We will also build web crawlers and multi-step data gathering and visualization processes. We will use the D3.js library to do basic data visualization. This course will cover Chapters 14-15 of the book "Python for Everybody".