In this project, I utilized APIs to geocode data, subsequently fetching FIPS codes based on coordinates. This was a key component in my research. Furthermore, I'll walk you through the process of creating heat maps with Python using the obtained coordinates.

Visualization is essential in understanding county-level variables. In this project, I used Python to make detailed maps that enable users to visually inspect data and identify geographic patterns with ease.

While working with data, we often encounter the need to merge datasets without a unique and precise key. In this example, I'll introduce you to the concept of record linkage (sometimes called probabilistic matching or fuzzy matching). Utilizing Python and machine learning, I show how to train models on pre-linked data, allowing for the matching of datasets with missing keys. This project underscores the power of ML and record linkage libraries.

Data analysis often requires retrieving information from large datasets. These datasets are frequently accessed through a SQL dialect, which is known for its efficiency and organizational superiority.  The tutorial provides an example of how to use Python for accessing and creating SQL tables. It also offers a concise introduction to connecting a SQL dataset with PowerBI and creating a basic dashboard that visually represents the processed data. This project showcases the essential steps of data retrieval, processing, and visualization using SQL, Python, and PowerBI.