In the course Advanced Data Wrangling on Udacity, you will learn the essential steps of the data wrangling process, which include gathering, assessing, and cleaning data. The gathering phase involves extracting data from various sources such as file archives, text files, APIs, JSON files, HTML files, and SQL databases. You will also learn how to identify different file formats that data analysts might encounter. In the assessing phase, you will distinguish between dirty data and messy data, identify data quality issues, and assess data quality visually and programmatically using pandas. Additionally, you will strategize about data structuring for analytical datasets and assess data structure visually and using pandas. The cleaning phase focuses on defining, coding, and testing data cleaning tasks based on assessment findings. You will clean the data using Python and test the cleaning code visually and programmatically. Finally, you will store the cleaned data using flat files. Throughout the course, you will work on a course project where you will apply your data wrangling skills to gather data from various sources, assess its quality and structure, and clean it using Python. You will document your wrangling efforts in a Jupyter Notebook and showcase your analyses and visualizations using Python and its libraries.