In this course, we will explore the intricacies of analyzing time, a task that can often be perplexing. While a time machine may be out of reach, we can equip ourselves with a machine for analyzing time. When time becomes a factor in analysis, complexities arise, such as navigating day and month boundaries, time zones, and daylight saving time. To effectively tackle these challenges, Python is the ideal tool. Throughout this course, we will delve into the realm of time analysis using Python. By working with diverse data sets, including information on hurricanes and bike trips, we will learn how to count events, calculate time intervals between events, and visualize data over time. Our exploration will encompass both standard Python and the powerful Pandas library. Additionally, we will touch upon the dateutil library, the sole timezone library endorsed by the official Python documentation. Upon completion of this course, you will possess the skills and confidence to proficiently handle date and time data in any format. With your newfound expertise, you will navigate the complexities of time analysis like a true champion.