Python Tutorial : Activity of zebrafish and melatonin

Want to learn more? Take the full course at https://learn.datacamp.com/courses/case-studies-in-statistical-thinking at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hello, and welcome to the course! My name is Justin Bois, and I'm a lecturer in the Division of Biology and Biological Engineering at Caltech. My goal for you in this course is to hone and extend your statistical thinking skills by working through real data sets. Before we dive into the two main case studies, it is important to review what you learned in Statistical Thinking I and II. I thought a great way to do that would be to play around with a couple of data sets from my colleagues in the biological sciences at Caltech. The first data set we will practice with comes from the lab of David Prober, a leading expert on sleep. In this study, the researchers in Prof. Prober's lab studied the activity of zebrafish larvae. Each fish was put in its own little well and recorded with a camera. Whenever a fish moves, the system detects and records the movement, indicated here by the red flashes in the video. The more movement, the more wakeful the fish. These fish are interesting because some of them have a mutation in a gene involved in producing melatonin, an important hormone for sleep regulation. Fish that have the mutation are called mutants, and those that do not are called wild type. If we look at the mean activity of the fish over time, we see that compared to wild type the mutant fish are more active at night, which is indicated by the gray regions on the plot. Our goal with this warm-up analysis is to quantify the effect of this mutation on wakefulness. In the exercises, you will use nighttime active bouts as a metric for wakefulness of the fish. An active bout is a period of time where a fish is consistently active. The length of an active bout is the number of consecutive minutes that a fish is active. This is enough background