Python Tutorial: Experimental Design in Python | Intro

Want to learn more? Take the full course at https://learn.datacamp.com/courses/experimental-design-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hello! My name is Luke Hayden, and in this course we're going to learn how to use data to answer questions and get reliable answers. We can use data to answer questions. Does a drug work? Is one variable correlated with another? This course will teach you skills to help answer such questions. However, to get reliable answers we will need to think scientifically, considering different groups and different experiments and making valid comparisons. This course will teach you how to understand the factors at work. We will learn a set of rigorous methods, including a number of statistical tests. First, we need to know what questions to ask. Here, exploratory data analysis is very useful. For example, we can build graphs that make us wonder about whether our data contains a trend and then examine these with statistical tests. We can encounter many types of variables and one way to classify them is as either discrete or continuous. A discrete variable can have a finite set of possible values, for example, a binary true or false. A continuous variable can have an infinite number of values, like a measurement. When plotting, we map these variables to different aspects of the plot. We can assign a variable to the X or Y axis, or we can change the color using the fill or color arguments, depending on the variable. In this course, we'll be using the plotnine package to create plots. Plotnine uses a "grammar of graphics" approach, which makes it really easy and intuitive to use. We start with a pandas DataFrame, containing at least one variable of interest, and pass it to the ggplot() function. Then, we define what aspects of the plot will correspond to which variables in the DataFrame with the aes() function. Finally, we specify a geometry, wh