Data Visualization
Explore how to create insightful data visualizations using Python libraries such as Matplotlib, Seaborn, Bokeh, and Missingno. This lesson helps you understand how to visualize data patterns and missing data, enabling clear communication of project results to technical and non-technical audiences alike.
We'll cover the following...
In the early stages of a project, creating visualizations helps with Exploratory Data Analysis (EDA). Visualizations allow us to gain insights into our data by making things easier to understand. When we place data into a visual context patterns, trends, and correlations that might have otherwise gone undetected rise to the surface.
In the middle stages of a project, we can use visualizations to gain more insights and generalize model results.
Towards the end of a project, it’s very important to be able to present our results in a clear, concise, and compelling manner so that our audience (which is often made up of non-technical people) can understand the results.
Matplotlib
Matplotlib is a Python library that helps with data visualization. It’s a 2D plotting library which produces different types of figures. It can be used ...