Matplotlib is one of the most popular visualization libraries used by data analysts and data scientists working in Python, but can often be intimidating to use. This course serves to make working with Matplotlib easy and simple.
This course will focus on making Matplotlib accessible and easily understandable to a Data Scientist or Business Analyst who needs to quickly and visually come to grips with relationships in a large dataset. In this course, Building Data Visualizations Using Matplotlib, you'll discover the basic components which make up a plot and see how you can tweak parameters and attributes to have the visualizations customized to exactly how you want it. First, you’ll grow to understand the basic APIs available in Matplotlib and where they are used and learn how to customize the display, colors, and other attributes of these plots which will have multiple axes. Next, you’ll build intermediate and advanced plots, drawing shapes and Bezier curves, using text and annotations to highlight plot elements, and normalizing the scales that are used on the x and y-axis. Lastly, you’ll use some real-world data to visualize statistical data such as mean, median mode, and outliers, cover box plots, violin plots, histograms, pie charts, stem and stack plots and autocorrelations graphs. By the end of this course you’ll not only have explored all the nitty gritty that Matplotlib has to offer; but you’ll also be capable of building production-ready visuals to embed with your UI or to display within reports and presentations.
A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.
Course Overview (Music playing) Hi, my name is Janani Ravi and welcome to this course on Building Data Visualizations Using Matplotlib. A little about myself, I have a master's in Electrical Engineering from Stanford, and have worked at companies such as Microsoft, Google, and Flipkart. At Google I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high quality video content. In this course, we'll understand the basic components which make up a plot, and see how we can tweak parameters and attributes to have the visualizations customized to exactly how we want them to be. We start off by understanding the basic APIs available in Matplotlib and where they are used. We'll build basic plots and learn how to customize the display colors and other attributes of these plots. We'll work with plots which have multiple axes and also with watermarks. We'll then move onto building intermediate and advanced plots, drawing shapes and Bezier curves, using text and other annotations to highlight plot elements, and normalizing this case that we use on the X as well as the Y axis. We'll then use some real world data to visualize statistical data, such as mean, median, mode, and outliers in our data. We'll cover box plots, violin plots, histograms, pie charts, stem and stat plots, and autocorrelation graphs. This course will allow you to explore all of the nitty-gritty that Matplotlib has to offer, so you can build production-ready visuals who embed with your UI or to display within reports and presentations.