Plotly is Python's browser-based graphing library, which provides users with online graphing, analytics, and statistics tools. In this course, you'll explore how to use Plotly's declarative APIs to build interactive graphs and visualizations. You'll start this course by getting familiar with the components of the Plotly library. You'll identify the role of the high-level library (plotly.express) in creating visualizations and the low-level library (plotly.graph_objects) in creating granular customizations of your charts. Next, you'll investigate the use of box plots in visualizing the statistical properties of a continuous data series. You'll also discover how to represent additional categorical data by creating separate box plots and customizing their color. Finally, you'll examine how to implement a candlestick chart to reflect the trend of stock price performance over a period of time and visualize sequential data in a linear process using funnel charts.

Using data visualizations during exploratory data analysis is an important part of the data science process. The Plotly graphing library helps with this by allowing users to investigate their data through interactive charts. In this course, you will explore the construction and applications of advanced charts in Plotly for varied use cases. You'll begin by identifying how to present multi-dimensional ordinal data along different axes using radar charts. You'll then recognize how to use sunburst charts to visualize multi-level hierarchical data. Next, you'll implement Gantt charts to visualize schedules and timelines in a project and then move on to exploring how to represent the flow of data between entities using Sankey diagrams. You'll finish the course by investigating the use of geo plots and choropleth maps in visualizing geographical data and plot locations.

This course serves as an introduction to Plotly's Chart Studio - a handy visualization tool that supports various purposes. You'll start by covering the basics, creating or loading a dataset into a grid. You'll then use that data to generate a chart. Following from there, you'll practice loading different data source types into Chart Studio, such as CSV files and SQL query results. You'll then create basic visualizations, including a bar graph and a table, and style them to suit your aesthetic requirements and, more crucially, to accurately convey the underlying information in your dataset. Lastly, you'll delve into how to set up a single plot to include multiple charts, allowing users to analyze data along various dimensions.

This course's focus is on working with the variety of charts available in Plotly Chart Studio - each of which has its use case in terms of the kind of information it conveys best. Upon completion, you'll recognize which chart to choose to best present information the underlying data. You'll begin by working with pie charts, a great way to project the proportion of values represented by individual categories. You'll then work with scatter plots to visualize relationships between two variables, line charts to analyze sequential data, and histograms to understand a distribution of values. While exploring all of these charts, you'll cover several features that make plots useful and exciting, such as using multiple charts within subplots, adding annotations, and configuring the chart axes.