Plotly for Data Visualization: Advanced Charts & Features in Chart Studio

Plotly for Data Visualization: Advanced Charts & Features in Chart Studio

• discover the key concepts covered in this course
• visualize the distribution of numeric values in a field by rendering a box plot using Plotly
• analyze the detailed distribution of numeric values in a field by creating a violin plot in Plotly
• share your Plotly Chart Studio plots and data with viewers and collaborators
• plot the variations in price of a tradeable asset using candlestick charts in Plotly
• convey details about geographical regions using an atlas map in Plotly
• use Plotly to render information about geographical regions by shading regions based on the value of a variable
• analyze the stages of a sales cycle by generating a funnel chart in Plotly
• use Plotly to visualize the proportional contribution of various categories that have a hierarchical relationship
• combine related plots into a single page using a dashboard in Plotly
• use Plotly to plot the relationship between two variables in your data by generating a fitted curve to map the relationship
• use Plotly to analyze data over a long term by plotting a moving average curve
• create Plotly Chart Studio plots from a Python app and upload them to your account on the cloud
• define Plotly Chart Studio plots from a Python app and save them on your local file system
• summarize the key concepts covered in this course

There are many specialized charts and other advanced features available in Plotly Chart Studio, which can be used to give life to your data. These include Box and Violin plots to analyze the distribution of values, Candlestick charts to analyze tradeable assets' performance, and map charts to visualize geographical data.

In this course, you'll learn how to work with these chart types, as well as funnel, treemaps, and sunburst charts. You'll also practice sharing plots and charts with collaborators and viewers.

Beyond this, you'll learn how to create a dashboard to group related charts. You'll generate curves, namely a moving average and a fitted curve, to augment some of your charts. You'll then finish my writing some Python code and using it to generate charts from a Python application.

Plotly for Data Visualization: An Introduction to Plotly Chart Studio

Plotly for Data Visualization: An Introduction to Plotly Chart Studio

• discover the key concepts covered in this course
• log in to the Plotly Chart Studio web console and create a new data grid
• visualize preexisting data in a Chart Studio data grid in the form of a bar chart
• recognize the different data formats that can be loaded into Chart Studio
• use the Falcon client to integrate Chart Studio with the results of a query against a relational database
• load one of the example charts in Chart Studio in order to recognize the features available to visualize data
• define a Chart Studio table using data and formatting information set in a file
• create and configure a bar chart in Chart Studio
• define a plot that includes multiple traces in order to convey multiple series of data
• summarize the key concepts covered in this course

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.

Plotly for Data Visualization: Exploring Chart Studio Visualizations

Plotly for Data Visualization: Exploring Chart Studio Visualizations

• discover the key concepts covered in this course
• render a pie chart to analyze the proportion of contributions from various categories
• create a plot with two charts by rendering them within subplots
• view the relationship between two variables in your dataset by plotting them in a scatter plot
• configure the axes of a plot to make them more prominent and make your charts easier to read
• represent multiple dimensions of data in your scatter plot by configuring the points that are rendered
• apply transform operations on your dataset in order to perform an aggregation
• visualize sequential data using a line chart
• augment your chart by including an annotation to convey additional information
• view the distribution of values in a field by plotting a histogram
• summarize the key concepts covered in this course

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.