Enterprise Database Systems
Data Visualization with Bokeh and Plotly
Data Visualization: Building Interactive Visualizations with Bokeh
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Data Visualization: Getting Started with Plotly
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Data Visualization: More Specialized Visualizations in Bokeh
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Data Visualization: Visualizing Data Using Advanced Charts in Plotly
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Data Visualization: Building Interactive Visualizations with Bokeh
Lesson Objectives
Data Visualization: Building Interactive Visualizations with Bokeh
- discover the key concepts covered in this course
- use Jupyter notebooks to install and import Bokeh
- create a Bokeh chart and save it in PNG and HTML formats
- display your visualization inline in a Jupyter notebook
- visualize data using a bar chart
- visualize data using a stacked bar chart
- represent data using a clustered bar chart
- visualize proportions in data using pie charts
- visualize proportions in data using donut charts
- summarize the key concepts covered in this course
Overview/Description
An interactive visualization library, Bokeh allows users to create diverse graphics and highly interactive dashboards and data applications. In this course, you'll achieve a foundational knowledge of using Bokeh to build simple graphs and visualizations. You'll start by exploring how to install Bokeh on your local machine, display charts inline within your Jupyter notebooks, and create an interactive visualization. You'll then recognize how to save Bokeh charts as HTML and PNG files. Next, you'll investigate how to visualize categorical data using bar charts, stacked bar charts, and clustered bar charts. You'll also identify how to implement pie charts and donut charts to represent compositions in your data. You'll finish the course by examining the ease of interactivity and granular customizations that Bokeh offers.
An interactive visualization library, Bokeh allows users to create diverse graphics and highly interactive dashboards and data applications. In this course, you'll achieve a foundational knowledge of using Bokeh to build simple graphs and visualizations. You'll start by exploring how to install Bokeh on your local machine, display charts inline within your Jupyter notebooks, and create an interactive visualization. You'll then recognize how to save Bokeh charts as HTML and PNG files. Next, you'll investigate how to visualize categorical data using bar charts, stacked bar charts, and clustered bar charts. You'll also identify how to implement pie charts and donut charts to represent compositions in your data. You'll finish the course by examining the ease of interactivity and granular customizations that Bokeh offers.
Target
Prerequisites: none
Data Visualization: Getting Started with Plotly
Lesson Objectives
Data Visualization: Getting Started with Plotly
- discover the key concepts covered in this course
- install and import Plotly using Jupyter notebooks
- identify the components of a Plotly graph
- visualize statistical data using box-and-whisker plots
- visualize categorical data using box plots and strip plots
- create colored and notched box plots
- recognize how to plot financial data using candlestick charts
- use funnel charts to visualize sequential data
- summarize the key concepts covered in this course
Overview/Description
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.
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.
Target
Prerequisites: none
Data Visualization: More Specialized Visualizations in Bokeh
Lesson Objectives
Data Visualization: More Specialized Visualizations in Bokeh
- discover the key concepts covered in this course
- visualize data using a scatter plot
- interpret relationships in data using scatter plots
- recognize how to configure scatter plots
- use a heatmap to represent relationships between variables
- visualize the trend of variables over time using line charts
- create a multi-line line chart and an area chart
- identify relationships between entities using a network chart
- summarize the key concepts covered in this course
Overview/Description
Bokeh facilitates the creation of high-performance charts allowing users to build impactful web-based dashboards and applications. In this course, you'll investigate how to visualize your data using complex charts in Bokeh. First, you'll identify how to visualize relationships that exist in your data using scatter plots, discover the function of jitter in viewing individual data points, and configure scatter plots where both axes represent continuous values. Next, you'll outline how to represent relationships between pairs of variables using heatmaps. You'll then recognize the use of line and area charts to visualize time-series data. Finally, you'll explore how to visualize data structures in the form of nodes and edges using network graphs. When you have completed this course, you'll possess the skills and knowledge to build simple as well as complex interactive visualizations using Bokeh.
Bokeh facilitates the creation of high-performance charts allowing users to build impactful web-based dashboards and applications. In this course, you'll investigate how to visualize your data using complex charts in Bokeh. First, you'll identify how to visualize relationships that exist in your data using scatter plots, discover the function of jitter in viewing individual data points, and configure scatter plots where both axes represent continuous values. Next, you'll outline how to represent relationships between pairs of variables using heatmaps. You'll then recognize the use of line and area charts to visualize time-series data. Finally, you'll explore how to visualize data structures in the form of nodes and edges using network graphs. When you have completed this course, you'll possess the skills and knowledge to build simple as well as complex interactive visualizations using Bokeh.
Target
Prerequisites: none
Data Visualization: Visualizing Data Using Advanced Charts in Plotly
Lesson Objectives
Data Visualization: Visualizing Data Using Advanced Charts in Plotly
- discover the key concepts covered in this course
- compare values belonging to different categories using radar charts
- represent continuous data across categories using radar charts
- visualize hierarchical categories using sunburst charts
- create a Gantt chart to visualize schedules and timelines
- visualize the flow of data using a Sankey diagram
- create a Sankey diagram to visualize data
- create maps to visualize geographical data
- summarize the key concepts covered in this course
Overview/Description
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.
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.
Target
Prerequisites: none
