Course: Data visualisation with Bokeh and Plotly

$109.00
$131.89 incl. vat
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duration: 7 hours |

Language: English (US) |

access duration: 90 days |

In Onbeperkt Leren

Details

Data visualisation with Bokeh and Plotly. In this course, you discover the benefits of Bokeh and Plotly for data visualisation. Bokeh is an interactive visualization library, which 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 visualization. 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. Bokeh also facilitates the creation of high-performance charts allowing users to build impactful web-based dashboards and applications. You'll investigate how to visualize your data using complex charts in Bokeh and identify how to visualize relationships that exist in your data using scatter plots.

Plotly is Python's browser-based graphing library, which provides users with online graphing, analytics, and statistics tools. 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. You'll explore how to use Plotly's declarative APIs to build interactive graphs and visualization. You will 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. And you will learn about the construction and applications of advanced charts in Plotly for varied use cases.

Result

After completing this course you can use Bokeh and Plotly to make attractive and informative data visualisations.

Prerequisites

No prerequisites.

Target audience

Database Administrators, Data analist

Content

Data visualisation with Bokeh and Plotly

7 hours

Data Visualization: Building Interactive Visualizations with Bokeh

  • 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.

Data Visualization: More Specialized Visualizations in 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.

Data Visualization: Getting Started with Plotly

  • 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.

Data Visualization: Visualizing Data Using Advanced Charts in Plotly

  • 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.

Course options

We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.

Optional practice exam (trial exam)

To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam. 

Optional practice lab

To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediatelyThe lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience. 

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