Matplotlib is a Python plotting library used to create dynamic visualizations using pyplot, a state-based interface. You'll learn how to correctly install and use Matplotlib to build line charts, bar charts, and histograms in this course. You'll create basic line charts out of randomly generated data. You'll learn how to use the plt.subplots() function, import data from a CSV file using pandas, and create and customize various line charts. Additionally, you'll create figures holding more than one axes object, learn why and how to use the twinx() function, and create multiple lines in the same line chart with different y-axes for each line. Moving on, you'll construct histograms that visualize multiple variables and approximate the cumulative probability density function. Lastly, you'll create some bar charts to represent categorical data.

Matplotlib can be used to create box-and-whisker plots to display statistics. These dense visualizations pack much information into a compact form, including the median, 25th and 75th percentiles, interquartile range, and outliers. In this course, you'll learn how to work with all aspects of box-and-whisker plots, such as the use of confidence-interval notches, mean markers, and fill color. You'll also build grouped box-and-whisker plots. Next, you'll create scatter plots and heatmaps, powerful tools in exploratory data analysis. You'll build standard scatter plots before customizing various aspects of their appearance. You'll then examine the ideal uses of scatter plots and correlation heatmaps. You'll move on to visualizing composition, first using pie charts, building charts that explode out specific slices. Lastly, you'll build treemaps to visualize data with multiple levels of hierarchy.

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.

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.

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.

Perform data visualization tasks such as creating an Infogram project, building an infographic and creating box-and-wisker plots, line charts and histograms. Then visualize relationships using a scatter plot, create a bar chart using Bokeh, and create a box chart using Plotly.

This lab provides access to tools typically used for data visualization, including:

- Jupyter Notebook
- matplotlib
- numpy
- pandas
- Plotly
- Bokehh

Final Exam: Data Visualization with Python will test your knowledge and application of the topics presented throughout the Data Visualization with Python track of the Skillsoft Aspire Data Visualization Journey.