Advanced Visualizations & Dashboards: Visualization Using Python

Advanced Visualizations & Dashboards: Visualization Using Python

• Course Overview
• recognize the importance and relevance of data visualization from the business perspective
• list libraries that can be used in Python to implement data visualization
• set up a data visualization environment using Python tools and libraries
• list the prominent data visualization libraries that we can be used with Matplotlib
• create bar charts using ggplot in Python
• create charts using the bokeh and Pygal libraries in Python
• recognize criteria that should be considered when selecting an appropriate data visualization library
• create interactive graphs and image files
• plot graphs using line and markers
• plot multiple lines in a single graph using different line styles and markers
• create a line chart with Pygal, create an HTML directive to render the line chart, and render the line chart

In this course, learners explore approaches to building and implementing visualizations for data science, as well as plotting and graphing using Python libraries such as Matplotlib, ggplot, bokeh, and Pygal. Key concepts covered here include the importance and relevance of data visualization from the business perspective; libraries that can be used in Python to implement data visualization and how to set up a data visualization environment using Python tools and libraries; and prominent data visualization libraries that can be used with Matplotlib. Then see how to create bar charts by using ggplot in Python; how to create charts, using the bokeh and Pygal libraries in Python; and criteria that should be considered when selecting an appropriate data visualization library. Learners observe how to create interactive graphs and image files; how to plot graphs using line and markers; and how to plot multiple lines in a single graph with different line styles and markers. Finally, see how to create a line chart with Pygal, create an HTML directive to render the line chart, and render the line chart.

Advanced Visualizations & Dashboards: Visualization Using R

Advanced Visualizations & Dashboards: Visualization Using R

• Course Overview
• list the different types of charts that can be implemented and their relevance in data visualization
• demonstrate how to create a stacked bar plot
• create Matplotlib animations
• use NumPy and Plotly to create interactive 3D plots in Jupyter Notebook
• list graphical capabilities of R from the perspective of data visualization
• build heat maps and scatter plots using R
• implement correlogram and build area charts using R
• recognize ggplot2 capabilities from the perspective of data visualization
• build and customize graphs using ggplot2 in R
• create heat maps using R, create scatter plots using R, and create area charts using R

Discover how to build advanced charts by using Python and Jupyter Notebook for data science in this course, which explores R and ggplot2 visualization capabilities and how to build charts and graphs with these tools. Key concepts in this course include different types of charts that can be implemented and their relevance in data visualization; how to create a stacked bar plot; how to create Matplotlib animations; and how to use NumPy and Plotly to create interactive 3D plots in Jupyter Notebook. Learners are shown the graphical capabilities of R from the perspective of data visualization; how to build heat maps and scatter plots using R; and how to implement correlogram and build area charts using R. Next, you will explore ggplot2 capabilities from the perspective of data visualization; learn how to build and customize graphs by using ggplot2 in R; and how to create heat maps, a representation of data in form of a map or diagram. Finally, learn to create scatter plots and create area charts with R.