This course teaches how to present and visualize complex data in a manner that makes sense to people, and allows for insights to be easily seen and derived. This course makes use of several Python data visualization libraries such as Matplotlib, Seaborn, Plotly, and Folium, to explore Canadian immigration data. It also covers dashboard creation using Plotly Dash, with a Peer graded assignment combining the skills learned to analyse and report US domestic airline flights performance in a dashboard.
📑 Main Topics
- Introduction to Data Visualization Tools
- Basic and Specialized Visualization Tools
- Advanced Visualizations and Geospatial Data
- Dashboard creation using Plotly Dash.
🔑 Key Skills Learned
- Using Matplotlib to create various plots, including line plots, area plots, histograms, bar charts, and more.
- Applying advanced visualization tools to create waffle charts and word clouds.
- Using Seaborn with Matplotlib to generate attractive regression plots.
- Creating maps and visualizing geospatial data, using Folium.
- Using Plotly, Plotly.graph_objects, and Plotly express commands.
- Using Dash and basic Dash components (core and HTML).
The following tools were used to complete this certification:
The following Python libraries were used throughout the certification:
🏆 Certificates
To verify the certificates, click the images to follow the links.
Data Visualization with Python
Issued by Coursera Authorized by IBM This badge earner has a good understanding of what data visualization is, uses of data visualization, and best practices when creating plots and visuals. The individual has the skills to use different Python Libraries, mainly Matplotlib and Seaborn to generate different types of visualization tools such as line plots, scatter plots, bubble plots, area plots, histograms, and bar charts. The earner is able to use the Folium library to visualize geospatial data and to create choropleth maps.