Supercharge your data science skills by learning how to create data visualization in Python.
Over four courses and one assessment, you’ll explore Python's most popular and robust data visualization libraries, including Matplotlib, Seaborn, Bokeh, and others, to create beautiful static and interactive visualizations of categorical, aggregated, and geospatial data.
Along the way, you’ll develop the essential skills to create informative visualizations that can showcase your data, giving you the confidence to create your own data visualizations with Python.
Data visualization is fast becoming an essential skill in industries as diverse as finance, education, healthcare, retail, and more. This track will help you develop practical Python data visualization skills to apply across various data-driven roles, helping you tell stories with your data.
Overview
Syllabus
- Introduction to Data Visualization with Matplotlib
- Learn how to create, customize, and share data visualizations using Matplotlib.
- Introduction to Data Visualization with Seaborn
- Learn how to create informative and attractive visualizations in Python using the Seaborn library.
- Improving Your Data Visualizations in Python
- Learn to construct compelling and attractive visualizations that help communicate results efficiently and effectively.
- Visualizing Geospatial Data in Python
- Learn how to make attractive visualizations of geospatial data in Python using the geopandas package and folium maps.
- Compare Baseball Player Statistics using Visualizations
Taught by
Mary van Valkenburg, Ariel Rokem, Nicholas Strayer, and DataCamp Content Creator
