Data Visualization with Python

Visualizing data in plots and figures helps you to see patterns and provides insight. Visualizations can also be useful for data analysts or other data users. This course will show you how to use Matplotlib which is a powerful Python data visualization program. Matplotlib allows you to create rich visualizations using many types of data. This course will show you how to create visualizations using different types of data. These visualizations can also be automated, customized, and shared.

Seaborn is a powerful Python library that makes it easy to create informative and attractive visualizations. This course will introduce Seaborn and show you how to visualize data with box plots and scatter plots. While you examine survey responses about student hobbies and factors that influence academic success, this will be done. Seaborn's benefits as a statistical visualization tool will be discussed, along with how it calculates confidence intervals. Seaborn will be used in a variety of situations to help you analyze and communicate your data.

Data visualization is a crucial aspect of data science, as it allows for the discovery of insights and effective communication of results. While scatter plots and bar graphs are commonly taught, the true power of data visualization lies in understanding how, what, and why you visualize your data. This course focuses on creating captivating visualizations that effectively communicate your findings. It includes a comparison of US data and explores how uncertainty can be displayed. The course concludes with the use of open-access data from farmers' markets to create a polished visual report. By learning Python and utilizing its tools for data science, participants can gain the necessary skills to excel in this field.

One of the most important tasks is to understand the relationships between data scientists' data and their geographical context. This course will show you how to create beautiful visualizations using geospatial data with the GeoPandas package. This course will show you how to spatially link datasets and data to context. You will learn how to overlay geospatial data on maps to add spatial cues. You will use several datasets from Nashville's open-data portal to find the location of chickens in Nashville.

You can boost your data science skills with Python's most robust and popular data visualization libraries. You will learn how to use Matplotlib and Seaborn to create stunning interactive and static visualizations of categorical, aggregated and geospatial data.