Code and run your first Python program in minutes without installing anything! The "Visualizing & Communicating Results in Python with Jupyter" course is designed for learners with limited coding experience, providing a foundation for presenting data using visualization tools in Jupyter Notebook. Throughout the course, you will learn to describe and make inferences from data, and better communicate and present data. The modules in this course cover a wide range of visualizations that allow you to illustrate and compare the composition of the dataset, determine the distribution of the dataset, and visualize complex data such as geographically-based data. Completion of Data Analysis in Python with pandas & matplotlib in Spyder before taking this course is recommended.
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This course consists of modules covering the creation of various visualizations, communication of data using Jupyter Notebook, and practical application of learned concepts within a real-world context.
Learn to create charts to describe and compare the composition of datasets. Gain the skills to illustrate the distribution of data through visualizations. Benefit from instant feedback through a variety of assessment items, progressing from quick understanding checks to small, approachable coding exercises.
Master the art of generating visualizations for specialized data, such as geographical and three-dimensional data. Complete a cumulative lab at the end of the course to apply all learned concepts within a real-world context.
Explore the process of communicating data using Jupyter Notebook and understand how to effectively present data through visualizations. Completion of Data Analysis in Python with pandas & matplotlib in Spyder before taking this course is recommended.
Acquire the skills to visualize and communicate data effectively using Jupyter Notebook with a focus on illustrating and comparing dataset compositions, determining data distributions, and visualizing complex data such as geographically-based data.
Learn to visualize and communicate data effectively using Jupyter Notebook, focusing on creating comparison and composition charts, distribution charts, specialized visualizations, and practical application of learned concepts within a real-world context.
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