That’s why DataCamp has collaborated with Bryan Van de Ven, Bokeh core contributor, on a Interactive Data Visualization with Bokeh course, which was recently launched and which guides you through the possibilities that this package has to offer step-by-step and in an interactive way.Īdditionally, DataCamp also made sure that you can download a Bokeh cheat sheet for free so that you have a handy reference sheet to fall back on when you’re in doubt! Bokeh Cheat Sheet The package offers lots of possibilities to visualize your data in a compelling way, but it’s also so flexible and big that once you want to get started, you can feel a bit overwhelmed by the possibilities. ![]() That’s where the Bokeh package comes in: the Python data visualization library that enables high-performance visual presentation of large datasets in modern web browsers. When you do start with taking these topics into account, you’ll mostly hear matplotlib is one of the preferred packages for data visualization and it is, but sometimes you do need to take your skills up a notch if you’re working with large datasets that you want to visualize interactively in web browsers. Conversely, this might also be one of the hardest steps in your data science learning, as visualizing data or to tell a story about your data in such a way that your information sees the information that your analysis brings to the table can be particularly challenging. ![]() By Karlijn Willems, Data Science Journalist & DataCamp Contributor.ĭata visualization and storytelling one of the steps in the data science workflow that are often forgotten.
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