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In summary, here are 10 of our most popular seaborn courses
Frequently Asked Questions about Seaborn
Seaborn is an open-source Python data visualization tool that's based on matplotlib, a comprehensive Python library used to create interactive, static, and animated visualizations. Using seaborn gives you a way to bridge the gap between insight and data. It gives users a high-level interface to make statistical graphics, building upon matplotlib, and integrating with pandas data structures. It enables users to explore data with plotting functions that operate on dataframes. Users can focus more on what the elements of plots mean instead of how to draw them thanks to seaborn's dataset-oriented, declarative API.
Before you tackle learning seaborn, you should have a good foundation in the basic terminologies of computer programming. It also helps to have a firm grasp of Python or other programming languages. If you’re familiar with working with matplotlib, that’s a bonus too. If you’ve never worked with Python before, it might help to take a beginner-level course to develop those skills before learning seaborn.
Some common careers in data visualization include data visualization engineer, which has a median annual salary of $92,305 in the US as of 2021, according to PayScale. Other careers that may use seaborn include data analyst, business intelligence analyst, and data scientist. Because seaborn is a data visualization tool, it opens the door to a variety of careers working with data. Data is a hot field, with demand increasing for workers who can leverage the power of big data, according to The Balance Careers.
You can use online courses on Coursera to build a solid foundation in using Python and understanding data visualization in addition to learning how to use seaborn. While many of the courses are at the intermediate or advanced level, there are also courses available that are geared toward beginners. You may have an opportunity to produce charts using seaborn while learning more about data science and machine learning and developing hands-on skills such as dropping correlated features, implementing feature selection, and building boosts tree classifiers.
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