Aspire     Data Analyst to Data Scientist     Data Science Track 4: Data Scientist

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Explore Seaborn, a Python library used in data science that provides an interface for drawing graphs that conveys a lot of information, and are also visually appealing. To take this course, learners should be comfortable programming in Python and using Jupyter notebooks; familiarity with Pandas for Numpy would be helpful, but is not required. The course explores how Seaborn provides higher-level abstractions over Python's Matplotlib, how it is tightly integrated with the PyData stack, and how it integrates with other data structure libraries such as NumPy and Pandas. You will learn to visualize the distribution of a single column of data in a Pandas DataFrame by using histograms and the kernel density estimation curve, and then slowly begin to customize the aesthetics of the plot. Next, learn to visualize bivariate distributions, which are data with two variables in the same plot, and see the various ways to do it in Seaborn. Finally, you will explore different ways to generate regression plots in Seaborn.

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Objectives

Python for Data Science: Basic Data Visualization Using Seaborn Course Overview describe what Seaborn is and how it relates to other data science libraries in Python install Seaborn and load a dataset for analysis define and plot the distribution of a single variable using a histogram and kernel density estimate curve configure an univariate distribution's appearance, including color, size, and the components of the plot analyze the relationship between two variables by plotting a bivariate distribution distinguish between scatter plots, hexbin plots, and KDE plots use the Seaborn pair plot to generate a grid to plot the relationship between multiple pairs of variables in your dataset perform a regression analysis on a pair of variables in your dataset by using the Seaborn lmplot describe the basic aesthetic themes and styles available in Seaborn recall some of the use cases and features of Seaborn