The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. Finally, you will explore different ways to generate regression plots in Seaborn. In this course you will get an introduction to the main tools and ideas in the data scientists toolbox. 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. By developing your data literacy, you can effectively discuss different types of data, data sources, analysis, data. This understanding is commonly known as data literacy. 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. To interact with data and those who work with it, you need to understand its key terms, concepts, and language. ![]() 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. Data Science Online Statistical Inference and Modeling for High-throughput Experiments A focus on the techniques commonly used to perform statistical inference on high throughput data. 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. Without statistics, data science would be incomplete, and the analysis of data would be limited to basic descriptive measures like mean, median, and mode. 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.
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