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At the conclusion of every week, learners will apply what they’ve discovered utilizing Python within the course natural environment. For the duration of these lab-centered periods, learners will get the job done via tutorials specializing in certain case reports to help solidify the week’s statistical principles, that can incorporate additional deep dives into Python libraries which include Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook natural environment in just Coursera.

We will likely center on many modeling objectives, which includes creating inference about associations between variables and making predictions for potential observations.

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Displaying quantitative facts with graphs: Exhibiting and evaluating quantitative dataDescribing and comparing distributions: Exhibiting and evaluating quantitative dataMore on facts shows: Displaying and comparing quantitative knowledge

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