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学生对 密歇根大学 提供的 Inferential Statistical Analysis with Python 的评价和反馈

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In this course, we will explore basic principles behind using data for estimation and for assessing theories. We will analyze both categorical data and quantitative data, starting with one population techniques and expanding to handle comparisons of two populations. We will learn how to construct confidence intervals. We will also use sample data to assess whether or not a theory about the value of a parameter is consistent with the data. A major focus will be on interpreting inferential results appropriately. At the end of each week, learners will apply what they’ve learned using Python within the course environment. During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera....



Apr 1, 2020

This is a very great course. Statistics by itself is a very powerful tool for solving real world problems. Combine it with the knowledge of Python, there no limit to what you can achieve.


Jan 21, 2021

Very good course content and mentors & teachers. The course content was very structured. I learnt a lot from the course and gained skills which will definitely gonna help me in future.


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