Back to Inferential Statistical Analysis with Python
Learner Reviews & Feedback for Inferential Statistical Analysis with Python by University of Michigan
925 ratings
About the Course
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.
Top reviews
HJ
Nov 18, 2020
It was great. I could get a experience hands on and every skill were very useful. In other stats courses, I mostly felt hard to embrace the thoughts. Here, the instructors were very very insightful.
R
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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