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Inferential Statistical Analysis with Python に戻る

ミシガン大学(University of Michigan) による Inferential Statistical Analysis with Python の受講者のレビューおよびフィードバック



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....




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.



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.


Inferential Statistical Analysis with Python: 101 - 125 / 145 レビュー

by Dr G S


very good

by cameron g



by Yurii S



by EmyZhang



by P. B R



by s n






by Jerrold


I really don't see the reason for all the hate for this course and the specialization.


Robust syllabus on statistics and mathematics that covers all the important concepts in inferential stats

Ample example python notebook files for students to reference

High quality lectures and content

Manageable assignments and quizzes

Lots of guided examples (week4) and excellent readings written by UoM on statistics and data analysis theory and practices.

Student forum support from lecturers is excellent

Cons (minus 1 star):

While the material in this course is good, we should be given some notes with formulas and diagrams to accompany us at the start of week 2 and 3 (the hardest ones)

A person without a background in python will struggle in this specialization because you need to have programing skill and experience and the introductory practices are not enough.

You need to have some prior experience with stats or a pre-college/college year 1 text book to accompany you if this is your first time learning stats. The start-middle phase content at each chapter is explained and NOT skipped, but it could use more elaboration. I had to source elsewhere on the internet for the gaps in my knowledge (which were easily found). It is just missing a few elementary level explanations (how to calculate P values and what tests to use in different scenarios) to understand the more complex topics. I learned hypothesis testing in high school and had to refer to my textbooks for a few explanations and diagrams.


Very satisfied with this course for what I got out of it, I gained multiple skills and a lot of familiarity with theory and examples.

by Matteo L


Just like the other two courses of this specialization I believe the content offered here is great and the main methods used for statistical inference are well explained and even possibly more important, the interpretation of results is really hammered home here which is great. A few things that weren't covered thoroughly enough (if at all) in my opinion are QQplots (maybe this is more related to course 1...) and Chi-square tests (what are they and when do we use them?). Also it would have been nice to take a little bit more time to explain the differences in using t-tests and z-tests and why we would choose one over the other. I do believe the structure of the notebooks could be improved, maybe listing all of the possible functions that can be used for statistical inference for each type of scenario (e.g. functions applicable for mean of population proportion). As always, I would have loved for answers to be provided for the "extra practice" notebooks.

by Carlos M V R


This course gives a lot of important concepts such as confidences intervals, p-values and hypothesis testing, but I think it is short in terms of using it in real life because the explanations rely on examples that always fulfil the same conditions and in real life it is not possible to have always the same conditions for a problem you want to study. It would be nice if the course could be complemented (in a deep way) with applications of complex samples and non-probability samples, not only single random sample. Also, python codes are not explained in a deep way.

by Wenlei Y


The teaching team is great. But the assignments are not very helpful. And yes, this is more a statistics course than a python course. The application with python, which I am more interested in, seems just the supplementary portions to the lectures of concepts of statistics. There is not much introduction to how we use python to perform statistics, how we debug, and how we interpret the outcomes of programs.

by Hwanmun K


It would be better to give precise definitions of each test, at least in optional reading material. Also, sometimes different lecturers used different terminologies and sometimes concepts not covered before just popped up in the video (ex. chi-square test). In general, it seems more organization in the material needed.

by Pankaj Z


The course gives details on several stats concepts. Its one of the finest course here on Coursera. You gain a significant amount of knowledge on Statistics.

As the course progressed, I felt the content was squeezed and students were bombarded with the content without giving a real life example on them.

by Carlos F G


Clear and detailed explanation of inferential statistics. The course approach is more by blackboard than what can be interpreted by the title "with python". Although there are some examples in python, there are not many exercies for the student

by Asem K


Could be made more organized, like the first course in the specialization series. Seems there are some missing gaps (or assumptions of things being covered) that made it a challenge to smoothly proceed in the first 2 weeks of content.

by Vu M D


Useful course to learn basic concepts of inferential statistical analysis. However, I would expect more Python exercises/assignments than the essay-type writing assignment.

by William O


Thank you a lot. For me was an incredible course I learned many things and was very important to my career. Thanks to all the team, They are really masters.

by Yury P


Good theoretical foundation, but lacks explanation on python libraries extensively used in the course.

by Felipe B


the fundamentals and intuition are greatly explained. The python part feels a little rushed though.

by Harshad S M


Great experience, though very helpful and happy working with the real world dataset and problems

by Faroq M M A


​A very good one, but it would be great if more challenging exercises and examples were added.

by Sam F


Overall solid course. Could do without peer review assignment, more of a hassle than anything.

by Khaled S A


Perfect Course, It was very useful to understand the basics of inferential statistics

by Kim J


Good and accessible introduction to hypothesis testing and confidence intervals ...

by Bill G


Need Intermediate - Advanced skill level in Python.