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Inferential Statistical Analysis with Python(으)로 돌아가기

미시건 대학교의 Inferential Statistical Analysis with Python 학습자 리뷰 및 피드백

798개의 평가
146개의 리뷰

강좌 소개

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....

최상위 리뷰


2020년 4월 1일

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.


2021년 1월 21일

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.

필터링 기준:

Inferential Statistical Analysis with Python의 145개 리뷰 중 101~125

교육 기관: Dr G S

2022년 3월 9일

very good

교육 기관: cameron g

2019년 4월 21일


교육 기관: Yurii S

2020년 5월 17일


교육 기관: EmyZhang

2021년 5월 6일


교육 기관: P. B R

2020년 4월 24일


교육 기관: s n

2020년 3월 2일



2020년 3월 2일


교육 기관: Jerrold

2020년 11월 19일

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.

교육 기관: Matteo L

2020년 4월 5일

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.

교육 기관: Carlos M V R

2020년 8월 31일

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.

교육 기관: Wenlei Y

2019년 12월 17일

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.

교육 기관: Hwanmun K

2020년 2월 22일

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.

교육 기관: Pankaj Z

2020년 5월 20일

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.

교육 기관: Carlos F G

2022년 2월 21일

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

교육 기관: Asem K

2021년 12월 9일

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.

교육 기관: Vu M D

2022년 8월 7일

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

교육 기관: William O

2021년 1월 10일

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.

교육 기관: Yury P

2019년 7월 8일

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

교육 기관: Felipe B

2020년 1월 25일

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

교육 기관: Harshad S M

2020년 8월 19일

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

교육 기관: Faroq M M A

2021년 7월 15일

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

교육 기관: Sam F

2020년 1월 27일

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

교육 기관: Khaled S A

2020년 3월 23일

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

교육 기관: Kim J

2020년 10월 16일

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

교육 기관: Bill G

2020년 2월 24일

Need Intermediate - Advanced skill level in Python.