4.6
788 个评分
141 条评论

## 课程概述

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....

## 热门审阅

RZ

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.

RS

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.

## 26 - Inferential Statistical Analysis with Python 的 50 个评论（共 139 个）

Apr 8, 2020

All instructors were very knowledgeable. Special mention goes to Prof. West. I found the last section (week 4) very insightful, detailed and rigorous. I would have loved seeing a deep discussion on the theoretical and practical choices behind the Null and the Alternative Hypothesis. I am still slightly confused on the purpose of the Alternative hypothesis. Overall a great course!

Mar 27, 2021

I Used to have some trouble in understanding Hypothesis testing as a concept, but after completion of this course I got a solid Idea on the whole concept. Thanks to the instructors for making it easy. But you can add a cheat sheet for the formulas of various Confidence Interval calculations and Hypothesis Testing. It will a good way for us to summarize while revising the topics.

May 27, 2020

Excellent course! I really enjoy the combination of Statistics-based Python assignments. The Jupyter Notebooks are well written, easily documented, and there is plenty of lecture material to confidently complete the assignments. I find this makes it much easier to learn both Statistics and Python simultaneously, without any frustrating"This wasn't covered in lecture!" moments.

Jul 13, 2019

A complete course focused on teaching the details and intuition of experiment design, inferential analysis for decision making through confidence interval ans hypothesis testing and how to state effective questions.

I would recommend this course to everyone who are seeeking for more explainability and improvements in its ability to solve complex problems through data analysis.

Aug 15, 2020

my favorite course in this specification. The subject on hypothesis testing is well designed in this course, the instructors are good, reading are insightful, python programming illustrations are easy to understand even for a new programmer like me. Shout out to Julie, a five star instructor who has a beautiful voice!

May 7, 2020

Great course...It is well organized, tutors made complex concepts very simple, I learnt how to find CI and do hypothesis testing in python. Overall very good experience. Hope the course material is accessible to me later as well, I need go through it again to reconfirm my understanding of complex concepts

Mar 24, 2022

It's a great course. I struggled a lot before to understand hypothesis testing, confidence intervals and how to interpret such stuff. Now, I consider myself fully able to easily handle these concepts. The teaching approach is gradual, step by step, and will convey the info very successfully.

Jul 6, 2021

It is a excellent course, a lot of examples, guides and lectures than are very helpfull. The only thing that I could change is the use of only normal distribution in all examples, in the real life we must use some other probability distributions and is important to talk more about these.

Sep 17, 2021

I learned a huge lot from this Course! I would have loved to have more references on how to account for sample weights and the non-parametric testing part. But the content was super clear and my inderstanding of how statistical inference works change from earth to heaven!

Jun 26, 2021

No online course comprehensive than this one, applied Python skills along with important theoretical statistics concepts emphasized throughly. It has an applied approach also not delving into deep theory rather focusing the student to what and how to apply.

Jan 24, 2021

All are excellent, except that for peer-reviewed exercise, I think more guidance about correct answers should be given, because I found some peers didn't fully grasp the concepts and wondered if they could grade the other people memos appropriately.

Jan 3, 2021

Excellent course the professors transmitted in a synthetic way the essential of the statistics, in order to have a global vision.The practical cases allowing to directly apply the new tools, this training is simply brilliant!

Sep 10, 2020

Great Course. Very lucidly taught. The instructors have done a commendable job in breaking down complex statistical inferential methods into simple parts and explaining the same with diagrams, examples and resources.

Sep 30, 2021

O​verall it was pretty heavy. A certain delay was observed with the perr-reviewed assignment, but the content is quite good. It is a step-up from Course 1 however, and the pace has increased quite significantly.

Jan 6, 2021

Exceptional course- Brady T West explains everything so brilliantly and I love the recaps and plentiful examples. There are a few things referred to that then don't feature in Python Labs though e.g. Chi-Square

Oct 20, 2020

Material was presented in an organized fashion. Very helpful discussion forum. My questions were answered the same day, usually in a few hours. It was the best beginner statistics class that I've ever taken.

Jul 6, 2021

G​reat course. Wish there was a little more time spent covering how to do things in python (those videos went very fast and sometimes reading the documentation doesn't help much), but overall Very good!

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.

Jan 22, 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.

May 28, 2020

The best part of this that it is designed in a way that it encourages people to dig deeper and explore more. The instructors have done a great job in making the curriculam this good.

Aug 7, 2019

I really appreciate the course and let me accumulate a lot of knowledge about statistics. And I have developed a good impression of the University of Michigan teaching level.

Mar 7, 2019

If you are interested in statistics and statistical analysis, this course gets you grounded in the essential aspects of statistics. Excellent instructors.

Jun 22, 2019

A very in-depth learning material for inferential statistics. Very good explanation of p-value which clarifies some of the prevailing misunderstandings.

Nov 6, 2020

Great course with practical experience with Python. There are many courses that teach statistics with R but this is the first one to do so in Python.

Jun 1, 2021

Do you want to have an in-depth understanding of statistics in data science? Take the full specialization and you won't regret it. Fantastic course