4.6
789 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.

126 - Inferential Statistical Analysis with Python 的 139 个评论（共 139 个）

Oct 14, 2020

I had already taken a Statistics course in my College, and took this course less to learn the concepts and more so to understand how to code Inferential Statistic in Python.

I definitely learnt how to do that at the end of the course, Confidence Intervals, Hypothesis testing, Z and T tests, etc. were taught well by the instructors.

However, many of the Lectures don't match the subsequent Quizzes ( quizzes are much easier and sometimes unrelated), and the Jupyter notebooks have you code Normal Multiplication and division of numbers to find the Intervals (for eg), instead of teaching you how to master the Scipy.Stats Module or use other powerful libraries which you will be expected to know if you land a Statistics related role in a Company.

Overall, it was a good course and knowing it's part of a specialization means you still have much to learn, but I hope the course creators make it more challenging for non-beginners and Programmers

Sep 17, 2020

Statistics theory is well explained with several examples and additional resources, lectures are very clear, but it is part of a Statistics with Python Specialization, I expected to have more deep instructions about statistical part of Python (packages and strategies), there are lots of questions about Python coding and functions into the forums, I think a lecture explaining the different packages and functions would be a good idea. From my point of view the Python tutorials could also be more explored, it was too much on surface of it for me.

Jan 16, 2022

Mistake in the course instructions and very redundant material. A better understanding of the concepts rather than a series of walk-throughs for different scenarios, would've been better suited to me. Recommended external resources were good. Overall, an ok course, but definitely not the best in terms of design.

Jan 21, 2022

C​ourse focuses more on the mathmatical side without getting too far into much of any programming. I would have liked to have many more labs to go along with what we were learning and have more practice questiosn to solve instead of having my hand held through almost every aspect of the course.

Jun 6, 2021

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. But this course is quite difficult , but interesting also

Sep 20, 2019

I found Brady T West's videos in Week 4 to be unnecessarily confusing causing me to have to go back to Week 3 lectures to clarify the steps of hypothesis testing.

Jan 3, 2020

I'd like a little more interaction with Python during the explaination itself.

Oct 6, 2019

Some parts can be explained better

Jan 14, 2020

Need to improve slides

May 4, 2022

I guess I managed to learn some coding tricks with Pandas in this course, but I'm not sure what else it was supposed to teach. To the extent that I learned any statistics concepts, it was by searching on google to try to find explanations of whatever the instructors were talking about. They seem to have no interest in explaining anything, to be honest. They throw various equations at you without any indication of where these equations come from or why they work. Sometimes very important points, such as degrees of freedom in a test, are only mentioned as an aside. Really felt like a waste of my time and money. Hopefully there are some better statistics courses out there.

Jun 24, 2020

I will never take another course from University of Michigan. I'll just finish this specialization because I went 2/3 of the way and I feel bad if I don't get the certificate. but it was such a wate of time, one of the worst courses I have ever taken. it is not hard, it is bad! I don't recommend this to anyone because there is nothing to learn, unless you want to watch 12 weeks course to learn how to plot and read csv files and multiply numbers in python!

Jan 14, 2021

Peer reviewed things should be eliminated. It's taking forever to rate an assignment, in fact, more than the "expected date" that they show.

Jun 6, 2020

My final specialization course certificate not received, even after completing all courses in this specialization.

Nov 29, 2019

Python does not deserve to be in the title of this course.