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Learner Reviews & Feedback for Inferential Statistical Analysis with Python by University of Michigan

4.4
stars
220 ratings
45 reviews

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

RR

Mar 07, 2019

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

JX

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.

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26 - 43 of 43 Reviews for Inferential Statistical Analysis with Python

By Marina P

Oct 07, 2019

Very well done. A lot of practice.

By Benjamin F

Jan 14, 2020

Well presented and rich content.

By 고도균

Jul 12, 2019

The python codes are amazing.

By Beatriz J F

Nov 24, 2019

Very satisfied.

By cameron g

Apr 22, 2019

Excellent

By Wenlei Y

Dec 18, 2019

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

Feb 22, 2020

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 Yury P

Jul 08, 2019

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

By Felipe N N B

Jan 25, 2020

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

By Sam F

Jan 27, 2020

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

By Bill G

Feb 24, 2020

Need Intermediate - Advanced skill level in Python.

By Kevin K

Oct 29, 2019

Wish there were more practice problems.

By Frank S Y R

Feb 14, 2019

I really enjoyed the course.

By Christine B

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.

By Marnix W

Jan 03, 2020

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

By Divyam A

Oct 06, 2019

Some parts can be explained better

By Andres R d S

Jan 14, 2020

Need to improve slides

By Darien M

Nov 30, 2019

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