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

4.6
stars
916 ratings

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

HJ

Nov 19, 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.

R

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.

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101 - 125 of 166 Reviews for Inferential Statistical Analysis with Python

By JAVIER O F C

Jun 26, 2020

Excelente curso, muchas gracias

By Joao S

Oct 1, 2020

Just another fantastic course.

By balaji r

May 20, 2020

Highly recommend this course.

By 고도균

Jul 12, 2019

The python codes are amazing.

By HASSAN M A H H M A H

Apr 12, 2023

سهلة التعلم لتحليل البيانات

By JITHIN P J

May 17, 2020

very nice and informative

By Sebastian R R

Aug 16, 2020

Excelente curso!!!

By Aniket S

Apr 15, 2020

Excellent Cousre.

By Beatriz J F

Nov 24, 2019

Very satisfied.

By Varanasi v r

Jul 6, 2020

Extremely Good

By Ime E

Oct 3, 2020

Great course.

By Eduardo L L

Sep 22, 2021

Good Course

By Dr G S

Mar 10, 2022

very good

By cameron g

Apr 22, 2019

Excellent

By Hoang V T

Oct 11, 2022

thanks

By Deleted A

May 17, 2020

Greate!

By Justin H

Sep 24, 2023

brutal

By Kazhumurat B

Dec 17, 2024

круто

By C R K R

Nov 21, 2024

good

By EISSA Y S

Apr 1, 2023

شكرا

By EmyZhang

May 6, 2021

good

By P. B R

Apr 24, 2020

good

By s n

Mar 2, 2020

good

By KOPPARTHI H H

Mar 2, 2020

good

By Jerrold

Nov 19, 2020

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

Pros:

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.

Summary:

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.