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Opiniones y comentarios de aprendices correspondientes a Inferential Statistical Analysis with Python por parte de Universidad de Míchigan

4.6
estrellas
554 calificaciones
95 reseña

Acerca del Curso

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....

Principales reseñas

RZ

Apr 02, 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.

AA

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.

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51 - 75 de 93 revisiones para Inferential Statistical Analysis with Python

por 18z334 M

Jul 29, 2020

Every concept was clearly explained

por Marina P

Oct 07, 2019

Very well done. A lot of practice.

por RODRIGO E P M

Aug 26, 2020

An excellent introductory course.

por Benjamin F

Jan 14, 2020

Well presented and rich content.

por Thanaa A K

Aug 22, 2020

perfect course thanks everyone.

por Javier F

Jun 26, 2020

Excelente curso, muchas gracias

por BALAJI R

May 20, 2020

Highly recommend this course.

por 고도균

Jul 12, 2019

The python codes are amazing.

por JITHIN P J

May 17, 2020

very nice and informative

por Sebastian R R

Aug 16, 2020

Excelente curso!!!

por Aniket D S

Apr 15, 2020

Excellent Cousre.

por Beatriz J F

Nov 24, 2019

Very satisfied.

por Dr V R V

Jul 06, 2020

Extremely Good

por Cameron G

Apr 22, 2019

Excellent

por Yurii S

May 17, 2020

Greate!

por P. B R

Apr 24, 2020

good

por SREEYA N

Mar 02, 2020

good

por KOPPARTHI H H

Mar 02, 2020

good

por Matteo L

Apr 05, 2020

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.

por Carlos M V R

Aug 31, 2020

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.

por 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.

por 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.

por Pankaj Z

May 20, 2020

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.

por Yury P

Jul 08, 2019

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

por Felipe B

Jan 25, 2020

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