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Avis et commentaires pour d'étudiants pour Inférence statistique par Université Johns-Hopkins

4.2
étoiles
4,341 évaluations
878 avis

À propos du cours

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data....

Meilleurs avis

JA

25 oct. 2018

Course is compressed with lots of statistical concepts. Which is very good as most must know concepts are imparted. Lots of extra reading is required to gain all insights. Very good motivating start .

MI

24 sept. 2020

the teachers were awesome in this course. I liked this course a lot.Understood it properly.Thanks to all the beloved teachers and mentors who toiled hard to make these course easy to handle.Gracious!

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751 - 775 sur 846 Avis pour Inférence statistique

par Esha D

11 févr. 2016

professors caffo and leek go way too fast and are completely monotonous. the examples they use assume we know basic stats when likely most of us are a bit rusty.

par Andaru

5 févr. 2016

The class needs to be more accessible especially for non statisticians, I learned more from khan academy which got me through this class than the class itself

par Peggy C

1 févr. 2016

This is the worst course so far. This should be a 2 month course or the course descritpion should be changed to make sure you have some statistics knowledge.

par Jose P

20 mars 2018

Course needs more hands on example on all statistical inference tools - trying to disconnect from the daily routine and dive right into stats was difficult.

par Nelson G C G

28 mars 2016

There are better courses on the subject on coursera and other platforms. It worth it pursuing it if you are interested in joining the capstone project.

par Romain F

9 juin 2016

Seemed like a "ghost" course, issues reported with the swirl package, duplicate questions in the final assessment, where is the instructor ?

par Prem T

31 oct. 2020

The lessons felt hurried and the explanation provided was complicated. All in all a bad method of teaching & a learning experience.

par Shikha B

10 mai 2016

Teaching material is fine. The Professor's explanation is monotonic and he uses textual definitions rather than simple explanations

par Fabiola J C

10 janv. 2021

Even though Brian presents the theory in a clean mathematical way, it is still difficult to understand and apply to the problems.

par Emanuel A F d S

23 juil. 2017

The course lectures are very confusing. I had to read a statistics book to clarify some of the concepts.

par Mario R

29 janv. 2016

The instructor is not very good when compared to the others that are involved in this specialization.

par ooi s m

12 juin 2017

Lots of formula without detail explanation, not recommended for people without statistics knowledge.

par Fabian H

15 juin 2016

Concepts are very hard to understand, even with some background knowledge in statistics.

par Chengde W

24 juin 2017

He is just reading the slides. I'd prefer to read a book rather than watch the videos.

par Gareth S

14 juin 2017

Assumed a level of knowledge of stats already. Found it went too complex too quickly.

par Mike B

3 oct. 2021

Most of the homework was not covered in the lectures. There are poor examples.

par Haolei F

15 avr. 2016

Not beginner friendly, might be good as a refresher for grad students

par Daniel R

14 mai 2016

This course was more like a glossary. Not quite good but practical

par Colin B

2 févr. 2019

Teacher is a bit erratic. It makes the course hard to follow.

par Peter H

17 mai 2016

Poor concepts exposition with a bad teaching method.

par Tony C

3 mars 2018

This class is not very well explained.

par Hariharan D

12 août 2017

Pedagogy needs to be improved.

par Zaid M M

15 déc. 2018

Could be better ...

par Anamaria A

28 févr. 2017

Too much, too soon.

par Nicolas C

17 août 2017

For beginners.