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

4.2
étoiles
4,044 évaluations
803 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

Oct 26, 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

Sep 25, 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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626 - 650 sur 771 Avis pour Inférence statistique

par B S

Apr 25, 2018

Less good than expected. Explanations could be more clear.

par pulkit k

May 26, 2018

I don't like the example and the explanation at all.

par Jim M

Jun 07, 2020

Great material, but could be better organized.

par Thomas F

May 31, 2018

really bad review criteria for grading peers.

par chris

Jul 11, 2017

Heavy content to cover in such a short time

par Ram K P

Aug 03, 2018

Most lessons lack clarity. very evasive

par Lei M

Aug 23, 2017

The stuff is very high leveled for me.

par Tom C

Sep 16, 2018

Would be better if taught with Python

par Bharadwaj D

Apr 05, 2017

Learnt many new things. It was good.

par Koen V

Aug 11, 2019

Hard subject, hard explanations

par Charbel L

Mar 08, 2019

Difficulty level is high...

par KUNAL J

May 02, 2020

Its good but not too good.

par Wassim K

Jun 05, 2017

Too mathematical for me

par Biju B

Jun 05, 2017

The lectures were Dry

par Dipankar

Sep 04, 2017

Good, Productive

par David K

Aug 17, 2017

a bit cursory

par Luv K

Aug 23, 2020

Too complex

par Roberto L

Nov 11, 2018

Too sparse.

par Ankush K

Jul 06, 2017

Very basic.

par Santiago P G

Aug 01, 2017

A hard one

par Suzhongdayi

Jul 12, 2016

no passion

par Hani M

Nov 01, 2016

A lot of the concepts in Stats Inf - although simple when you think about it and used pretty much every day - I felt were difficult to understand at first. Wikipedia and some other online sources, and youtube videos, were more helpful but I think the real issue lay in the teaching style. I won't knock Mr. Caffo like some of the others here have because at the end of the day everyone learns differently. What works for some might not work for others and unfortunately his style did not suit my learning requirements.

My rating is purely based on the content which I think can be simplified by giving more visual examples. I am rating this after taking the 'Regression Models' course and in that course it is MUCH easier because he gives "real time" and visual examples of what, eg Residuals, mean or represent. Just that alone made a huge difference and it then helps me focus on how to write the R code rather than trying to understand the math. Hope this helps!

par Vincenc P

Feb 11, 2016

I am left feeling this course needs work. I don't know if it's the pain of switching to the new platform or what, but the total lack of any support from the TA/instructor team is frustrating. Add to that the fact that Brian skips from slide to slide very quickly often not providing adequate explanations and you'll be re-watching the videos many times over.

Several of the videos have blatant errors in them, like the fast that the fourth video of a week also contains the entire third video... again.

Such things should not have passed a half decent QA test.

More than anything this specialization should not be marketed as "no previous experience needed". You need to know some statistics. And by some, I mean do the whole thing on Khan Academy first.

par Supharerk T

Mar 07, 2016

I get this course since I really want to learn on boosting. However, I think the course pace is too fast and should be include more 'non-greece symbol'. I have a background in biostatistic and epidemiology but I'm still having a hard time understanding lecture. I would say that this course is the most difficult among this specialization (practical machine learning is much easier than this). I bet many students will have a problem in this course since it's a 'beginner' level specialization.

My suggestion is to 'slow down' , expand the lecture with more drawing/picture explanation, more r coding and get rid of those greece-symbol as much as possible.

par Eduard R

May 26, 2020

Connection between the slides, transcript, R code, and pdf presentation slides and the text book is great! Easy to follow along. Concepts are explained poorly. Often definitions are missing and the student has to guess what is meant by a variable on the slides. Very superficial learning. Not nearly compareable to real university course. I think the students would benefit from more project work assignments and peer reviews. This is when you really learn something - when you have to do it yourself. Quizes are a good start. I did the course as a refresh and I can't imagine correctly understanding the concepts just by having completed this course.