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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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701 - 725 sur 846 Avis pour Inférence statistique

par chris

11 juil. 2017

Heavy content to cover in such a short time

par Ram K P

3 août 2018

Most lessons lack clarity. very evasive

par Lei M

23 août 2017

The stuff is very high leveled for me.

par Tom C

15 sept. 2018

Would be better if taught with Python

par Bharadwaj D

5 avr. 2017

Learnt many new things. It was good.

par Koen V

11 août 2019

Hard subject, hard explanations

par Charbel L

7 mars 2019

Difficulty level is high...

par KUNAL J

2 mai 2020

Its good but not too good.

par Wassim K

5 juin 2017

Too mathematical for me

par Biju B

5 juin 2017

The lectures were Dry

par dipankar b

4 sept. 2017

Good, Productive

par David K

16 août 2017

a bit cursory

par Luv K

23 août 2020

Too complex

par Roberto L

11 nov. 2018

Too sparse.

par Ankush K

6 juil. 2017

Very basic.

par Santiago P G

1 août 2017

A hard one

par Suzhongdayi

11 juil. 2016

no passion

par Hani M

1 nov. 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 Eduard R

26 mai 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.

par Ricardo M

29 déc. 2017

The course delves into some relevant topics however it doesn't feel as properly structured. While on the first week the lectures seem to try to give a basic and comprehensible learning of probabilities, once we start into the topics of pure statistics, it's just gets a mess.

Lots of formulas and concepts thrown at you without much clarification. For someone without any knowledge/background on statistics this can be quite difficult to grasp the concepts.

The course should be reviewed or at least the indication of "eginner Specialization.No prior experience required." should be updated to mention that some knowledge in statistics is recommended .

par Thomas G

5 avr. 2017

This course seems weirdly balanced between assuming one knows very little about statistics while also assuming one is intimately familiar with statistics notation and terminology. It would probably be better if the data science track had an optional "intro to statistics" class that can take more time to let students familiarize themselves with the terminology, and then a separate "ins and outs of probability testing in R" for those already familiar. This course seems to try and bite off more than it can chew by attempting to be both at the same time.

Still, the lectures are interesting and the material is important to learn / cover.

par Qasim Z

26 oct. 2016

This course means well but the lectures in the first half of the course are not good. The instructor seems to take a midway between rigorous mathematics, using terms like robust etc while at the same time also trying to keep it easily accessible. RD Peng's courses take a much better approach in that they keep things at one end of the spectrum (simple language). Having a background in theoretical physics and computer science, this duality in this course is very confusing for me. Also, I do not need to see a tiny, grainy video inset of the instructor during the lecture videos.

par Krishna U

29 juil. 2019

Terribly confusing, and concepts were made so much more complicated than needed (lectures, instructions, quizzes).

Most other sources (Khan Academy, Stattrek, Stats textbooks etc) were used and preferred to complete course, to completion of the Data Sciences specialization. Or just have a full understanding in Statistics prior to this course.

Additionally, there's little discussion or help; wish this course could've been updated with revisions or clarified over the years.

par graham s

21 mai 2016

completely missed the explanation part of the teaching. Why use n-1 for standard deviation? "Because of degrees of freedom" Only mention, no further explanation. Just no explanations of anything in this course. I looked at the biostats course by the same guy. Same story. Teaching is more than just saying the facts, you have to explain things, lead the understanding. The materials are just not there, not in the book either.

par Christine L

5 nov. 2016

If I wasn't already familiar with statistics, I would find the lectures and course book difficult to follow. If future revisions to the course are made, consider including a cheat sheet with the notation, parameter abbreviations used, etc. It would also be helpful to rewrite (or at least include a reference back to) the equation being used in the example calculations instead of immediately filling numbers in.