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

4.2
étoiles
4,053 évaluations
805 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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526 - 550 sur 773 Avis pour Inférence statistique

par Jeff H

Sep 14, 2017

tougher content

par Mehul P

Sep 20, 2017

Nice course !!

par Anup K M

Oct 20, 2018

good content

par Craig S

Dec 04, 2017

Good content

par Sravan K

May 28, 2017

good course

par Rohit K S

Sep 21, 2020

Good one!!

par Johnnery A

Dec 31, 2019

Excellent!

par Timothy V B

Mar 26, 2017

great

par karan

Feb 24, 2016

Nice.

par Reinhard S

May 19, 2017

ok

par Jared P

Apr 10, 2017

I'm in the data science specialization. Statistical Inference was my 6th course. All of them have been on a spectrum of good to great. But Statistical Inference is a mixed bag for me.

First, if you are thinking about this course, take some time reading the other reviews. I find many of them resonate with my experience leading to 1/5 stars.

One reviewer who gave 5/5 stars said they loved the course. They suggested that other reviewers who gave low ratings are ones that dropped out. I don't find this to be the case. Many of the low rating reviewers actually did pass the course and said very similar things as those who did not pass the course.

Another reviewer enjoyed Brian's dry humour. I must have missed the jokes after watching each video 5 times...

For the record, I almost aced this course. The reason for not getting 100% was because I was so annoyed with one of the quizes that I didn't bother taking it again to correct it. I decided living with 8/10 correct questions was better than having a stroke while in the pursuit of two extra points. Yes, that is how much I hated this course.

The first 2 weeks of the course were the worst. I dropped out for about a month (because of life priorities). Then I couldn't get motivated. 1 month turned into 2 months, then into 3 months. I basically took the entire summer off. Finally I bit the bullet and completed the final 2 weeks. The 3rd week of the course wasn't actually all that bad (though the quiz was terrible). The 4th week felt like the first 2 weeks...terrible.

(By the way, it's a mistake to take such a long break. I had to re-watch the first videos to recall things for the remaining quizzes).

If you don't have some sort of statistical knowledge (or inherent aptitude), be prepared to work four times longer on the course. For all your quiz and assignment time-to-completion estimates, multiply them by 4 or more. Seriously, I spent probably 10 hours on the final assignment which said it should only take 2 hours. Each quiz took me an entire Sunday afternoon (My partner was not pleased).

Now here is where things get awkward. I hated the course .... BUT....I learned things that actually stuck. So in THAT regard, I give this course extra stars. It accomplished something that some University courses could not. I even found myself USING the new knowledge in real world problems. So ironically, is that not a sign that it's ...dare I say...a GOOD course?

Would I take the course again? I actually might, but ONLY because of its place in the overall certification. If you are a prospective student wondering if you should take the course as a standalone course, I don't think I could recommend it, because there are far better ways to learn. In fact, just doing the Swirl lessons could be good enough.

If you are a prospective student and you want the certification, then you'll HAVE to take the course. Why are you even bothering to read reviews?

So I'm giving the course 3/5 stars. If I gave it 1 or 2 stars, my review would be clustered with the majority. If I gave it 4 or 5 stars, I'd be lying.

par Johann R

Jul 17, 2017

The content is what you would expect for this subject, but it is not quite presented in a logical and ordered way. The lecturer's style is also very uncomfortable, especially in the first week or two, where it feels like the content is just read (and fast), and not explained on a level expected for a course having no prerequisites. If students don't have any previous statistics experience or knowledge, they would find some of the concepts very difficult, especially as presented in this course, as it appears that the assumption is made that students have a certain level of statistics knowledge already.

I have done the Basic Statistics course on Coursera (University of Amsterdam) and that course takes a more methodical and logical approach to the basic concepts, and if I hadn't done that course already I would have really struggled with grasping the concepts explained in this course. Even having done the Basic Statistics course I struggled anyways, and had to resort to additional information like Statistics for Dummies and various other internet / YouTube videos for more methodical and clear explanations.

par Normand D

Jan 29, 2016

This is a great course taught by a clever teacher but...

The content is presented in a very dry, not easy to grasp, manner. In several cases, I had to use external sources to understand the content and/or derive it by myself. When I finally understood the content I couldn't understand why it is presented in such a cryptic manner when the concepts are rather simple to grasp and the math not so advanced.

Professor Caffo is a good communicator in some occasion (the module on Power for example was incredibly well communicated). But most of the time he just throw us some result without properly setting the context and concepts, as if it was understood that we already know most of what he is talking about. (Not the case!)

I plan to make a document that follows the course module and fill in the missing piece of contextual information, derivations and concepts. But this takes a lot of time. If/when it will be completed, I will try to find a way to share it with future generation of students. Because, honestly, the content of this course is not so hard and shouldn't be!

par Stefan L

Aug 29, 2016

As someone who's new to the world of data science and doesn't have a university degree this course was very hard to get a good grasp on.

That's partly the "cause" of how the course was taught which was assuming you had all the knowledge at hand of all the stuff Statistical Inference is about.

For people that are starting this stuff it might be nice to have a introductory course of Statistical Inference as I did not finish this course by just watching the course video's and additional information, I had to look up additional resources which explained the material better.

Still, a big thank you for explaining statistical inference and opening my eyes regarding this topic, it surely helped getting me to the next step in what Data Science is all about and makes it ever more interesting!

par Lee G

Jan 09, 2017

The course is a very quick run through of basic statistics and not very intuitive for people without much statistics/maths background. The swirl exercises is a very good practical learning tutorial that supplements the course, but overall it still lacks on the conceptual aspect. Personally, I have to occasionally refer to other basic statistics materials to be able to follow the flow and understand the lectures.

For the course project, there is a huge discrepancy in what the project expect the students to perform and the peer grading criteria. As a basic statistic course, the correctness of the estimation/ calculation/ assumptions is integral in any analysis but the grading criteria mostly neglect all this aspect. Hopefully the course admin can rectify this aspect of the course.

par Jan K

Mar 07, 2017

This is of course my personal opinion, with all due respect for the Tutors. Plus, it has to be noted that I am writing this as a Mathematics graduate, and this course was most probably not meant for people with any background. However, I have seen similar opinions from people like me. Probability calculus and statistics are both enormous areas of mathematics. Introducing them in a 4-week course seems a really bad idea to me. The probability part was in my opinion far better than the statistical, the origin of every new concept was clear. In my opinion, the optimal solution for the course would be to create a separate, longer course in PC and stats and require knowledge of the two for taking Data Science Specialization.

par Marcelo S

Feb 28, 2018

The course is not meant for beginners, but seems to be advertised as such. Knowledge of Elementary Statistics is a must. The course is fast-paced and most people would not be able to finish it in 4 weeks or understand all the concepts in the course without outside help. Use of Discussion Forums and Mentors such as Leonard Greski is invaluable for completing the course successfully. There are several minor flaws in the videos and textbook that need to be addressed. This course would be much better off broken into two (Elementary + Inferential Statistics) and buffered with longer videos and step-by-step instruction and help.

par Huang-Hsiang C

Jul 04, 2020

There is no doubt that topics covered in the course are fundamental and critical. However, instructors rushed through most topics and explain them in a not very intuitive way. To be fair, the "power" section in the course is actually pretty organized. Swirl exercises can improve your understanding to some degree, make sure to take them.

This is definitely not the 1st course if you are completely new to statistics. I'd suggest taking other similar Coursera courses or reading articles from different resources (e.g. http://www.sthda.com/english/) to better internalize all concepts.

par Andrew W

Jan 25, 2018

A topic such as statistical inference is not complicated, and could be taught in a much more straight forward and comprehendible fashion. Just look at the tons of material and (good old fashion books) that relate this material in a much more concise manner. Moreover, the material in this class including the R-files are not well synchronized (gives low quality impression). A lot of time is needed to sort out the documentation between R-files, the book (Statistical Inference for Data Science) and the slides. I find many errors and sometimes inconsistent notation.

par Olivia U

May 29, 2020

I have mixed feelings about this class. We are rushed through the concepts, I had to study a lot on my own to deeply understand the mechanisms - lucky for me I studied advanced mathematics in College, I mostly had to revive my memories. The video lectures are of very average quality, but the practical exercises in swirl helped a lot. Just the one final project is not enough imho. I can't judge yet if what I learned is enough to properly apprehend the algorithms at play in ML - we'll see. All in all, not the best course so far from the specialization.

par Tai C M

Sep 27, 2017

This course covers the very important things about statistics, I totally agree with that. But I find that if Coursera can make the entire course easier to understand for the layman, it will be the best. After I took the course, I need to visit youtube to do some researches to understand the more complex stuffs like power t test. Maybe coursera should look at Khan Acedemy and see if they can get some idea from it.

I usually go to https://www.youtube.com/watch?v=uhxtUt_-GyM&list=PL1328115D3D8A2566 to look for those chapters that I need to revise.

par Amol K

Jan 31, 2016

This course goes on a very fast pace and simply does not have the charm of all the other courses in the specialization. I understand that a lot of content is covered within a month, but there should be supplementary course material available. Moreover, TAs should be more active on the forums. I have seen most of the questions just being discussed among the students. A little disappointed. Will probably have to watch all the material again to have confidence with it.

par Emre S

Nov 23, 2017

Course topics is good and heavily dive into statistical training.

I may say that there is a lot of theoretical stuff and these need to be supported by real world simple examples.

I have spent twice the time to watch the youtube videos about the classes to settle my mind and see some examples.

Course content need to revised and realistic easy to understand content including R coding should be included.

Thanks for the effort spent so far.

par A. R C

Aug 23, 2017

It was more difficult than I expected. Besides to imagine inside your head some of the theoretical concepts. Instead of "accept or reject", we have "reject" and "fail to reject".... just as an example :) And now there is this discussion about p-values omg....

https://www.vox.com/science-and-health/2017/7/31/16021654/p-values-statistical-significance-redefine-0005?imm_mid=0f55ac&cmp=em-data-na-na-newsltr_20170809

par Sven K

Jan 29, 2019

I think it could be taught a tad better. Maybe more explanations in lessons and a bit better (read: less vaguely) worded course project description would be useful. I do understand the importance of this part of the DS specialization, but I would have loved a bit more careful approach to the subject. It is probably hard for an expert to lower himself to this admittedly low level of knowledge, but please do try.