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존스홉킨스대학교의 통계적 추론 학습자 리뷰 및 피드백

4.2
별점
3,843개의 평가
767개의 리뷰

강좌 소개

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....

최상위 리뷰

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 .

AP

Mar 22, 2017

The strategy for model selection in multivariate environment should have been explained with an example. This will make the model selection process, interaction and its interpretation more clear.

필터링 기준:

통계적 추론의 734개 리뷰 중 601~625

교육 기관: Bharadwaj D

Apr 05, 2017

Learnt many new things. It was good.

교육 기관: Koen V

Aug 11, 2019

Hard subject, hard explanations

교육 기관: Charbel L

Mar 08, 2019

Difficulty level is high...

교육 기관: KUNAL J

May 02, 2020

Its good but not too good.

교육 기관: Wassim K

Jun 05, 2017

Too mathematical for me

교육 기관: Biju B

Jun 05, 2017

The lectures were Dry

교육 기관: Dipankar

Sep 04, 2017

Good, Productive

교육 기관: David K

Aug 17, 2017

a bit cursory

교육 기관: Roberto L

Nov 11, 2018

Too sparse.

교육 기관: Ankush K

Jul 06, 2017

Very basic.

교육 기관: Santiago P G

Aug 01, 2017

A hard one

교육 기관: Suzhongdayi

Jul 12, 2016

no passion

교육 기관: 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!

교육 기관: 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.

교육 기관: 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.

교육 기관: 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.

교육 기관: Ricardo M

Dec 29, 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 .

교육 기관: Thomas G

Apr 05, 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.

교육 기관: Joana P

Feb 22, 2018

I found that the materials given or the lectures never allow you to clearly follow a structure.

I understand that are so many contents to present, but jumping around from one to another is not the way.

Quite frequently a lot of the slides are just useless. Not all of us have the time to go behind every mathematics, so I would like to see more real examples of how to use the contents you teach us, than knowing all the mathematics and have a lot of slides to show how to deduct mathematically the probability of something to happen. But might be my opinion because I had other expectations.

교육 기관: Qasim Z

Oct 26, 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.

교육 기관: Christoph G

Sep 13, 2016

I have to admit, that most of the videos confused more than they helped. For most of the topics I simply went to KhanAcademy and got it easily there and I was asking myself, why a topic was made so complicated. I have the same issue with the next course. I highly recommend to start visual explanations of what you want to achieve instead of throwing formulars at people and confuse them. IN KhanAcademy e.g. they do exactly this and you got the point and if you got the point, the formular wasn't such a big thing anymore.

교육 기관: Anant C

May 06, 2020

The content of the course was well organized and structured, but the content delivery in the videos was terrible. I was unable to understand even the basic concepts from the professor due to his fragmented sentence structure, lack of lecture planning and an emphasis on evaluating R code more than on explaining the concept. I am now hesitant to go on to the next course in this specialization. The Swirl() exercises, however, were very thorough and did a good job in explaining the concepts.

교육 기관: Krishna U

Jul 29, 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.

교육 기관: graham s

May 21, 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.

교육 기관: Christine L

Nov 05, 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.