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Avis et commentaires pour d'étudiants pour Dealing With Missing Data par Université du Maryland, College Park

3.8
étoiles
118 évaluations
33 avis

À propos du cours

This course will cover the steps used in weighting sample surveys, including methods for adjusting for nonresponse and using data external to the survey for calibration. Among the techniques discussed are adjustments using estimated response propensities, poststratification, raking, and general regression estimation. Alternative techniques for imputing values for missing items will be discussed. For both weighting and imputation, the capabilities of different statistical software packages will be covered, including R®, Stata®, and SAS®....

Meilleurs avis

ZM
19 août 2019

interesting material, well taught, lots of short quizzes to enforce understanding.

MM
4 juin 2017

This course quite help to get as much reliable data as possible for any survey.

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1 - 25 sur 30 Avis pour Dealing With Missing Data

par MARTYNS N

17 mai 2019

The professor was not very explanatory and I just managed to finish the course out of my sheer strong will

par marine h

13 févr. 2019

very idfficult to understand. The sound of the videio is so low that most of it is impossible to understand, I had to try 10 times some of the tests because couldn't find the answer and had to guess it!

par Evan

24 déc. 2016

While this course seems to have potential, there are many aspects of it that don't result in a great learning experience. The course resources comprise of videos and notes. The videos are informative but the notes are fairly lacking. Perhaps the biggest issue that I found with this course was the disconnect between the material covered in the videos and that which was tested on the quizzes. Often times the quiz questions were either painfully easy or worded in such a way that was not verifiable in any of the class resources. As a result, confusion occurred sometimes more often than true learning. A topic such as missing data is naturally very complex and I wouldn't expect a short course on Coursera to be able to adequately cover it. However, I do think that a lot more could be done to improve the value of this course even if that means changing the scope of the materials. Also, the lack of responsiveness to issues raised on the forum and issue-reporting buttons was a disappointment.

par Ahmed I

31 août 2016

The quality of the presentation is very low, and way below the quality in other courses. The assignments are very poorly designed. This is not a subjective personal experience. This is based on discussions with other learners in the forum who have expressed disappointment and frustration.

par Reni A

5 avr. 2018

Prof. Richard Valliant, Ph.D. clearly enough explain all of these course materials. I will use these materials to dealing missing data on our census or survey. I believe that these materials were very helpful for me and my agency.

Thank you very much for all of this course.

par Lingbing F

10 févr. 2019

The topic of this course is attractive as it is hard to get from elsewhere. However, the content of this course is actually quite barren, practices are easy and not closely refective of the corresponding videos.

The fourth week is most interesting and I was happy to know that multiple imputation is actually not key on the "imputation" part. It emphasizes the fact that missingness should be considered as uncertainty in modelling.

After all, this is a interesting course and can be better designed and delievered. Thanks to the team.

par Iyshia L

8 nov. 2018

As others have stated before the audio is REALLY LOW. It makes it very difficult to hear him without headphones for my phone. The course was fine, overall.

par Patrick C

20 août 2020

I agree with the other reviewers. This course was terrible. Unlike other professors who have taught courses in the survey specialization, Professor Valliant made no attempt to explain the concepts in a way that would be comprehensible to an educated layperson. Instead, the lectures were rushed and laden with unexplained jargon. In order to have a minimal grasp of what is being presented, you must have a foundational knowledge of intermediate statistics and basic econometrics. Anything less than that and you'll be in over your head.

par Santiago R

26 août 2020

Compared to the other courses in the specialization, this course is not good. The professor mostly recites what he knows, but he is not trying his best to explain new concepts to students. Explanations should be more thorough, finding different ways to explain things, not just putting a slide and repeating. Examples are too far away from concepts, so the concept is explained without an example and later the example is givien. This makes it harder to understand the concept.

par Zachary M

20 août 2019

interesting material, well taught, lots of short quizzes to enforce understanding.

par Mohammad M

5 juin 2017

This course quite help to get as much reliable data as possible for any survey.

par Carlos F P

27 avr. 2017

Excellent review of relevant material.

par Tin K O

25 janv. 2017

Good knowledge about Non-responses!

par Roberto D C

4 juin 2020

Very useful and informative!

par Neeraj K

26 oct. 2016

it is very informative

par Anna B R

24 janv. 2018

Great course!

par Sid

3 juin 2020

The worst course in the specilisation. Bad content, bad instruction and a horrible experience.

par Ana A

11 févr. 2021

Great course! Thanks, Prof. Valliant.

par Zachary H

31 août 2016

I was interested in the topic. The course itself seems like just a starting point with understanding dealing with missing data. I wanted to know more and see more examples than the videos offered. I also would have appreciated including examples from more than just R, though I did appreciate the minimal discussion of other statistical software that are available for statistical analysis when it did occur.

par Tracy S

27 mai 2021

The course material was good but there were a couple of questions on the exams that weren't covered until the next module. Otherwise everything was very easy to follow and understand. I liked that the videos were shorter in duration as I was able to stay focused easier that way given the material can be a bit on the dry side with all the formulas, etc.

par Hussein E

25 déc. 2017

This is a higher level course. Good for beginners.

par Kelly D

22 juin 2021

I found it hard to follow this course and didn't find the instructor very engaging for some reason. More assignments rather than just quizzes would have helped. But the information covered is good and something I will refer back to when I need to.

par Alicia K

4 mai 2020

Good course, but I would have liked some hands-on course assignments to feel like I could apply what I learned.

par Anandita G

3 nov. 2018

There is scope for a lot of improvement in terms of quality of content as well as videos. There also appeared to be technical issues in the quizzes wherein the correct responses were often returned as incorrect & vice-versa, for a few quizzes. Without a moderator, queries are not addressed and nobody appears to be keeping track of the feedback. I was disappointed in the course since the previous courses in the specialization were far better designed and executed.

par Réjane F R

22 déc. 2016

The contents of this course could be interesting, but they end up being terribly boring. The course lacks examples to bring things to life. A pity!