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Retour à Biostatistique mathématique Cours intensif 1

Avis et commentaires pour d'étudiants pour Biostatistique mathématique Cours intensif 1 par Université Johns-Hopkins

398 évaluations
88 avis

À propos du cours

This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required....

Meilleurs avis

4 juin 2017

I knew a lot about probability before starting this course, but I didn't know much of anything about frequentist statistics. This course helped me understand some tricky concepts.

15 janv. 2021

Brian Caffo is the best statistics teacher I have ever had. I like how he breaks down things and he covers the ways to think about statistics far beyond any course I have taken.

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1 - 25 sur 88 Avis pour Biostatistique mathématique Cours intensif 1

par Omar M B

29 janv. 2017

For prospective students who are looking to enter the biostatistics/epidemiology field in the future. This course is designed as an in depth fundamentals of biostatistics where Professor Brain Caffo dives deep into some of the key formulas, origins of statistical formulas, and theoretical aspects of statistics.

Great lecture with valuable information, however, due to the lack of engagement within the lectures, it absolutely leave students who have no background of Calculus or Linear Algebra in the dust with no reference to assist them in the course. Once or if a student has a solid foundation for the topics covered in this course, this information is very insightful and understandable.

you might ask yourself, someone who doesn't have a prior knowledge of the material covered in the course if you should even take part in the course, my answer would still be yes, so long as you are willing take notes, save the videos, and return back to them at a later point once you build yourself up by studying calculus, statistics, and/or linear algebra.

par Xavier S

25 déc. 2019

Interesting topic. You just need a basic high school level mathematic background (derivative, integral, set theory) to succeed.

I learned many things and for that I am grateful and that's why I have given 3 stars, *BUT* it was suffering to follow this course due to the lack of pedagogy (my opinion). Even if the teacher apparently tried to be didactic, he failed in my opinion.

The slides are mostly text and formula, no schemes, no tables, no animations, almost entirely black and white, nothing to help visually. If you are not an auditory memory person, you are in the bad lecture. The content is easy and basic, but the way it is presented is rather harmful. Fery few examples. The homeworks and quizz are pertinent but there is not enough questions, not enough exercises to try our understanding. And the corrections are really minimalist or even inexistent. I did not catch the objectives of most of the lectures, the motivations was not relly explained neither the link between the lectures. I found that the structure was not adequate for this basic level of statistic course.

The interpretations of the claims and results are very poorly explored, that's a shame because when Brian Caffo rarely covered interpretations, it was very interesting because he gives us many details about the different way of interpretation and the strenghts and weakness of each interpretation.

In conclusion, this COULD HAVE BEEN an excellent AND pleasant course, but for that you have to consider the question "How could I understand sufficiently well and present sufficiently well my lectures and each slides and each exercise and each example and each question (... ...) such as someone that never heard about this topic and that does not have my background and experience can understand deeply what I am saying without the need of exterior help?" Especially for a MOOC.

Despite all of these points, I repeat nevertheless that I learned many things and that I am grateful for the content and the initiative and the work that was done to prepare and realize this course (I am totally aware how much work and time it takes).

par Soren S S

18 juin 2017

I'd previously done stats a few years ago, I came to this course to refresh my knowledge. Practice problems can't be done based on lecture videos alone, and take much longer to do than advertised.

par Joseph L

15 juin 2018

This is a very worthful course to SUFFER! I'd like to say, no camp, no gain. In addition to the statistics with math, I experience how important of "hang on there and never give up"!I'm going to the camp 2 to see what's gonna happen.

par Mark B

31 déc. 2016

Undoubtedly the course instructor is very knowledgeable. However I did not take away as much from this course as I would like to. It is mostly theoretical; very limited examples. I also missed the bio in biostatistics. A title like "Mathematical Statistical theory boot camp" would be better suited. I believe having a set of cases that get reused in some form over all lectures would be very beneficial for -at lease mine- understanding the topics. The form of lecturing is not using the benefits that an online platform offers. So instead of short interactive videos, this has slides with lengthy essential spoken word with it; hardly summaries.

par Mitchell L

20 juin 2016

He terribly prepared us for quizes and gave about 4 examples in the entire course. I filled a notebook with about 40 pages of notes, about 3 of which were useful for the quizes. Though content was good, but i found myself looking things up because of his somewhat neive explanations of difficult concept.

par Charles M

8 déc. 2019

Lot of material to cover obviously and bridging theory to practical skills and knowledge is a tall order. But this course finds a way to force the learner to really understand some fundamental statistical concepts through brilliantly designed, albeit very challenging, quiz questions. The lectures are as straight-forward as you could ask for without compromising the integrity of this being a purely statistical course. If you're looking to establish a basic foundation in statistics, I strongly recommend this course without reservation. Recommend students take the math requirements seriously (algebra, integral and differential calculus).

par Mengyu D

18 févr. 2017


par Chrys

11 juil. 2017

I learned a lot in the course. I'm not sure that Dr Caffo is the best explainer ever, and there could be more worked examples. Or maybe extra quizzes?

par Divij R

23 déc. 2020

No responses to the discussion forum. Looks like the prof has just left the course like an orphan, and does not take responsibility.

The only plus is, I find the assignments challenging, which really help me reinforce the concepts. No solutions though, so I'm left wondering what's the right way to do some of them.

The lectures are monotonous and simple reading from ppt. Would recommend ONLY if you're looking for challenging assignments.

par Tarun

3 sept. 2020

Excellent course. I thank Prof Caffo for creating this content rich course and explaining it in an utmost lucid manner, that even person with average mathematical background could also grasp the idea without much difficulty.

par Phillip A B

11 janv. 2016

Very concise, well-presented course. This was my second time taking it as a refresher. Prof. Caffo does a great job presenting the materials. However, prepare to be challenged.

par Jeremy B

9 juil. 2017

Great course, though a little difficult in parts, particularly the first week. Worth working through though for a better understanding of probability and statistics.

par Alexander K

15 mai 2021

Powerpoint style is suboptimal; it presents a weird problem for the student where it' s more important to listen to what the speaker is saying or to first read what's on the slide. (Sure, I can pause the video, but that interrupts the "flow" in a sense).

Content is great and gives a good sense of how the statistical techniques could be useful in practice. However, in some cases, there is some lack of depth, especially concerning some of the mathematics, (even more particularly, the section on log-normals). I would have liked to see more fully worked examples and not just snapshots of R code which provide some solutions; i.e. it'd be nice to have the thinking broken down instead of presented in one chunk.

Overall, Brian is engaging, and I am very thankful he put this together; I found it very helpful.

par Marco C

31 août 2017

The content is in-depth and the instructor is knowledgeable, but the quiz demands a quite wide knowledge base and does not provide feedback.

par KJ B

27 juil. 2017

There are few quizzes to test skills, and lectures are not interactive. There are better course on this site for statistics learning.

par vibha h

17 juil. 2019

Lectures are a bit confusing. I watched youtube videos for better explanations.

par Pedro R S d C

18 mars 2021

Very well balanced between mathematical reasoning and applications to analysis. It needs knowledge on algebra and rules of differentiating and integrating (go do some other mooc courses, or khan academy, if you lack these skills, and then come right back to it). Linear algebra I would say is not much of a requirement, neither R, just be prepared to get a little lost in some slides, but nothing you would really need for you to absorb the main content. The 1st assignment is the one actually quite challenging, but persevere through this one and you surely can complete the course, because it gets quite easier afterwards.

par Stephanie L S

22 oct. 2021

T​his is indeed an advanced course, but as long as you study it with the right method, you could get through it (and obtain not-so-introductory level of knowledge). Just as everyone says in the review, you need to write down the notes. Personally, I also tried to do the examples given in the lectures before the solution is given, so I have a chance to try to apply the concepts on my own. That really helped me with the quizzes.

Additionally, you need to be comfortable with single variable calculus (especially derivatives and integrals) before starting on this course.

par Mihnea T

6 janv. 2018

Great course, insuring the basics for statistical inference. Well thought explanations and a good examples. Does get a bit hard to follow with Lecture 14 (last one). Practice exercises and homework are great for applying the covered concepts. Pity there isn't much activity on the forum. Still, this course is a must if you want to have the stats ABC in order and move forward to other data science topics in full understanding of what's happening behind the scenes.

par Mikolaj K

2 janv. 2019

Very thorough introduction to statistics (Definitely more generic than just "bio"). Some of the homeworks are very mathematical, but the main body of work is understandable without too much of mathematical effort. Extremely well paced and self-contained which is a signature of the best coursers materials.

Good starting point for all scientist eager to truly understand the tools we all use. I'm looking now for a follow-up course from professor Brian.

par Sven S

1 mai 2021

Great, concise introduction to probability and statistical testing. Be prepared to solve some integrals. Course material is pretty smooth though sometimes smaller errors are not corrected. Questions in the quizzes sometimes refer to notation or material covered in the next part. This can be a bit annoying. B. Caffo sometimes gives lengthy background motivation and then really quickly almost glosses over the math.

par Jingkai Z

3 déc. 2021

This course is great. It covers many mathematical statistics principles required by biostatistics, with moderate difficulty. This is not a course for people without foundation. Its curriculum is more suitable for students who have a certain foundation in statistics and mathematics and want to start biostatistics learning, deeply understand more topics or do some reviews about the basic statistics knowledges.

par Francisco G S

24 janv. 2021

Brian is an incredible professor. Really try to convey topics I was struggling to understand in an approachable manner. You will get a much better intuition of sample versus population parameters, confidence intervals and much more. Different methods for statistics (frequentist, likelihoods and bayesian) . I will move on to part 2

par Brett B

27 mai 2018

Does this really need to be called mathematical BIOstatistics? The content itself is regular mathematical statistics, just with examples from biology. Add in a couple of examples from elsewhere and the scope of the class is broadened. Still, as someone with a STEM background but not in biology, I found this course to be excellent.