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Inférence statistique, Université Johns-Hopkins

4.2
3,027 notes
612 avis

À propos de ce 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

par 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 .

par 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.

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579 avis

par Matthew Stetz

Feb 10, 2019

Excellent course if you have some background in math or stats already. This course might be difficult if you don't have that background. The peer graded assignment does a good job tying everything together in my opinion.

par Chadrick A Eakin

Feb 04, 2019

The course contains a lot I want to learn, but as someone with a limited background in statistics - I found many of the lectures not to provide clear explanations for concepts. I had to use a lot of outside material to try to learn and understand the concepts. The course lectures seem incomplete to me.

par Colin Bissegger

Feb 02, 2019

Teacher is a bit erratic. It makes the course hard to follow.

par Don Moffatt

Feb 01, 2019

This is an excellent course, though it is fast-paced. I didn't have time to watch the lectures and also do the practice exercises in Swirl in the time allotted. As usual, the time estimates for completion are wonky. I ended up just watching the lectures and taking the tests, which is far from ideal (I am taking some time to do those valuable exercises now that the course is done). Although I got 100% in the course, I felt the learning experience could have been better as a result.

par Sven Kunsing

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.

par Katakam Sai Teja

Jan 29, 2019

no clarity in the explaination

par Savitri

Jan 29, 2019

Good Course to Learn the statistical Inference

par Ajit Sharma

Jan 26, 2019

The coverage of Confidence Interval and power of a test was really helpful. I'd recommend to all interested in analytics.

par LIWANGZHI

Jan 18, 2019

this course really provides me a insight into statistical inference. Thanks, Brian!

par Raul Martinez

Jan 16, 2019

This course should be targeted for Data Scientists, in my opinion it is more for statisticians.

Too much about the insight of statistics and some but not enough about how to use the statistic tools.

Some time the professor seems like he is just reading the slides which I think it doesn't intensive the student.