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

4.2
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4,305 évaluations
869 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
25 oct. 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
24 sept. 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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501 - 525 sur 837 Avis pour Inférence statistique

par Vincent G

14 août 2017

Very good material. I only wish the examples could be more varied to include some "business" examples as opposed to mainly bio med ones.

par Juan B M V

24 sept. 2016

Touches all the key concepts of statisticas inference. A bit challenging, even more if you don't have previous knowledge of statistics.

par Harsh G

29 mai 2020

Overall a great course to learn and understand, few concepts weren't explained properly and I faced difficulties in adapting to those.

par Scipione S

16 juin 2020

Nice Course, probably it needs more time to study in deep certain topics like tests, confidence intervals, and resampling techniques.

par Enrico D

24 avr. 2017

It is good but you have to study by yourself using different material as the video contents are not enough to understand the subject

par Tomas M

25 mars 2020

Course lectures could be a bit simpler. without that many theoretical demonstrations & more pragmatics summaries of the concepts.

par Joost L

18 oct. 2020

More difficult than the previous modules. Some explanations by the teacher seem to expect some basic knowledge on statistics.

par Salva C

7 déc. 2020

Nice course, I really enjoyed it and learnt a lot from it. These were basic notions of statistics but still very informative

par Henry L

23 juil. 2019

A very good introduction to statistical inference. Coves both data with known distributions and more unknown data handling.

par fabio a a l l

24 avr. 2017

I think the "homework" exercises are a very good idea, but

since the course is very fast paced, more exercises are needed.

par Ajit S

26 janv. 2019

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

par Lluís G

15 févr. 2018

Very good course. Maybe in some cases it misses a little bit more depth on the why or when we apply certain methods.

par James S

25 mai 2016

It was a pretty good course with really great examples. I feel the instruction was a bit too droll and mechanical.

par Stephan H

9 oct. 2017

Nice course but the estimated lengths for the assignments and swirl lessons differ from my personal time spent.

par Sandeep P

4 juin 2018

The subject is really good. It's the course content which lacks a bit, it should be enhanced and elaborated.

par Mark S

24 avr. 2018

Good explanations, but it would be better to apply them directly to the R language and understand the output

par Marco A B V

27 déc. 2020

I learned more using the companion book. I think the videos should be remade. Beyond that is a good course.

par Camilla J

9 oct. 2017

This was the only slightly boring class in the specialisation, but we did learn a lot of R programming.

par Tiziano V

27 déc. 2016

Good course. It could be more clear and deep in some concepts, especially in the final part of week 4.

par AYUSH K

5 juin 2020

everything is good. except the lectures are kind of boring. i hope lecturer may be a bit more active

par Gustavo W R

26 oct. 2018

The content is really interesting.

But the presentations (including audio and video) could be better.

par Surekha P

22 mai 2020

The course is very deep . you need to rewatch videos if you don't understand it.

anyway, its good.

par Michael H

2 juin 2018

Tough, but good information presented in a way to capitalize on R for doing statistical analysis

par Jesse L

26 août 2020

More graphic explanations would have helped me a lot. Had to do a lot of supplemental learning.

par Manh H T

14 janv. 2017

The course is useful and essential not only for those studying statistics but machine learning