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

4.2
étoiles
3,989 évaluations
791 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

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.

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176 - 200 sur 761 Avis pour Inférence statistique

par Long T

Sep 10, 2017

Very nice and descent. The homework is especially interesting and well designed.

par danxu

May 22, 2016

Very useful !

Teacher is awesome, statement is clear and simple.

love this course!

par Bruno R S

Apr 20, 2019

this course is excellent as it is hard if do not have a good base on statistics

par Vinicio D S

Dec 22, 2017

Great course for getting an introduction into the Stats needed for Data Science

par Léa F

Nov 10, 2017

Thank you Brian !!! I learned a lot of things in a very short amount of time :)

par Peter G

Feb 04, 2016

Definitely the best and most useful course of the Data Science Specialization!

par VICTOR G C

Jun 04, 2020

It gives the idea and the tools that everybody needs to develop data science.

par Offiong E

Jun 28, 2017

very fast paced course. i totally enjoyed digging deep into my inner reserve.

par Carolina G G

Sep 23, 2019

Muy organizado, con temarios interesantes y mucha claridad en los contenidos

par Ruddy U

Jun 14, 2016

It was very interesting, descriptive and joyful. I really loved this course.

par pravin

Jul 03, 2017

Very valuable overview for all statistical analysis used in future courses.

par Marcelo A P

Aug 17, 2016

Need to improve the clarity of the lectures, but it´s a good course though.

par ONG P S

Feb 16, 2020

Very good course. A lot of examples helping me understanding the theories.

par SATHYANARAYANAN S

Sep 11, 2017

Very good for anyone wanting to get into the field of Data Science using R

par Sukanya S

Apr 06, 2018

This is the most difficult course so far as I am not a statistics student

par Illich M

Jun 16, 2017

This is a tough course! I had to take it multiple times to understand it.

par Jorge B S

Jun 03, 2019

Very nice introductory course to statistical inference concepts using R.

par Sanjeev K

Apr 02, 2018

A great course for the beginner who are new to the field of Data Science

par Huey Y T

Mar 21, 2017

I enjoyed the lectures. The lecturer was very clear on the explanations.

par Bopeng Z

Jun 08, 2017

Very useful and thorough review of statistics. Invaluable in practice.

par Simeon E

Feb 20, 2017

Not so easy, but extremely interesting. Highly recommended to anyone.

par Sebastian R

Sep 19, 2017

Great intro to statistical inference. Also, the materials are awesome

par Raunak S

Nov 01, 2018

nice course before digging deeper into Data Science advanced topics.

par Karthik R

Aug 07, 2017

Good Course, but I think you need to have some Statistics background

par Diego N

Mar 26, 2017

Excellent course to get up to speed on important statistics concepts