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

4.2
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4,094 évaluations
814 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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101 - 125 sur 783 Avis pour Inférence statistique

par Mark F

6 juin 2018

Loved the course, also very pleased that there was recommended reading for further study. Also loved Brian Caffo's deadpan joke delivery, really hard to know if that's an act ;)

par Yatin M

3 déc. 2017

If you work through all the examples, you will be pleasantly surprised. This is an awesome course. Highly recommended. Many thanks to Brian Caffo for improving my understanding.

par Nils R

28 oct. 2020

I think that this course is great. I particular like the practical exercises ; which gives hand on experience with the bit complex theory. I could miss more training quizzes.

par Pranay R

3 juin 2019

A very conceptual course to understand the fundamentals of Inferential Statistics. I would recommend this course to all aspiring data analysts/scientists or business analysts.

par João F

14 mai 2018

Very intensive and demanding course with interesting examples. Students without previous knowledge in statistics will likely need additional resources to complete the course.

par marcelo G

14 août 2016

Outstanding material. You can scale the difficulty and depth on the subject as you wish. Great source and references. (Recommend seeing the videos at 1.5 x speed though).

par Deleted A

7 déc. 2018

The course was quite technical and difficult, but the lectures of the teacher helps to understand the main points and reading the ebook of the course helps a lot as well.

par Vidar I

24 oct. 2016

Great course ! Important for those who are either going to take the Regression analysis or those who are working with data and want to do same basic hypothesis testing.

par Diego S

2 juin 2020

It's good as a reminder course, but I recommend coming with some prior knowledge.

My recommendation to the instructors, update the course material, at least the videos.

par Vijay B

2 nov. 2018

More practical exercises with R (like a pre-exam of examples with exercices) would give more opportunities to practice and understand the matter (R implementation).

par Sophia H

11 oct. 2020

Many thanks for this course! I learned a lot of things that i've never imagined that i would manage to learn! thanks for everyone who makes this become possible!

par Claudia D

9 mars 2018

This course was very helpful to remember so concepts of statistical inference. The swirl and project exercises helped me to practice more my R-programming skills

par Vamshi D

1 nov. 2016

Understanding statistics is not easy, but caffo makes us to understand them easily... Thank you very much Mr. Caffo Now i can say that i know some statistics.

par Harris P

22 nov. 2016

I thoroughly enjoyed the swirl mode of interactive learning provided in the course. It helped me in understanding the power concept in statistical inference.

par Filipe M d L

13 sept. 2020

I already had this course in my university, and this course helped me a lot to understand some concepts that I didn't fully understand in that course.

par Nathan M

11 juin 2016

Great class; very informative! I was surprised to see that Brian mentioned the Central Limit Theorem; he definitely knows what he is talking about.

par Hua S

22 nov. 2020

I learned this course two years ago and now I'm coming back to retake again, this type of knowledge never expires! Please don't remove this course!

par Gayathri N

27 juil. 2020

Excellent material and illustrations by Brian Caffo. definitely enjoyed it, though mastery will be possible only if I keep refreshing my basics

par Arun V K

26 oct. 2017

Can improve the intonation and content delivery. sentences are too complex to comprehend and lost motivation to complete the course some times,

par Rumian R

23 août 2020

Worth taking twice. These are rudimentary concepts that even seasoned statisticians need to keep in the forefront of their mind at all times.

par sneha

2 juil. 2018

best course to learn and understand deeply about statistical inference thank you Mr Jeff for giving everyone opportunity to learn this course

par Andrey V

10 mars 2017

Statistical inference is one of the most useful things in data analysis.

It was very interesting and useful course!!! Many thanks to authors!

par Pavel T

23 janv. 2017

This course is exciting opportunity to "connect the dots" in introductory statistics. It is challenging, but very informative and engaging.

par Joonas S

29 mai 2017

Really good course when combined with the e-book and exercises in that. Gives a really solid foundation for modelling at later stages.

par Joaquin T

17 juin 2017

Extremely important subject within the specialization. Instructor is precise and articulate in his excellent delivery of the material.