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

4.2
étoiles
3,652 évaluations
715 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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501 - 525 sur 683 Avis pour Inférence statistique

par Ramy H

Oct 01, 2017

Material should be supported by more examples. ie. at the end of the course, I couldn't perform a basic statistical test.

Bootstrapping modules completely missed the context.

par César A

Nov 16, 2017

You will review basics and main statistical theories. However the course videos and explanations are not as intuitive as in the previous courses. Statistic is always tough.

par Svetoslav A

Dec 19, 2016

3.5 - Good, but I feel some of the explanations were over complicated a little compared to other coursers such as openintro to stats. Overall good experience though

par Hongzhi Z

Nov 16, 2017

整个专题里面boring的一门课之一,Brian教授的视频一直是1.25time速度看完,有些例子例如最后的Hypothesis testing 真的学得很困难,即使我在大学时候曾经上了概率统计的课,对没有数学和统计基础但想从事数据科学的人员真的是十分不友好,希望改进:1、课程视频变得有趣 2、PPT资料里面的公式详细解析

par Stefan P

Jan 30, 2016

Brian Caffo is a brilliant mind. I am sure, but in a way for me it is difficult to follow. In parallel I checked out Khan Academy and it was easier to understand.

par Fabien N

Nov 15, 2019

I find the lectures sometimes not clear enough to answer the quizzes questions. On the other hand, the course provides material in many ways, which is very nice.

par Asier

Mar 11, 2016

At times the content can be confusing. Some points are clearly explained. "Data Analysys Tools" course is a good complement in order to understand the subject.

par Talant R

Aug 27, 2016

Covers a lot of info too fast! Some concepts are not clearly explained , had to surf online to get better understanding. Overall, fine course, very practical.

par Yadder A G

Jan 25, 2018

I didn't like the way how the professor explained the topics. It was difficult to understand him. I just understood when I did the swirl activities.

par Diego T B

Dec 04, 2017

Very useful but too many concepts. It was hard to follow him during 20 minutes. Videos are very extensive, also useful. But take into account this.

par dhaval s

Feb 21, 2017

Indept videos and materials should be provided for this course. The lectures are not enough to understand the Statistics involved in data science.

par Chouaieb N

Nov 11, 2019

The course content is very interesting and sums up fundamental aspects of statistical inference. But the way the course is presented is average.

par Aaron S

May 07, 2018

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ts just to have a chance of passing this one.

par Mohammed A E M

Jul 17, 2017

the subject kinda not hard but not easy to understand also, how ever the instractor was kinda fast which made me lake some of the information.

par Ivan G

Jan 08, 2017

To be honest, I like the subject but found the course material and content not very well structured. I missed more mathematical foundation.

par Pawel D

Dec 03, 2016

Brian Caffo is explaining the Statistical Inference methodically, but he could work on making the lectures less tiresome and monotonous.

par Ramon S

May 19, 2017

Not really a logical path to follow. Too much topics for me. I really needed more examples with code.

Thanks a lot for the lessons!

par Naeem K

Aug 08, 2016

The amount of materials is more than course period. You may need to study a couple of other resources to understand the course.

par Hernan S

Apr 16, 2016

The subject is interesting, but the explanations are a little confusing. May need more diverse real-life examples to relate.

par Masahiro H

Mar 27, 2016

it gives an idea of how one is prepared to ingress to Data Science. I

see that I need to review it more carefully later on.

par Stavros S

Nov 21, 2019

Weeks 3 and 4 should have been split into 2 extra weeks to explain the concepts deeper and also have more exercises

par Marcus H Y T

Jun 02, 2019

Concepts are not well explained and slides are not well prepared. Last few topics are too brief to be useful.

par Maria C I S

May 02, 2019

3 stars because a total beginner would not have been able to follow these lessons without a lot of rewinding.

par Joe F

Nov 27, 2016

Materials need to be updated - there are way too many inconsistencies between videos, exercises, and slides.

par Vasudevan D

Jan 28, 2018

Too much concepts to learn and practice. Course material can be little more engaging and split accordingly.