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

4.2
étoiles
4,341 évaluations
878 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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51 - 75 sur 846 Avis pour Inférence statistique

par Johnny C

10 mai 2018

The lessons require intermediate level in statistics and it is a complete waste of time watching the videos without doing an initial course of statistics. Thereby, It requires much more time than expected to learn the topic, which includes reviewing basic concepts and doing the (optional) assignments. Moreover, the questions in all quizzes are more than challenging very tricky.

par Jason D

24 avr. 2019

The course is poorly laid out and the concepts are poorly explained. You'll need either previous college level statistics courses or be willing to spend a lot of time outside of the class to understand what's being taught. The quizzes have little to do with what is presented in the lecture. Unless you are going for the data science certificate, I would look some place else.

par Nils H

20 mars 2021

The lecturer is talking way too fast, simply reads off the slides and doesn't dive deep into any of the concepts behind all those definitions. You won't learn anything new here! So stay away from this course if you don't really need it for the Data Science specialization. There are way better alternatives even on Coursera (e.g. Inferential Statistics by Duke University)

par HIBRAIM A P M

4 mai 2020

Los ejercicios están completamente desactualizados y no corren con versiones actuales de los programas. Es necesario que den mantenimiento a este curso, ya que los últimos comentarios que se respondieron por parte de los instructores, lo hicieron hace más de dos años.

par Zeinab B

29 mars 2021

The course is very monotonic and confusing. The lecturer literally reads the notes quickly without trying to connect to the students. It seems that the teacher is in rush to finish the video.

I do not recommend this course and I think it's a waste of time and money!

par Chris W

7 mars 2019

Not designed for people without good Stats knowledge. Formulae thrown onto the page at blistering speed. Terms and notations used that have not been defined. Course book pretty poor. I had to do another stats course elsewhere to have any chance of taking it in.

par Nelly C

13 déc. 2019

There is a lot of theory in the course but it is not always treated with the necessary rigorousness; this creates confusion and makes it difficult to understand the basic concepts.

par Alessandro F

20 mai 2020

I don't find the button to leave the course!!!!

par Christopher C

9 mars 2016

I learned so much from this course. Brian has an occasional irreverence and dry wit that keep things lively. I will say that I disagree with some of his interpretations, but this is OK!

I would like to see some integration of type s errors, capture intervals, and all the other things the cool kids are doing nowadays.

I am now taking Bayesian statistics online via Richard McElreath's course and this one does help a bit in understanding likelihood functions.

par Lloyd N

4 juin 2017

I thought most of the lessons in this lecture were enjoyable, since it went into the theory of decision-making from data. I feel you need to take an introduction to statistics course before taking this course though, since the lecturer goes too fast at times. I recommend Udacity's Intro to Statistics course, as it helped me understanding the lectures in this course. A+ material though in my opinion.

par AMIT P

3 oct. 2018

This course is one of the most difficult to comprehend, particularly if one does not have any prior knowledge of statistics and probability. But Swirl package of Statistical Inference helps a lot and is a good heuristic approach to learn.

P.S. I would recommend to read this lecture along with any textbook. I referred Probability and Statistics (Schaum Series).

par Prashanth R

2 janv. 2018

I absolutely loved this course and felt like i learned a lot about statistics. This was very informative and the peer graded assignment was a perfect way to conclude the course, by having to perform all of the phases in Data Science that I learned by taking other courses in this series. Thank you for this course! Looking forward to the next set of courses.

par José A R N

31 mars 2017

My name is Jose Antonio. I am looking for a new Data Scientist career ( https://www.linkedin.com/in/joseantonio11)

I did this course to get new knowledge about Data Science and better understand the technology and your practical applications.

The course was excellent and the classes well taught by the Teachers.

Congratulations to Coursera team and Teachers.

par chirag y

27 janv. 2016

It was a good course especially for beginners like me. Though i would advice to continuously keep digging more about other packages also and also going through stack overflow for various hurdles encountered during doing programming assignment.

I would recommend this course to everyone who wants to know about data analysis using R language in particular.

par Olga H

29 déc. 2017

Very illuminating and well taught. I think this is content every data scientist should master to begin with. I recommend following this class if you did not learn it in this way already at university, which might be the case if you are in exact sciences. And even if you did, this course might be useful to brush up your skills.

par Paul C

11 févr. 2017

Kudos to Caffo for using charts and examples to provide a lot of insight without using a lot of math. However, I would personally like the math to be presented, too (e.g., the 'off-center' T-distribution, etc.). This could be done is special sections of the book and lectures, as is done in the Regression Models class.

par Qian N

16 avr. 2017

The course materials are well designed and delivered. I have taken basic inferential statistics at various levels in the past like 5 years, this is a really nice refresh and update (with respective the use of R). I would recommend this courses taught by Dr. Brian Caffo to others who are interested in the subject.

par Max M

21 févr. 2020

Tought. Took me around 3 months to complete. I also took extra courses and bought a book to help me out on this one. Is not easy if your background in statistics is not already solid. But once you finish and you find yourself running simple statistics in R then everything is very rewarding!. Very good course!

par Saul C

12 déc. 2016

Although the instructor is very good, it would be nice to have a direct link to more references that explains the basics without skipping certain steps that a beginner may find difficult. The course is pretty good and if the student is proactive he/she will find a way to self-learn those missing steps :)

par Gopinath V

27 août 2017

I didn't find time to sit for this course as I was involved in other activities. So also whenever I get time to see the lectures, I felt I need to see the previous slides/lectures. And I did go back then and after. But the course content was good. The instructor has the command over the subject.

par Joseph M

3 déc. 2015

This is an excellent course for anyone who needs a better understanding of statistics and that includes all professions that deal with quantitative data. It helps you become a better citizen by helping you decide when something is mere chance and when mere chance would not explain the events.

par Lucia F M

17 juil. 2017

Awesome course if you need to understand the theory behind the statistical test you keep reading in scientific articles, if you wanna get the basis with which to learn more complicated regressions models, or if you have studied statistics before and forgotten most if it !

par Sanil S

14 janv. 2019

The course starts from very basic probability piece which is great for beginners and covers all relevant topics. I found that some of the topics difficult to grasp. However I did supplement this course by seeing Youtube videos from jbstatistics and Marins stat lectures.

par 李佳童

1 déc. 2015

Dividing a week's contents into modules and adding a brief introduction at the beginning of each module makes the course much more clear. Students can also know what programming assignments (swirl) they should do every week. I appreciate those changes in the new class.

par Charles M

27 mai 2019

Elegant presentation materials and contains evaluation materials that target essential concepts and learner's ability to apply course information. Very well done and looking to take the biostatistics bootcampe alluded to in the lectures, by the same professor (Caffo).