Retour à Inférence statistique

4.2

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3,617 évaluations

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700 avis

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....

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 .

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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par Do H L

•Jun 17, 2016

This course is tough, informative. Good for people who want a summary of all the statistical concepts you can use for data science. You'll get the most out of this course not by expecting it to be beginner, because it is not. This course is best supplemented by having background knowledge in statistics. Meaning, learners would be much better off if he/she did some statistical course before. This course will provide the practical experience of implementing statistical concepts in R.

par Christopher C

•Mar 09, 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 Boris K

•Oct 13, 2019

This is so far the most difficult course in the specialization, but also the most useful. In this course you are taught to think like a scientist, to test hypothesis and provide evidence for your analysis. The lectures are succint and clear, the quizzes are clever and useful and the final project will make you create a very beautiful report while doing scientific work, which is the reason I started studying data science in the first place!

par Angela W

•Oct 19, 2017

I really liked this course, especially the course project at the end - the second part felt like (a really simplified version of) a task one might actually have to do as a data scientist, and I liked that through this course and the previous ones, I knew exactly what I had to do. The course itself is pretty mathematical and I think intellectually the most challenging so far, especially since it's a lot of content for 4 weeks.

par Kaie K

•Jan 16, 2016

Even as a mathematician I found it super useful to participate this class. I have learned similar material in an undergrad course, but I forgot most of it. In fact this course is so much better than the undergrad course I took, because quizzes and the project help me to learn the material by practical exercises. I am really thankful for the Data Science team for this course and all the Data Science Specialization!

par Lloyd N

•Jun 05, 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

•Oct 04, 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

•Jan 02, 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 Jose A R N

•Mar 31, 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

•Jan 27, 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

•Dec 29, 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

•Feb 11, 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

•Apr 16, 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

•Feb 21, 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

•Dec 12, 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

•Aug 27, 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

•Dec 04, 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

•Jul 17, 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

•Jan 14, 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

•Dec 01, 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

•May 27, 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).

par BALSHER S

•Feb 03, 2017

This is a good course to set up for further learning. One gets exposure to topics in intro and intermediate statistic and starts to grasp how intricate the web of statistics it all the while the focus is on Hypothesis testing which is one cornerstones of statistics.

par Craig L

•Dec 05, 2016

This is the toughest content yet of the Data Science specialisation but probably the most valuable piece so far. Video content is good but moves along very quickly so finding another book on statistics to back up the course content will be a great benefit.

par Greg A

•Feb 22, 2018

Very good course, but definitely a challenge. There is no shame in watching some of these lectures multiple times. I would recommend taking all of these quizzes until you can get 100%. It will help you out a lot in the regression and machine learning

par Nino P

•May 24, 2019

It's basically introduction to statistics. I have taken them as part of my education so it was a bit easier for me, but I think somebody new to this can lear a lot. It's a bit harder than first 5 courses, but still important and well teached.

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