Retour à Inférence statistique

étoiles

4,341 évaluations

•

878 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....

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!

Filtrer par :

par Johann R

•17 juil. 2017

The content is what you would expect for this subject, but it is not quite presented in a logical and ordered way. The lecturer's style is also very uncomfortable, especially in the first week or two, where it feels like the content is just read (and fast), and not explained on a level expected for a course having no prerequisites. If students don't have any previous statistics experience or knowledge, they would find some of the concepts very difficult, especially as presented in this course, as it appears that the assumption is made that students have a certain level of statistics knowledge already.

I have done the Basic Statistics course on Coursera (University of Amsterdam) and that course takes a more methodical and logical approach to the basic concepts, and if I hadn't done that course already I would have really struggled with grasping the concepts explained in this course. Even having done the Basic Statistics course I struggled anyways, and had to resort to additional information like Statistics for Dummies and various other internet / YouTube videos for more methodical and clear explanations.

par Normand D

•29 janv. 2016

This is a great course taught by a clever teacher but...

The content is presented in a very dry, not easy to grasp, manner. In several cases, I had to use external sources to understand the content and/or derive it by myself. When I finally understood the content I couldn't understand why it is presented in such a cryptic manner when the concepts are rather simple to grasp and the math not so advanced.

Professor Caffo is a good communicator in some occasion (the module on Power for example was incredibly well communicated). But most of the time he just throw us some result without properly setting the context and concepts, as if it was understood that we already know most of what he is talking about. (Not the case!)

I plan to make a document that follows the course module and fill in the missing piece of contextual information, derivations and concepts. But this takes a lot of time. If/when it will be completed, I will try to find a way to share it with future generation of students. Because, honestly, the content of this course is not so hard and shouldn't be!

par Stefan L

•29 août 2016

As someone who's new to the world of data science and doesn't have a university degree this course was very hard to get a good grasp on.

That's partly the "cause" of how the course was taught which was assuming you had all the knowledge at hand of all the stuff Statistical Inference is about.

For people that are starting this stuff it might be nice to have a introductory course of Statistical Inference as I did not finish this course by just watching the course video's and additional information, I had to look up additional resources which explained the material better.

Still, a big thank you for explaining statistical inference and opening my eyes regarding this topic, it surely helped getting me to the next step in what Data Science is all about and makes it ever more interesting!

par Lee G

•8 janv. 2017

The course is a very quick run through of basic statistics and not very intuitive for people without much statistics/maths background. The swirl exercises is a very good practical learning tutorial that supplements the course, but overall it still lacks on the conceptual aspect. Personally, I have to occasionally refer to other basic statistics materials to be able to follow the flow and understand the lectures.

For the course project, there is a huge discrepancy in what the project expect the students to perform and the peer grading criteria. As a basic statistic course, the correctness of the estimation/ calculation/ assumptions is integral in any analysis but the grading criteria mostly neglect all this aspect. Hopefully the course admin can rectify this aspect of the course.

par Jan K

•7 mars 2017

This is of course my personal opinion, with all due respect for the Tutors. Plus, it has to be noted that I am writing this as a Mathematics graduate, and this course was most probably not meant for people with any background. However, I have seen similar opinions from people like me. Probability calculus and statistics are both enormous areas of mathematics. Introducing them in a 4-week course seems a really bad idea to me. The probability part was in my opinion far better than the statistical, the origin of every new concept was clear. In my opinion, the optimal solution for the course would be to create a separate, longer course in PC and stats and require knowledge of the two for taking Data Science Specialization.

par Marcelo S

•28 févr. 2018

The course is not meant for beginners, but seems to be advertised as such. Knowledge of Elementary Statistics is a must. The course is fast-paced and most people would not be able to finish it in 4 weeks or understand all the concepts in the course without outside help. Use of Discussion Forums and Mentors such as Leonard Greski is invaluable for completing the course successfully. There are several minor flaws in the videos and textbook that need to be addressed. This course would be much better off broken into two (Elementary + Inferential Statistics) and buffered with longer videos and step-by-step instruction and help.

par Huang-Hsiang C

•3 juil. 2020

There is no doubt that topics covered in the course are fundamental and critical. However, instructors rushed through most topics and explain them in a not very intuitive way. To be fair, the "power" section in the course is actually pretty organized. Swirl exercises can improve your understanding to some degree, make sure to take them.

This is definitely not the 1st course if you are completely new to statistics. I'd suggest taking other similar Coursera courses or reading articles from different resources (e.g. http://www.sthda.com/english/) to better internalize all concepts.

par Andrew W

•25 janv. 2018

A topic such as statistical inference is not complicated, and could be taught in a much more straight forward and comprehendible fashion. Just look at the tons of material and (good old fashion books) that relate this material in a much more concise manner. Moreover, the material in this class including the R-files are not well synchronized (gives low quality impression). A lot of time is needed to sort out the documentation between R-files, the book (Statistical Inference for Data Science) and the slides. I find many errors and sometimes inconsistent notation.

par Olivia U

•29 mai 2020

I have mixed feelings about this class. We are rushed through the concepts, I had to study a lot on my own to deeply understand the mechanisms - lucky for me I studied advanced mathematics in College, I mostly had to revive my memories. The video lectures are of very average quality, but the practical exercises in swirl helped a lot. Just the one final project is not enough imho. I can't judge yet if what I learned is enough to properly apprehend the algorithms at play in ML - we'll see. All in all, not the best course so far from the specialization.

par Zhiming

•27 sept. 2017

This course covers the very important things about statistics, I totally agree with that. But I find that if Coursera can make the entire course easier to understand for the layman, it will be the best. After I took the course, I need to visit youtube to do some researches to understand the more complex stuffs like power t test. Maybe coursera should look at Khan Acedemy and see if they can get some idea from it.

I usually go to https://www.youtube.com/watch?v=uhxtUt_-GyM&list=PL1328115D3D8A2566 to look for those chapters that I need to revise.

par Amol K

•31 janv. 2016

This course goes on a very fast pace and simply does not have the charm of all the other courses in the specialization. I understand that a lot of content is covered within a month, but there should be supplementary course material available. Moreover, TAs should be more active on the forums. I have seen most of the questions just being discussed among the students. A little disappointed. Will probably have to watch all the material again to have confidence with it.

par Emre S

•23 nov. 2017

Course topics is good and heavily dive into statistical training.

I may say that there is a lot of theoretical stuff and these need to be supported by real world simple examples.

I have spent twice the time to watch the youtube videos about the classes to settle my mind and see some examples.

Course content need to revised and realistic easy to understand content including R coding should be included.

Thanks for the effort spent so far.

par Satyam S

•25 févr. 2021

I believe the theory part can be greatly improved to provide an understanding. Practical and all is good enough as someone who likes maths, I would like to see more of it in the theory classes. I did not quite understand some topics intutively for which I had to search for other materials, but swirl excercises are a big help actually. Also a big thank you to the professors/mentors who put their time and effort in this .

par A. R C

•23 août 2017

It was more difficult than I expected. Besides to imagine inside your head some of the theoretical concepts. Instead of "accept or reject", we have "reject" and "fail to reject".... just as an example :) And now there is this discussion about p-values omg....

https://www.vox.com/science-and-health/2017/7/31/16021654/p-values-statistical-significance-redefine-0005?imm_mid=0f55ac&cmp=em-data-na-na-newsltr_20170809

par Sven K

•29 janv. 2019

I think it could be taught a tad better. Maybe more explanations in lessons and a bit better (read: less vaguely) worded course project description would be useful. I do understand the importance of this part of the DS specialization, but I would have loved a bit more careful approach to the subject. It is probably hard for an expert to lower himself to this admittedly low level of knowledge, but please do try.

par Chantelle C

•9 sept. 2020

Great R material, powerpoints, and lesson materials, l however the material is extremely fast-paced. Recommend a page dedicated entirely to R formulas. This was a good refresher, but anyone who has not had at lease Stats 3 or 4 in college/graduate school should think twice before doing this program or at least have many outside hours dedicated to completing this program.

par Benjamin S

•10 déc. 2017

Caffo clearly knows his stuff. But some of the lectures start off going slow but then take a leap forward into a conceptual realm that is beyond most people if they are not at least somewhat familiar with statistical concepts. Take your time with this one and make sure to do the reading. The videos kind of cut off prematurely sometimes.

par Pedro J

•11 févr. 2016

Since it is a very theoretical subject, trying to explain it without proofs and plenty of background is hard. But i feel like most of the course is just to memorize formulas without much explanation where they come from. A few examples are computing the expectation and mean of the average distribution and computing confidence intervals.

par Jeffrey L R

•18 févr. 2021

Not my favorite course in this specialization. Very poor at developing "intuition" regarding statistical inference concepts. At many times I felt that the instructor was simply reading formulas, assuming that we already had the background. I had to go to YouTube to get real-world explanations of what different concepts meant.

par Polina

•11 mai 2018

The course covers very important topics pretty well. The instructors knows the subject, materials are well chosen. However, the lectures could be done much better. There are many typos, the instructor is reading from the slights. Isn't it worth putting a little more effort since this course is taken by the thouthands of students?

par Gianluca M

•20 oct. 2016

The course is good, but not very challenging. Anybody having done any course in statistic would have little to no information from the first two weeks. Only week 4 was interesting to me, dealing with boostrapping.

The teacher is very clear and chooses the subject in a clever way. One always understands what he or she is doing.

par Allister G A

•27 nov. 2017

Brian Caffo is an interesting lecturer - he dives into the key concepts and ideas that are essential to understanding the statistical concepts necessary to gain a better appreciation of the course. However, presentation and materials need a LOT of work. They can be too overwhelming and most of the times feel irrelevant.

par Raul M

•16 janv. 2019

This course should be targeted for Data Scientists, in my opinion it is more for statisticians.

Too much about the insight of statistics and some but not enough about how to use the statistic tools.

Some time the professor seems like he is just reading the slides which I think it doesn't intensive the student.

par Kirill K

•21 sept. 2020

So in my opinion information that ws given in this course was not exlained well, lucky for me I was just refreshing these things, so I knew where I could lok for additional explanation. But if you don't have any background in this scope, it would be rather hard to understand why given formulas are working.

par C E

•4 févr. 2019

The course contains a lot I want to learn, but as someone with a limited background in statistics - I found many of the lectures not to provide clear explanations for concepts. I had to use a lot of outside material to try to learn and understand the concepts. The course lectures seem incomplete to me.

- Analyste de données Google
- Gestion de projet Google
- Conception d'expérience utilisateur Google
- Google IT Support
- Science des données IBM
- Analyste de données d'IBM
- Analyse des données IBM avec Excel et R
- Analyste de cybersécurité d'IBM
- Ingénierie des données IBM
- Développeur(euse) Cloud Full Stack IBM
- Marketing appliqué au réseau social Facebook
- Analyse marketing sur Facebook
- Sales Development Representative Salesforce
- Opérations de ventes Salesforce
- Connaître la comptabilité sur le bout des doigts
- Préparation à la certification Google Cloud : architecte de Cloud
- Préparation à la certification Google Cloud : ingénieur(e) en données sur Cloud
- Lancez votre carrière
- Préparez-vous pour obtenir un certificat
- Faire progresser votre carrière

- cours gratuits
- Apprendre une langue
- python
- Java
- conception web
- SQL
- Cursos Gratis
- Microsoft Excel
- Gestion de projet
- Cybersécurité
- Ressources humaines
- Cours gratuits en Science de données
- parler anglais
- Rédaction de contenu
- Développement Web Full Stack
- Intelligence artificielle
- Programmation en C
- Compétences en communication
- Blockchain
- Voir tous les cours

- Compétences pour les équipes en charge de la science de données
- Prise de décisions basées sur les données
- Compétences en génie logiciel
- Compétences personnelles pour les équipes d'ingénieurs
- Compétences en gestion
- Compétences en marketing
- Compétences pour les équipes en charge des ventes
- Compétences en gestion de produits
- Compétences en finance
- Cours populaires de science des données au Royaume-Uni
- Beliebte Technologiekurse in Deutschland
- Certifications populaires en cybersécurité
- Certifications populaires en informatique
- Certifications SQL populaires
- Guide de carrière de responsable marketing
- Guide de carrière de chef de projet
- Compétences de programmation en Python
- Guide de carrière de développeur Web
- Compétences d'analyste de données
- Compétences pour un concepteur UX

- Certificats MasterTrack®
- Certificats Professionnels
- Certificats d'université
- MBA & diplômes commerciaux
- Diplômes en science des données
- Diplômes en informatique
- Diplômes en analyse des données
- Diplômes de santé publique
- Diplômes en sciences sociales
- Diplômes en gestion
- Diplômes des meilleures universités européennes
- Masters
- Licences
- Diplôme avec un Parcours de performance
- Cours de BSc
- Qu'est-ce qu'une licence ?
- Combien de temps dure un Master ?
- Un MBA en ligne vaut-il le coup ?
- 7 façons de payer ses études supérieures
- Voir tous les certificats