Retour à Inférence statistique

étoiles

4,314 évaluations

•

870 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 Philip K

•27 janv. 2016

Very disappointed with how the transition from the old Coursera platform to the new platform has been handled: lots of instances of the "see lecture X" in the quizzes where the reference is now just wrong because the lectures got renumbered, an almost complete lack of community TA/mentors, and no explanations from anyone as to how the new platform works.

Perhaps the worst of all has been the almost complete lack of acknowledgement of any problems from the folks at JHU. This feels like it's just been dumped on the students without any real testing or any appropriate resources to sort out any problems.

par Chris M

•17 avr. 2016

Content covered in this course was hard to learn, both because it was pitched at a level that realistically was more akin to a wrap up of content already covered (when in fact it was all new content) and because the instructor, Brian Caffo, has not a style that was conducive to teaching.

The instructor often would launch into a topic, and then speed through a calculation with basically no explanation.

In terms of time, this was one of the most intensive courses in the specialisation, and I'd recommend taking this course alone (not concurrently with other courses) for that very reason.

par Habib T

•12 mars 2016

This is 3rd time I a trying this course. Labeling someone just reading the slides out loud as a course is ridiculous. I have to express that this is horrible, Please don't callout a course. Call it Audio Slides.

I have a Master's degree in engineering and have won scholarship all my life. This is the first time I am trying out on-line course. The courses were okay till I came to this sections mostly done by Brian Jaffe. Knowing and teaching is two different things, Brian! I will continue, with help from other materials outside the course. But I have ti rate this as 1 star.

par Cristiano S d A

•20 mars 2016

The worst professor in this specialization. The subject really interesting, and I have been studying for a while in my Master's and PhD in engineering, so I could understand the bulk of the course. This is a very important subject in data analysis and these poor explained classes could make lot of people give up the specialization. Statistics involves much of mathematics and calculus which make it a natural challenge for most of the people. Please, improve these classes in order not to disappoint the student who want to become data scientists.

par George C

•18 mars 2018

I found the lectures to be very lacking. The lecturer seems to make too many assumptions on what the student knows. The pacing is off on what is important to know, and what isn't. There should be more examples on how the information can be utilized in R. The quizzes should be restructured to require writing some form of R script to solve the problem. The swirl exercises don't help either. Furthermore, I was hoping that there would be more depth on how this may be utilized in a real world setting.

par Ben K

•27 juin 2016

The lectures for this class are incredibly weak. Later lectures by the same professor are reasonable and decently structured. These lectures need to be redone. The quizzes are either out-of-order or expect you to do a lot of research on your own beyond the class notes and topics. The class project is unbelievably simple, and the final metric for the class project includes duplication and one portion of the grade assigned simply if you feel the person you're grading "tried".

par Nitin K

•21 nov. 2017

I am finding this course to have a flavor where the material written on the slides are just read out loud. The content doesn't seem interesting. I was determined to complete the Specialization but I am leaving it as, unfortunately, I am feeling sleepy just by listening to the course material. This was not the case at all before taking this course. I hope the teaching methodology can be enhanced to make it more engaging. Thanks.

par Karin K

•7 déc. 2020

The lectures were largely incomprehensible, even though I have a maths background, albeit some years ago. I used the textbook (on Leanpub) as the Syllabus and sought other books/websites, in particular OpenStats. The assignment was challenging initially, but once I'd done it (and got full marks!) I really had learned something. Easily the most frustrating and hardest course of the Data Specialisation so far.

par Rich

•4 avr. 2016

Many videos lacked associated pdf slides so confusing to watch. Some topics on slides were not covered in videos. A supplemental video for those would be great even of optional.

Brian Cato is a good presenter, however, more examples needed to be done showing how to work out various statistical problems both by traditional method and using R.

par Pankaj K

•17 janv. 2016

Explanations not clear and feels like he's reading rather than explaining things.

Consecutive videos feel like they are disconnected. Videos stop in the middle of him talking something. Thank god for the swirl assignments which make things much clearer!

Also the course proceed very fast not giving enough time to the concepts.

par Miguel A

•6 févr. 2021

Si estás haciendo las especialización de Data Science, te enfrentas a un módulo "duro". La filosofía de enseñanza del Prof Caffo dista mucho de la del Prof Peng (curso más amenos). Hay mucho que mejorar en el materíal del curso, a veces resulta dificil de comprender el objetivo de las explicaciones del profesor.

par Nick H

•18 sept. 2017

Very hard to follow, many of the maths symbols are not well explained. To little time spent on each concept and many concepts don't get a proper explanation. I had to re-learn almost every concept externally as I learnt little from the videos. Some of the lessons on khan academy are much easier to follow.

par Beverly A

•4 déc. 2016

I've not found this course organized pedagogically speaking. The organization, insofar as "here is a list of stuff I'll be teaching" makes sense. But a lot of the "teaching" is ill suited for someone looking to learn with a minimal statistical background. It's incredibly frustrating and disheartening.

par James T

•26 juil. 2016

Terrible instruction in the videos and unclear directions. I'd avoid this course if possible, but it's required for the specialization. New videos should be shot and inspiration taken from more instructive and interesting guides, like The Cartoon Guide to Statistics by Gonick & Smith.

par Robert O

•6 mars 2016

I really wanted to learn this stuff. I have almost no background in statistics. But the lectures didn't cover stuff with enough rigor and repetition for me to pick up much.

So I pretty much gamed the quizzes and project enough to get through the class. Rather disappointing.

par Donald R

•17 déc. 2016

Covers basic statistics (Mean, Variance, Simple Z and T tests). Not what I was expecting as Inference. Multiple testing of data is a sign of poor experimental design and should be avoided, not adjusted for. Bootstrapping is not a new idea and has been used in other fields.

par Nikolai A

•21 nov. 2017

Focused little on the programming side of probability, and the explanations of the material were so vague and assumed you knew so much about probability already, that I ended up using my college notes for the quizzes and projects more than the actual lecture video notes.

par Peihuan M

•27 juil. 2020

It is very hard for people with no previous statistics background to follow. Some key concepts are just one sentence mentioned in passing. I have to go on YouTube to learn first and come back. Then I will understand some of the things the instructors were talking about.

par Gennadiy R

•18 avr. 2016

Do NOT recommend. Very poorly explained. Refers to some concepts without introducing, introduces others without explaining. Spends most of the time looking to the computer screen instead of toward you and reading the text instead of explaining in understandable manner.

par Liew W P

•23 août 2016

Personally, I feel that the way of sharing and teaching was assuming everyone is an expert in this subject. This course need to be reviewed to make it more suitable to various groups of profession as not everyone has the same level of mathematics background.

par Jeffrey G

•16 août 2017

In general, I'm really excited about statistics, there are so many interesting and cool problems to work on, and yet, here I am, forced to do an analysis of, "The Effect of Vitamin C on Tooth Growth in Guinea Pigs."I can't make this shit up...

par Deas R

•15 févr. 2016

Seeking out supplemental material is not only helpful, but necessary. This course does not provide a foundation of knowledge for inferential statistics so much as present several equations and functions and describe how one might use them.

par Sharon E

•21 juil. 2020

This course starts with the assumption that you understand all of the terminology covered and that you already have a thorough understanding of statistics and inferences. I'm sorry - if I knew all that I wouldn't be taking this module.

par Paul K

•28 mars 2017

Poor quality audio. Monotonous lecture style with frequent inaccuracies in delivery (says one thing but actually means another). I found my motivation and performance dropped sharply in contrast to prior lectures in this series.

par raunak r

•25 déc. 2016

Very disappointing!!!!!!!too much emphasis on mathematical notations....where are the practical applications?lack of examples....it seemed more of a prose than a lecture....would not recommend to a beginner.Period

- 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