Retour à Inférence statistique

4.2

3,205 notes

•

633 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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By Pinar T

•Jan 11, 2019

When you start statistics with practical examples, people tend to presume certain things (e.g. independence is given like in munchausen example) so I sort of understand this desire to keep every definition abstract/pure for solid foundations but damn, this course goes way too far. I took statistics at uni and this was a refresher on the specialisation track but the way Bayes' rule was covered made me doubt what I knew. Oh also, the instructor does some of my biggest pet peeves which are (1) using his preferred notations without actually reading it out loud first (2) unnecessary use of synonyms which just distract me from what he actually means (3) reading from slides without any context as to how these concepts are used.

Also, the concepts are elaborated on a seemingly random basis. Mean is left at "center of mass" like we just came out of physics 101 but the area under a curve is dragged out with random wood cutting analogies. I am just surprised at how all over the place this course is so far. Anyone starting from scratch, I highly recommend probability and statistics reading and some basic calculus elsewhere first. Otherwise you will get frustrated with this course.

By Roman

•Oct 22, 2018

This course is a fucking shitshow. Not only does Brian Caffo not explain anything, he has a tremendous gift to confuse people and make them forget / not understand anymore what they already knew. Great fucking course. Not. Hated it from the first minute.

By Deshina B B

•Nov 27, 2018

The teach methods changed too drastically starting with this course. Much more prerequisite knowledge is required then is included int his concentration course set. No foundation or warning is given regarding this change and prerequisites. I had to seek out and spend countless hours on many other learning resources to get through this course and still don't understand what this course was trying to teach.

By Rebecca K

•Sep 03, 2018

The information is so important and useful, but I found the presentation of the material to be fast and not very interesting, and therefore it was hard for me to retain the material. I learned a lot, but I would need to invest a lot more time to realllyyy grasp everything in the course. It wasn't presented in a way that made it easy to learn, so I need to spend more time going back over things to really get it.

By Yusuf E

•May 21, 2018

At this point in the specialization, I was really worn out by the effort that I needed to put into this course (I solved the homework questions too). While I have no problem with the math, some topics like power should not even have been discussed or should have just been discussed in passing. Caffo spent a whole week on that. After taking the Applied Data Science in Python Specialization, I have a feeling like this course and Regression Models can just be merged, while logistics regression could just be transferred to the machine learning course.

Fortunately, the final assignment was very easy compared to the previous courses and one could finish it reasonably in a day (Reproducible Research final assignment itself took me almost a week) .

By Robert K

•Apr 16, 2019

A lot of material to cover - can be a strain, but well explained for the most part.

By Don M

•Feb 01, 2019

This is an excellent course, though it is fast-paced. I didn't have time to watch the lectures and also do the practice exercises in Swirl in the time allotted. As usual, the time estimates for completion are wonky. I ended up just watching the lectures and taking the tests, which is far from ideal (I am taking some time to do those valuable exercises now that the course is done). Although I got 100% in the course, I felt the learning experience could have been better as a result.

By Savitri

•Jan 29, 2019

Good Course to Learn the statistical Inference

By Anthony M

•Sep 03, 2018

Poorly organized content and the lectures are presented in a confusing way. The lecturer obviously knows the material well, but is not able to present it well. He should use more sample problems annd examples. In addition, I am having trouble getting my submission graded, although I have already graded 6 fellow students.

By Michael S

•Aug 26, 2018

The material is obviously invaluable but I thought the lectures themselves were lacking.

By Dai Y

•Aug 16, 2018

There're lots of practice on manually construct statistics. I'm not sure if it's necessary to do that since we could just use R code to do it. I think how to interpret it and use these statistics in examples would be more important. There're some examples, but could be more and interpret more in depth if there were less focus on the calculation.

By Mingda W

•Jun 05, 2018

My most recent experience with statistics was about 2 years ago, and it was college level statistics. Still, I find this class is hard to keep up sometimes. In general, I felt like the professor explaining too much on the mathematical meaning behind equations instead of talking about the real-world meaning of equation components, and why those calculation make sense.

By Nina

•Jul 21, 2017

I couldn't make it through this course because I can't stand looking at the instructor's face on the screen. It is very distracting. He is also not very clear in his whiteboard explanations, too much scribbling. I prefer courses taught by Roger Peng.

By Stephen G

•Oct 25, 2016

The only reason for enrolling is to complete the data science specialization, though it may make you reconsider continuing with it. The instructor and provided materials fail to adequately explain the concepts this course is supposed to cover, and do not prepare students for the quizzes or assignments. If you don't know statistics you won't learn it here. If you know statistics, you don't need this course.

By Joshua A B

•May 29, 2016

I've taken several statistics, data science, and R courses. This is one of the worst. I took others' advice, and I also strongly suggest looking to other sources to learn Statistical Inference before taking this course. Khan Academy, DataCamp, Udacity, Duke (Coursera), and Columbia (edX) all have great courses. Though they vary in depth, each leaves you with a good understanding of the concepts they teach.

By Chunyue Z

•Jun 16, 2019

The materials are not so clear to someone who's not familiar with stat.

By Rok B

•Jun 10, 2019

Great course! Gives a really nice and comprehensive overview of basic statistics

By Jorge B S

•Jun 03, 2019

Very nice introductory course to statistical inference concepts using R.

By Pranay R

•Jun 03, 2019

A very conceptual course to understand the fundamentals of Inferential Statistics. I would recommend this course to all aspiring data analysts/scientists or business analysts.

By 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.

By 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).

By 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.

By Сетдеков К Р

•May 16, 2019

Great reminder of statistics from my masters degree. Very condensed and easy to remember.

By Andrew

•May 05, 2019

Not my favorite course in the series, but I did learn a lot. I highly recommend following along with the course book provided in the course. The videos alone are not enough. I also recommend printing out a sheet with statistical formulas to use (not provided from the course, but you can find easily on the web). The stat sheet with formula helped me connect all the dots and better understand when to use a formula.

By 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.

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