Retour à Inférence statistique

4.2

3,145 notes

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626 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....

par JA

•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 .

par AP

•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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594 avis

par Jason Dyer

•Apr 24, 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 Bruno Rafael de Carvalho Santos

•Apr 20, 2019

this course is excellent as it is hard if do not have a good base on statistics

par Gabriel Grados

•Apr 17, 2019

Very good course, but requires a basic understanding of statistics.

par Robert Knights

•Apr 16, 2019

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

par Thej Kiran Ravichandran

•Apr 14, 2019

The hardest course I have ever taken! Very hard to follow! Spent a lot of time, trying to understnad the lectures! The final assignment was really good, it really tied everything together! But the lectures and following them was a nightmare and hard to understand! I spent 55 hrs on this particular course! and the last week 4 I spent 20 hrs on this course

par Rajit Aggarwal

•Apr 02, 2019

The course is very technical and needs a) reading and practice outside of the material presented here and, b) needs you to invest a lot more time than you might believe before you start this course. So if you are looking to just understand the basics of statistical inference or if you don't have a background in statistics then this is best avoided.

par Marc Torrey

•Apr 01, 2019

Not only did this course help me to understand concepts that I have encountered in my job over the length of my career, but it also introduced me to using R Markdown, which will come in handy for future projects.

par Nik Muhammad Naim Nik Ghazali

•Mar 27, 2019

Overall, this is a good course to learn statistics. The quality of the video could have been better but the stuff presented is still clear to me.

par Paul Ringsted

•Mar 13, 2019

Relatively, this is one of the best courses and lecturers of the specialization, Brian delivers clear, thorough and well-paced lectures. These lectures on statistics, regression and machine learning are where the rubber hits the road after a lot of prep work to learn R and principles/tools of data science taught in earlier classes.

par Shimon Yannay

•Mar 10, 2019

very unclear and monotonic lectures

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