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.

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- Week 1 -
**Week 1: Probability & Expected Values**

This week, we'll focus on the fundamentals including probability, random variables, expectations and more. - Week 2 -
**Week 2: Variability, Distribution, & Asymptotics**

We're going to tackle variability, distributions, limits, and confidence intervals. - Week 3 -
**Week: Intervals, Testing, & Pvalues**

We will be taking a look at intervals, testing, and pvalues in this lesson. - Week 4 -
**Week 4: Power, Bootstrapping, & Permutation Tests**

We will begin looking into power, bootstrapping, and permutation tests.

Coursera est une entreprise numérique proposant des formation en ligne ouverte à tous fondée par les professeurs d'informatique Andrew Ng et Daphne Koller de l'université Stanford, située à Mountain View, Californie.

Ce qui la différencie le plus des autres plateformes MOOC, c'est qu'elle travaille qu'avec les meilleures universités et organisations mondiales et diffuse leurs contenus sur le web.

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3 / 5

Terminé

The lectures are really hard to understand, while the material itself is really not that hard. The lecturer talks as if he is just reminding us everything we've already learned. Had to go to other MOOC (specifically Khan Academy) to obtain proper understanding of the topic.

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3 / 5

Terminé

i belive this course should be taught in 6 weeks at least and not 4 . There are multiple areas which needa deep dive. with the month based subscription it is very difficult to deep dive

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3 / 5

Terminé

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.

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5 / 5

Terminé

After many years had passed since my last encounter with statistics this course proved to be quite some work to complete. Nevertheless, still a great course and definitely worth your while.

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1 / 5

Terminé

The teacher does not explain clearly the concepts. I will not recommend this course to somebody interested in the subject as it is not effective to learn.

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5 / 5

Terminé

If you work through all the examples, you will be pleasantly surprised. This is an awesome course. Highly recommended. Many thanks to Brian Caffo for improving my understanding.

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3 / 5

Terminé

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.

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2 / 5

Terminé

True the content is rich, but the instructor is not engaging and much content is not well explained so the learner should search everywhere. If it is to compare with khan academy videos for example, they are much more coherent and way too easier to understand

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3 / 5

Terminé

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.

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1 / 5

Terminé

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.

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