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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1 / 5

Terminé

If you have taken Statistics before it MAY help you move through this course and meet deadlines, or you will need to set aside a VERY large amount of time daily to learn Inferential Statistics and then come back and take this course. The forums are a NECESSARY supplement to understanding the details of the project assignments and quizzes. At times my questions have gone unanswered though, so YMMV. May the odds be ever in your favor.

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5 / 5

Terminé

The course was at times difficult, I found that extra research was needed to fully understand what was going on. The extra questions related to the homework questions are a great way to test your understanding of the class.

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3 / 5

Terminé

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.

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1 / 5

Terminé

The lectures are very theoretic with a few practical examples. The only way I could finish up the course and understand the

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2 / 5

Terminé

I found that the materials given or the lectures never allow you to clearly follow a structure. I understand that are so many contents to present, but jumping around from one to another is not the way.Quite frequently a lot of the slides are just useless. Not all of us have the time to go behind every mathematics, so I would like to see more real examples of how to use the contents you teach us, than knowing all the mathematics and have a lot of slides to show how to deduct mathematically the probability of something to happen. But might be my opinion because I had other expectations.

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5 / 5

Terminé

Very good course, but definitely a challenge. There is no shame in watching some of these lectures multiple times. I would recommend taking all of these quizzes until you can get 100%. It will help you out a lot in the regression and machine learning

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3 / 5

Terminé

Good material but the lectures are not well put together for the novice. I think the professor needs to have a little more empathy for the students and not just read notes.

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3 / 5

Terminé

The subject of the course is very interesting and the professor is very competent. I had the feeling that some subjects were explained in a way that is not very convenient for someone coming from a non-statistical background.

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4 / 5

Terminé

I have done PhD level statistics courses before, but found that they either went too deep into theoretical mathematics as to completely loose the audience (at least me), or to not even try explaining what is going on under the hood of R or SPSS. What I really like about this cource is it pushed me to do calculations by hand, which really helped me understand the concepts. Dr Caffo is clearly a skilled statistician and the course is at its best when he goes off script (at least off slides) to explain and illustrate concepts. Minus one star because unfortunately the presentation of the material is uneven and some times distracting, e.g. talking very fast.

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4 / 5

Terminé

Nice course with an appropriate level covered for the data science specialisation
(assuming people taking these courses very have different prior knowledge of
statistics). It would however be good to add a second statistics course to the
stream with some more advanced topics. Yet, it is still one of the harder
courses of the specialisation.The only big criticism I have is that the course feels a lot less polished
than other parts of the specialisation. It feels like cut and pasted parts of other
courses added into one course than its own entity.

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