Retour à Inférence statistique

4.2

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3,989 évaluations

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791 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

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 .

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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par 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.

par 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.

par Moshe P

•Sep 24, 2019

Very difficult course even for someone who had learnt Mathematics and Statistic at the University level. Many concepts were very tersely explained with very few examples. The course book definitely helped. I would say two semesters of Statistics were squeezed into this course. Homework work exercises were very interesting and interactive.

par Stefan K

•May 02, 2020

I found the lectures hard to follow, they didn't help me one bit. If you get his book, read it, and do the exercises, you can save yourself some time.

par Jiapeng S

•Dec 10, 2019

The materials offered from this course is far away enough from understand the content :(

par Robert K

•Apr 16, 2019

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

par Tomasz S

•Jan 18, 2020

Very fast course... Additional reading required.

par Francisco J R L

•Apr 29, 2020

Previous courses in the specialization did a very good job in relating reality vs. theory. This particular course provided great amounts of mathematical theory to support learning, however, in my opinion fails to guide the student to relate real life cases with the theory, making it harder to understand and thus not as useful as it should have been.

par Dion F

•Dec 08, 2019

I'm in the middle of the course and I'm thinking seriously to abandon it... The instructor is simply very bad (he might be very knowledgeable, but he cannot teach – at least in an online manner). I rarely leave negative reviews, but this time I couldn’t resist…

par Ramesh N

•May 18, 2020

The material covered is quite a lot, but the course content is disorganized and the delivery is not engaging. At most, you can use videos and slides as a reference and learn from other sources (as I did).

par Marcela Q

•Jan 06, 2020

Terrible professor!. Too much theory, too little coding. However, the book is great. I recommend do not watch the videos just go to the book!

par Vipin A

•Apr 21, 2020

The instructor's way of explaining things was not that good. Could not understand most of the concepts.

par John M

•Sep 30, 2019

This course was very hard to complete. The lectures were harder to follow than the previous courses.

par Alexander D

•Jan 31, 2020

Wouldn't recommend for those learning stats. Try Duke's course instead. This one was poorly taught.

par HIBRAIM A P M

•May 05, 2020

Los ejercicios están completamente desactualizados y no corren con versiones actuales de los programas. Es necesario que den mantenimiento a este curso, ya que los últimos comentarios que se respondieron por parte de los instructores, lo hicieron hace más de dos años.

par Nelly C

•Dec 13, 2019

There is a lot of theory in the course but it is not always treated with the necessary rigorousness; this creates confusion and makes it difficult to understand the basic concepts.

par Alessandro F

•May 20, 2020

I don't find the button to leave the course!!!!

par Christopher C

•Mar 09, 2016

I learned so much from this course. Brian has an occasional irreverence and dry wit that keep things lively. I will say that I disagree with some of his interpretations, but this is OK!

I would like to see some integration of type s errors, capture intervals, and all the other things the cool kids are doing nowadays.

I am now taking Bayesian statistics online via Richard McElreath's course and this one does help a bit in understanding likelihood functions.

par Boris K

•Oct 13, 2019

This is so far the most difficult course in the specialization, but also the most useful. In this course you are taught to think like a scientist, to test hypothesis and provide evidence for your analysis. The lectures are succint and clear, the quizzes are clever and useful and the final project will make you create a very beautiful report while doing scientific work, which is the reason I started studying data science in the first place!

par Angela W

•Oct 19, 2017

I really liked this course, especially the course project at the end - the second part felt like (a really simplified version of) a task one might actually have to do as a data scientist, and I liked that through this course and the previous ones, I knew exactly what I had to do. The course itself is pretty mathematical and I think intellectually the most challenging so far, especially since it's a lot of content for 4 weeks.

par Kaie K

•Jan 16, 2016

Even as a mathematician I found it super useful to participate this class. I have learned similar material in an undergrad course, but I forgot most of it. In fact this course is so much better than the undergrad course I took, because quizzes and the project help me to learn the material by practical exercises. I am really thankful for the Data Science team for this course and all the Data Science Specialization!

par Lloyd N

•Jun 05, 2017

I thought most of the lessons in this lecture were enjoyable, since it went into the theory of decision-making from data. I feel you need to take an introduction to statistics course before taking this course though, since the lecturer goes too fast at times. I recommend Udacity's Intro to Statistics course, as it helped me understanding the lectures in this course. A+ material though in my opinion.

par amit p

•Oct 04, 2018

This course is one of the most difficult to comprehend, particularly if one does not have any prior knowledge of statistics and probability. But Swirl package of Statistical Inference helps a lot and is a good heuristic approach to learn.

P.S. I would recommend to read this lecture along with any textbook. I referred Probability and Statistics (Schaum Series).

par Prashanth R

•Jan 02, 2018

I absolutely loved this course and felt like i learned a lot about statistics. This was very informative and the peer graded assignment was a perfect way to conclude the course, by having to perform all of the phases in Data Science that I learned by taking other courses in this series. Thank you for this course! Looking forward to the next set of courses.

par Jose A R N

•Mar 31, 2017

My name is Jose Antonio. I am looking for a new Data Scientist career ( https://www.linkedin.com/in/joseantonio11)

I did this course to get new knowledge about Data Science and better understand the technology and your practical applications.

The course was excellent and the classes well taught by the Teachers.

Congratulations to Coursera team and Teachers.

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