Volver a Inferencia estadística

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755 revisiones

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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por Tomasz S

•Jan 18, 2020

Very fast course... Additional reading required.

por 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.

por 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…

por 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).

por 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!

por Vipin A

•Apr 21, 2020

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

por John M

•Sep 30, 2019

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

por Alexander D

•Jan 31, 2020

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

por Nada E N

•Apr 06, 2020

Do not take this course to learn statistics for the first time!! you would feel so helpless and you may hate the entire subject.. this course is great as a review or revision for someone who wants to recap his knowledge, but if you are learning this for the first time I really really recommend that you go through all the videos of khan academy (statistics playlist on Youtube ) and study it thoroughly, then come here to recap your knowledge.

por 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.

por 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.

por Alessandro F

•May 20, 2020

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

por Dasarathan S

•Feb 13, 2020

It is one of the most important courses in Data Science. It covers most of the mathematical portion and it is hard as well for a non-mathematical student.

For a minimum, every sentence will have any of the four words like distribution, sample, probability, variance, mean, median, standard deviation, etcetera. We have to spend enough time and to be very careful in understanding each and every sentence.

But this course was nicely categorized by Brian Caffo & others, This presentation was the simplest one on probability & statistics, I ever saw and it covers majority of the basic concepts.

Thanks to Brian Caffo, Roger D. Peng & Jeff Leek.

por prahlad p

•May 08, 2020

Initially it seemed all Greek and Latin and difficult to go through. With patience and slowly going through the course material again and again things started becoming easy and interesting. Now I do understand the importance of statistical modeling and how to predict the population behavior. I have learnt a lot to apply the linear regression, confidence intervals, t-testing, poison and binomial testing, and the p-values. All in all a very good experience and the Coursera team has been a great help.

por Do H L

•Jun 17, 2016

This course is tough, informative. Good for people who want a summary of all the statistical concepts you can use for data science. You'll get the most out of this course not by expecting it to be beginner, because it is not. This course is best supplemented by having background knowledge in statistics. Meaning, learners would be much better off if he/she did some statistical course before. This course will provide the practical experience of implementing statistical concepts in R.

por 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.

por 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!

por 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.

por 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!

por 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.

por 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).

por 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.

por 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.

por chirag y

•Jan 27, 2016

It was a good course especially for beginners like me. Though i would advice to continuously keep digging more about other packages also and also going through stack overflow for various hurdles encountered during doing programming assignment.

I would recommend this course to everyone who wants to know about data analysis using R language in particular.

por Olga H

•Dec 29, 2017

Very illuminating and well taught. I think this is content every data scientist should master to begin with. I recommend following this class if you did not learn it in this way already at university, which might be the case if you are in exact sciences. And even if you did, this course might be useful to brush up your skills.

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