In this course you will learn how to use survey weights to estimate descriptive statistics, like means and totals, and more complicated quantities like model parameters for linear and logistic regressions. Software capabilities will be covered with R® receiving particular emphasis. The course will also cover the basics of record linkage and statistical matching—both of which are becoming more important as ways of combining data from different sources. Combining of datasets raises ethical issues which the course reviews. Informed consent may have to be obtained from persons to allow their data to be linked. You will learn about differences in the legal requirements in different countries.
AA
Great course! Thanks, Professsor Valliant and Professor Frauke Kreuter.
显示：10/10
I found very difficult to understand the way professsor Valliant explains concepts. Also I think that the quizzes are not necesarily correlated with the presentation or talks. This is challenging, and usually is good, but there are no enough guides or Reading material to conpensate the lack of clarity and the extra concepts needed to know. What saves this course was the participation of Profess
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 Big Data and better understand the technology and your practical applications.
The course was excellent and the classes well taught by teachers.
Congratulations to Coursera team and Instructors.
Regards.
Jose Antonio.
116/5000
The course was useful and informative to me.
Teachers made complex topics clear and understandable.
I liked the practical examples
The good part of the course is the one dealt with by Frauke Kreuter.
This is the 1 of the 2 worst course I have done in Coursera. The instructional material that are very poor and nearly immpossible to follow. The courses in this specialisation run by other instructors are much better.
Great course! Thanks, Professsor Valliant and Professor Frauke Kreuter.
Mil gracias maestro Richard, Mil gracias Coursera.
Easy to understand
Unfortunately, I don't feel like I am learning enough, and I do not have the confidence I thought I would when I saw the course. Sadly, I might not be able to get the specialization certificate I thought I would, and at the same time, I want to get a certificate for what I believe that I could genuinely do.