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3,585 Bewertungen

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691 Bewertungen

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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von Сетдеков К Р

•May 16, 2019

Great reminder of statistics from my masters degree. Very condensed and easy to remember.

von Dan K H

•Jun 16, 2016

Excellent course well explained by Brian Caffo with both theory and practical examples!

von Harland H

•Jul 03, 2018

Great course. Difficult concepts, but the lectures and quizzes make it easy to learn.

von Charles Z

•Jul 05, 2017

Very helpful but need quite good statistic background to catch up with the learnings.

von LIWANGZHI

•Jan 18, 2019

this course really provides me a insight into statistical inference. Thanks, Brian!

von André M M

•Mar 04, 2018

I enjoyed a lot the theoretical content and the way it was present by the lecturer.

von Dewald O

•Dec 21, 2018

very informative, very technical for a beginner, but provides a good understanding

von David R

•Nov 12, 2018

Great refresher on stats and how to carry out tests in R...lots of useful examples

von Robert H

•Sep 05, 2019

Very good course, well detailed and a nice introduction to Statistical Inference.

von Elmer P

•Aug 19, 2018

Solid on statistics, which I think is very relevant in Data Science and Research.

von Maliheh S

•Sep 17, 2017

The best course I've taken in Data Science Specialization.

Thanks Professor Caffo!

von Rodrigo P

•Apr 27, 2017

Statistics is not easy, but it is very powerful to understand real data problems.

von Rok B

•Jun 10, 2019

Great course! Gives a really nice and comprehensive overview of basic statistics

von Long T

•Sep 10, 2017

Very nice and descent. The homework is especially interesting and well designed.

von danxu

•May 22, 2016

Very useful !

Teacher is awesome, statement is clear and simple.

love this course!

von Bruno R d C S

•Apr 20, 2019

this course is excellent as it is hard if do not have a good base on statistics

von Vinicio D S

•Dec 22, 2017

Great course for getting an introduction into the Stats needed for Data Science

von Léa F

•Nov 10, 2017

Thank you Brian !!! I learned a lot of things in a very short amount of time :)

von Peter G

•Feb 04, 2016

Definitely the best and most useful course of the Data Science Specialization!

von Offiong E

•Jun 28, 2017

very fast paced course. i totally enjoyed digging deep into my inner reserve.

von Carolina G

•Sep 23, 2019

Muy organizado, con temarios interesantes y mucha claridad en los contenidos

von Ruddy U

•Jun 14, 2016

It was very interesting, descriptive and joyful. I really loved this course.

von pravin

•Jul 03, 2017

Very valuable overview for all statistical analysis used in future courses.

von Marcelo A P

•Aug 17, 2016

Need to improve the clarity of the lectures, but it´s a good course though.

von SATHYANARAYANAN S

•Sep 11, 2017

Very good for anyone wanting to get into the field of Data Science using R

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