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Statistical Inference

Coursera · Johns Hopkins University · 2 HN comments

HN Academy has aggregated all Hacker News stories and comments that mention Coursera's "Statistical Inference" from Johns Hopkins University.
Course Description

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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This course is offered by Johns Hopkins University on the Coursera platform.
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Hacker News Stories and Comments

All the comments and stories posted to Hacker News that reference this url.
Do they really get better? I'm either going to jump straight to the (R) Statistical Inference[1] course from JHU, or switch to the Berkeley/EdX Spark course[2].

I use a lot more Spark in my day job than R, but I really should learn statistics more formally.



I thought they got better compared to the first few classes, but they do really revolve around R. For a rigorous treatment of the subject matter, the MITx course on Probability is really good. [1] You could also take a look at the two JHU "Mathemtical Biostatistics Bootcamp"[2] courses. Those are also quick compared to the MITx course, but a little more careful about the math than the courses in the data science specialization are.

I haven't ever used Spark, and I like R, but I am going to take the Berkeley/EdX course.


[2] &

Looks like this may be Prof. Leek's course mentioned at the end of the article:

Also there was previous HN post on p-values, which I found really interesting:

Also this Nautilus article:

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