# 学生对 约翰霍普金斯大学 提供的 统计推断 的评价和反馈

4.2
4,219 个评分
853 条评论

## 课程概述

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....

## 热门审阅

JA
Oct 25, 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 .

MI
Sep 24, 2020

the teachers were awesome in this course. I liked this course a lot.Understood it properly.Thanks to all the beloved teachers and mentors who toiled hard to make these course easy to handle.Gracious!

## 651 - 统计推断 的 675 个评论（共 821 个）

Apr 15, 2016

The subject is interesting, but the explanations are a little confusing. May need more diverse real-life examples to relate.

Mar 27, 2016

it gives an idea of how one is prepared to ingress to Data Science. I

see that I need to review it more carefully later on.

Nov 21, 2019

Weeks 3 and 4 should have been split into 2 extra weeks to explain the concepts deeper and also have more exercises

Jun 1, 2019

Concepts are not well explained and slides are not well prepared. Last few topics are too brief to be useful.

May 1, 2019

3 stars because a total beginner would not have been able to follow these lessons without a lot of rewinding.

Nov 27, 2016

Materials need to be updated - there are way too many inconsistencies between videos, exercises, and slides.

Jan 28, 2018

Too much concepts to learn and practice. Course material can be little more engaging and split accordingly.

Dec 10, 2017

Very difficult lectures. You need a solid statistical background to keep up with the pace of the professor.

Mar 2, 2017

The ideas and concepts explained here are really important but are explained/written in a bit messy manner.

Sep 22, 2016

lectures notes is not details enough, had to google around other materials to grasp the courser work better

Jan 11, 2021

It's very important and very helpful, but it needs to be of more time/low speed to be perfectly absorbed.

Mar 13, 2020

It covers a lot of topics, good for that but submitting assignment via Swirl is extremely boring.

Jul 5, 2020

This is module where I have learn less. Instructor also was not dynamic as previous ones.

Mar 5, 2018

Too bad it all starts from mathematical theorey; I would prefer a problem based approach.

Feb 12, 2018

Very detailed and a little painful :) but I am sure it will be useful information

Mar 2, 2017

These are complex topics, and just the quick overview doesn't fully explain them.

Oct 11, 2020

Me hubiera gustado tener más detalle de algunos conceptos clave de estadística.

Feb 9, 2016

Not one a statistics newbie should take, had to take it twice just to grasp 80%

May 20, 2016

Content runs a bit fast but good course for stat inference with R focus.

Jun 15, 2019

The materials are not so clear to someone who's not familiar with stat.

May 3, 2017

Concepts weren't explained properly. The instructor was going too fast.

May 4, 2016

It's quite involved, fast and not explained thoroughly in some places.

Jun 13, 2017

Unfortunately, the manner of presenting information desires the best.

May 10, 2016

Steep Learning Curve. Swirl exercises are important for this course

Apr 25, 2018

Less good than expected. Explanations could be more clear.