Back to Statistical Inference

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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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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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Quite useful to most scientists that rely on data (real/from simulations) to draw conclusions. The fact that the course was generic and widely applicable to all fields was the highlight!

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By dipankar b

•Sep 4, 2017

Good, Productive

By David K

•Aug 16, 2017

a bit cursory

By Luv K

•Aug 23, 2020

Too complex

By Roberto L

•Nov 11, 2018

Too sparse.

By Ankush K

•Jul 6, 2017

Very basic.

By Santiago P G

•Aug 1, 2017

A hard one

By 苏仲达

•Jul 11, 2016

no passion

By Marouane M

•Aug 2, 2023

1 Line Review: This course disappointingly reeks of lack of effort in preparation for such a reputable university.

Overall, I did learn and understand some of the content I wanted to learn, but that was about 40% of the course content. and it did leave me needing to undertake another similar course to fill in the gaps left by this one.

- The course feels incredibly rushed. Every topic is explained and discussed in lightning speed.

- There isn't enough effort spent in simplifying new concepts or even clarifying concepts.

- In a science such as statistics, you would expect instructors to illustrate the notations for concepts they are using in their class, but that is not the case in this course. I was left wondering what some symbols meant for most of my time watching the course videos.

- The only visual aids used in this course are charts created using R from the codes used while explaining the content. No illustrations or otherwise any other type of visual aids, which was disappointing to say the least.

By Hani M

•Nov 1, 2016

A lot of the concepts in Stats Inf - although simple when you think about it and used pretty much every day - I felt were difficult to understand at first. Wikipedia and some other online sources, and youtube videos, were more helpful but I think the real issue lay in the teaching style. I won't knock Mr. Caffo like some of the others here have because at the end of the day everyone learns differently. What works for some might not work for others and unfortunately his style did not suit my learning requirements.

My rating is purely based on the content which I think can be simplified by giving more visual examples. I am rating this after taking the 'Regression Models' course and in that course it is MUCH easier because he gives "real time" and visual examples of what, eg Residuals, mean or represent. Just that alone made a huge difference and it then helps me focus on how to write the R code rather than trying to understand the math. Hope this helps!

By Theresa S M S

•Oct 25, 2023

Worst course of the specialisation (so far). Explanations very fast and not very clear (I had a whole semester of statistics at university and now I feel I know less than before this course). The videos were cut off somewhere in the middle of a sentence, for whatever reason. I had to find other videos on YouTube on the same subject to understand what's going on. Statistics is a difficult subject in itself, so I think it's very important that you get clear explanations and also learn how to develop some intuition about it, as in all the previous courses. I think a big step would be to provide some kind of glossary that explains the most important terms in simple words, as well as an overview of which statistical methods are used when and under what conditions.

By Eduard R

•May 26, 2020

Connection between the slides, transcript, R code, and pdf presentation slides and the text book is great! Easy to follow along. Concepts are explained poorly. Often definitions are missing and the student has to guess what is meant by a variable on the slides. Very superficial learning. Not nearly compareable to real university course. I think the students would benefit from more project work assignments and peer reviews. This is when you really learn something - when you have to do it yourself. Quizes are a good start. I did the course as a refresh and I can't imagine correctly understanding the concepts just by having completed this course.

By Ricardo M

•Dec 29, 2017

The course delves into some relevant topics however it doesn't feel as properly structured. While on the first week the lectures seem to try to give a basic and comprehensible learning of probabilities, once we start into the topics of pure statistics, it's just gets a mess.

Lots of formulas and concepts thrown at you without much clarification. For someone without any knowledge/background on statistics this can be quite difficult to grasp the concepts.

The course should be reviewed or at least the indication of "eginner Specialization.No prior experience required." should be updated to mention that some knowledge in statistics is recommended .

By Thomas G

•Apr 5, 2017

This course seems weirdly balanced between assuming one knows very little about statistics while also assuming one is intimately familiar with statistics notation and terminology. It would probably be better if the data science track had an optional "intro to statistics" class that can take more time to let students familiarize themselves with the terminology, and then a separate "ins and outs of probability testing in R" for those already familiar. This course seems to try and bite off more than it can chew by attempting to be both at the same time.

Still, the lectures are interesting and the material is important to learn / cover.

By Qasim Z

•Oct 26, 2016

This course means well but the lectures in the first half of the course are not good. The instructor seems to take a midway between rigorous mathematics, using terms like robust etc while at the same time also trying to keep it easily accessible. RD Peng's courses take a much better approach in that they keep things at one end of the spectrum (simple language). Having a background in theoretical physics and computer science, this duality in this course is very confusing for me. Also, I do not need to see a tiny, grainy video inset of the instructor during the lecture videos.

By Krishna U

•Jul 29, 2019

Terribly confusing, and concepts were made so much more complicated than needed (lectures, instructions, quizzes).

Most other sources (Khan Academy, Stattrek, Stats textbooks etc) were used and preferred to complete course, to completion of the Data Sciences specialization. Or just have a full understanding in Statistics prior to this course.

Additionally, there's little discussion or help; wish this course could've been updated with revisions or clarified over the years.

By graham s

•May 21, 2016

completely missed the explanation part of the teaching. Why use n-1 for standard deviation? "Because of degrees of freedom" Only mention, no further explanation. Just no explanations of anything in this course. I looked at the biostats course by the same guy. Same story. Teaching is more than just saying the facts, you have to explain things, lead the understanding. The materials are just not there, not in the book either.

By Christine L

•Nov 5, 2016

If I wasn't already familiar with statistics, I would find the lectures and course book difficult to follow. If future revisions to the course are made, consider including a cheat sheet with the notation, parameter abbreviations used, etc. It would also be helpful to rewrite (or at least include a reference back to) the equation being used in the example calculations instead of immediately filling numbers in.

By Charles K

•Sep 18, 2016

The instructors approach in this course is very cursory. He tries to split the difference in going through the mechanics/mathematical theory and practical applications. As a result, he fails at both. I think it would be better to leave the mathematics and application learning to supporting materials and focus on explaining the theory and concepts of statistical inference in the lectures.

By Thej

•Apr 14, 2019

The hardest course I have ever taken! Very hard to follow! Spent a lot of time, trying to understnad the lectures! The final assignment was really good, it really tied everything together! But the lectures and following them was a nightmare and hard to understand! I spent 55 hrs on this particular course! and the last week 4 I spent 20 hrs on this course

By Rajit A

•Apr 2, 2019

The course is very technical and needs a) reading and practice outside of the material presented here and, b) needs you to invest a lot more time than you might believe before you start this course. So if you are looking to just understand the basics of statistical inference or if you don't have a background in statistics then this is best avoided.

By Jake T T

•Apr 8, 2018

This was a difficult course to get through. The lectures were almost completely useless - I had to look up videos from youtube and other sources for every single lecture to learn the concept, and then rewatch the lecture - even then the instructor was difficult to follow. If this wasn't part of the specialization I would have dropped the course.

By Shane H

•Feb 1, 2023

The lectures are super boring (sorry Brian), and they feel rushed and disorganized. Plus the due dates for the assignments are all messed up. The week 3 quiz is due 1 day after the week 2 quiz, and that threw me off. I rely on those due dates to keep me on pace. I was cruising through the Data Science Specialization until I hit this course.

By Eugene K

•Mar 8, 2018

Good material but the lectures are not well put together for the novice. I think the professor needs to have a little more empathy for the students and not just read notes for the class. Too many sentences with esoteric terms are spewed out without truly trying to explain the material in a way that the student will understand.

By Omer A

•May 15, 2016

this is heavy material, and I suggest it be broken down to two separate courses, and the author take his time in explaining the various concepts in much more detail vs. trying to cram them within 5 or 10 minute sessions. I know I wasn't the only one struggling to keep up with the teacher after week 2.

By Stephane B

•Apr 11, 2018

The content of this course is interesting and i learned a lot BUT it's indeed badly explained, and i lost a lot of time to understand certain things. My advice: watch others videos (from Khan Academy for instance) in order to understand the basics concepts and then, come back to this course.