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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创建者 Pinar T

•Jan 11, 2019

When you start statistics with practical examples, people tend to presume certain things (e.g. independence is given like in munchausen example) so I sort of understand this desire to keep every definition abstract/pure for solid foundations but damn, this course goes way too far. I took statistics at uni and this was a refresher on the specialisation track but the way Bayes' rule was covered made me doubt what I knew. Oh also, the instructor does some of my biggest pet peeves which are (1) using his preferred notations without actually reading it out loud first (2) unnecessary use of synonyms which just distract me from what he actually means (3) reading from slides without any context as to how these concepts are used.

Also, the concepts are elaborated on a seemingly random basis. Mean is left at "center of mass" like we just came out of physics 101 but the area under a curve is dragged out with random wood cutting analogies. I am just surprised at how all over the place this course is so far. Anyone starting from scratch, I highly recommend probability and statistics reading and some basic calculus elsewhere first. Otherwise you will get frustrated with this course.

创建者 Roman

•Oct 22, 2018

This course is a fucking shitshow. Not only does Brian Caffo not explain anything, he has a tremendous gift to confuse people and make them forget / not understand anymore what they already knew. Great fucking course. Not. Hated it from the first minute.

创建者 Deshina B B

•Nov 27, 2018

The teach methods changed too drastically starting with this course. Much more prerequisite knowledge is required then is included int his concentration course set. No foundation or warning is given regarding this change and prerequisites. I had to seek out and spend countless hours on many other learning resources to get through this course and still don't understand what this course was trying to teach.

创建者 Anthony M

•Sep 03, 2018

Poorly organized content and the lectures are presented in a confusing way. The lecturer obviously knows the material well, but is not able to present it well. He should use more sample problems annd examples. In addition, I am having trouble getting my submission graded, although I have already graded 6 fellow students.

创建者 Nina

•Jul 21, 2017

I couldn't make it through this course because I can't stand looking at the instructor's face on the screen. It is very distracting. He is also not very clear in his whiteboard explanations, too much scribbling. I prefer courses taught by Roger Peng.

创建者 Stephen G

•Oct 25, 2016

The only reason for enrolling is to complete the data science specialization, though it may make you reconsider continuing with it. The instructor and provided materials fail to adequately explain the concepts this course is supposed to cover, and do not prepare students for the quizzes or assignments. If you don't know statistics you won't learn it here. If you know statistics, you don't need this course.

创建者 Joshua A B

•May 29, 2016

I've taken several statistics, data science, and R courses. This is one of the worst. I took others' advice, and I also strongly suggest looking to other sources to learn Statistical Inference before taking this course. Khan Academy, DataCamp, Udacity, Duke (Coursera), and Columbia (edX) all have great courses. Though they vary in depth, each leaves you with a good understanding of the concepts they teach.

创建者 Shimon Y

•Mar 10, 2019

very unclear and monotonic lectures

创建者 Nithiwat S

•Oct 08, 2019

Very horrible course. This course is a good example of how to design a bad online course and teach a complex material so it's more difficult to understand. I'd give other courses a shot instead of this one, unless you want the specialization.

First, the course should have listed R programming as a prerequisite. Second, instead of only reading the definition of terms, he should have explained what they are and give example. I spent more than 4x of the video lecture length, trying to think and follow, but I still didn't understand. No example, no explanation, just *reading* the definition right off the text book using mathematical term. Math is complex, but it can be explained so everyone can understand. Third, I simply have a hard time understanding why the professor has to talk so fast and edit his video and/or make slides so text pops up in order to speed up the lecture instead of writing along as he speaks and explains (but there was no explanation in the lecture anyway so pausing the video to slow down wouldn't help). It's difficult to catch up or take notes when he edits video so text pop up very quickly. I don't think there's a restriction on the video. Overall, horrible experience.

创建者 Elaine E

•Nov 14, 2019

I'm sure he is super nice and smart, but Caffo is an awful instructor. I kept waiting for him to actually get to the explanation of what he is talking about, but he never does. He explains the equation...with the equation. I really like statistics, but he was beginning to change my mind and somehow I was actually unlearning everything I had learned from other courses in the past. I would highly suggest taking this somewhere else, I like the one from Duke on Coursera, she actually explained things and I could understand!

创建者 Michael S

•Aug 26, 2018

The material is obviously invaluable but I thought the lectures themselves were lacking.

创建者 Jacob A

•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 .

创建者 Yusuf E

•May 21, 2018

At this point in the specialization, I was really worn out by the effort that I needed to put into this course (I solved the homework questions too). While I have no problem with the math, some topics like power should not even have been discussed or should have just been discussed in passing. Caffo spent a whole week on that. After taking the Applied Data Science in Python Specialization, I have a feeling like this course and Regression Models can just be merged, while logistics regression could just be transferred to the machine learning course.

Fortunately, the final assignment was very easy compared to the previous courses and one could finish it reasonably in a day (Reproducible Research final assignment itself took me almost a week) .

创建者 Rebecca K

•Sep 03, 2018

The information is so important and useful, but I found the presentation of the material to be fast and not very interesting, and therefore it was hard for me to retain the material. I learned a lot, but I would need to invest a lot more time to realllyyy grasp everything in the course. It wasn't presented in a way that made it easy to learn, so I need to spend more time going back over things to really get it.

创建者 Dai Y

•Aug 16, 2018

There're lots of practice on manually construct statistics. I'm not sure if it's necessary to do that since we could just use R code to do it. I think how to interpret it and use these statistics in examples would be more important. There're some examples, but could be more and interpret more in depth if there were less focus on the calculation.

创建者 Dasarathan S

•Feb 13, 2020

It is one of the most important courses in Data Science. It covers most of the mathematical portion and it is hard as well for a non-mathematical student.

For a minimum, every sentence will have any of the four words like distribution, sample, probability, variance, mean, median, standard deviation, etcetera. We have to spend enough time and to be very careful in understanding each and every sentence.

But this course was nicely categorized by Brian Caffo & others, This presentation was the simplest one on probability & statistics, I ever saw and it covers majority of the basic concepts.

Thanks to Brian Caffo, Roger D. Peng & Jeff Leek.

创建者 Do H L

•Jun 17, 2016

This course is tough, informative. Good for people who want a summary of all the statistical concepts you can use for data science. You'll get the most out of this course not by expecting it to be beginner, because it is not. This course is best supplemented by having background knowledge in statistics. Meaning, learners would be much better off if he/she did some statistical course before. This course will provide the practical experience of implementing statistical concepts in R.

创建者 Ritwik V

•Jun 08, 2020

Nice course,enjoyed it the most till now out of previous courses of Data Science Specialization. But is tough for people from non-Statistics background. I am a Statistics Major and I have studied all these topic in great detail so I didn't need to watch much videos.

创建者 Long H

•Jan 31, 2016

I found this course really good introduction to statistical inference. I did find it quite challenging but I can go away from this course having a greater understanding of Statistical Inference

创建者 Pankaj K

•Oct 16, 2018

This course covers the very basics of statistical inference which will help to strengthen your base concept. I loved doing the course especially the practice assignments on swirl.

Thanks.

创建者 John D M

•Feb 01, 2019

This is an excellent course, though it is fast-paced. I didn't have time to watch the lectures and also do the practice exercises in Swirl in the time allotted. As usual, the time estimates for completion are wonky. I ended up just watching the lectures and taking the tests, which is far from ideal (I am taking some time to do those valuable exercises now that the course is done). Although I got 100% in the course, I felt the learning experience could have been better as a result.

创建者 Audun T H B

•Oct 01, 2019

Thorough course. A bit difficult to follow the lectures at times.

创建者 Andrew

•May 05, 2019

Not my favorite course in the series, but I did learn a lot. I highly recommend following along with the course book provided in the course. The videos alone are not enough. I also recommend printing out a sheet with statistical formulas to use (not provided from the course, but you can find easily on the web). The stat sheet with formula helped me connect all the dots and better understand when to use a formula.

创建者 Mingda W

•Jun 05, 2018

My most recent experience with statistics was about 2 years ago, and it was college level statistics. Still, I find this class is hard to keep up sometimes. In general, I felt like the professor explaining too much on the mathematical meaning behind equations instead of talking about the real-world meaning of equation components, and why those calculation make sense.

创建者 Moshe P

•Sep 24, 2019

Very difficult course even for someone who had learnt Mathematics and Statistic at the University level. Many concepts were very tersely explained with very few examples. The course book definitely helped. I would say two semesters of Statistics were squeezed into this course. Homework work exercises were very interesting and interactive.