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....

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!

筛选依据：

创建者 Even R

•Feb 10, 2018

I have done PhD level statistics courses before, but found that they either went too deep into theoretical mathematics as to completely loose the audience (at least me), or to not even try explaining what is going on under the hood of R or SPSS. What I really like about this cource is it pushed me to do calculations by hand, which really helped me understand the concepts. Dr Caffo is clearly a skilled statistician and the course is at its best when he goes off script (at least off slides) to explain and illustrate concepts. Minus one star because unfortunately the presentation of the material is uneven and some times distracting, e.g. talking very fast.

创建者 Ada

•Nov 14, 2016

This was the toughest of all the Data Science courses so far. Without the statistical inference book, the practical exercises and the swirls it would have been very challenging to pass the course. These were very valuable tools. The videos that are available when I couldn't get a practical exercise right, also helped me a lot.

I majored in mathematical statistics 40 years ago, but have never used it in my whole career. But in my opinion this course explained the concepts much better than how it was done 40 years ago. Congratulations to everybody involved.

I have learned so much and was really proud of myself when I passed this one.

创建者 Joel H

•May 4, 2020

I think overall Professor Caffo does a fairly good job of explaining the material, though he covers a lot of topics quickly within the course. So I found myself having to pause and rewind often when taking notes. The course project was definitely the most challenging aspect of the course for me, since my background is in SAS and am an R novice. I spend a full weekend putting the report together. Since so many topics were covered in such a quick fashion, I don't think I retained it as well as I should. Luckily my undergrad statistics background helped a bit, even though it was over 20 years ago.

创建者 Robert O

•Jul 14, 2017

I get that the subject matter is hard and so this isn't going to be easy to absorb regardless of how it is taught. My biggest challenge was too many overloaded sentences where to understand the new area that was being focused on there was an assumption that i already had firm grasp on the set of other subject areas referenced in the same sentence. A lot of confusion as well arising from terms that sound the same except for one word or context of their use which maybe could be helped along by a summary slide of terms and meaning at the end of each lecture or section.

创建者 Kalle H

•Jan 28, 2018

Nice course with an appropriate level covered for the data science specialisation (assuming people taking these courses very have different prior knowledge of statistics). It would however be good to add a second statistics course to the stream with some more advanced topics. Yet, it is still one of the harder courses of the specialisation.

The only big criticism I have is that the course feels a lot less polished than other parts of the specialisation. It feels like cut and pasted parts of other courses added into one course than its own entity.

创建者 Alberto G G

•Dec 11, 2016

I am interested in taking the Regression Models course and took this one as a refreshment for the statistics knowledge I already had. I found the course well done and the resources easy to use and throughout.

As a negative point I would mention that as the topics get more involved, the time dedicated to each one seems to decrease, to the point where both MULTIPLE TESTING and GROUP COMPARISONS are pretty much a briefing, which kind of defeates the purpose of including them on the course in the first place...

I give the course a 8.5 out of 10.

创建者 Evgeny P

•Jun 2, 2018

Very... practical approach. Almost no math, almost no theory - but much R code to write and understand. Now almost any person could be a statistician.

I would prefer more details about caveats of the job, at least about p-value controversy. I would prefer more theory. But perhaps it just mean that I should take another course in addition to that one.

Thanks to Brian, Roger and Jeff for teaching me. Thanks to my fellow students - it was nice to interact with you. Thanks to Coursera and to Johns Hopkins University for making this happen.

创建者 Blaž Z

•Aug 27, 2019

I know this is a very shallow dive into the field, yet I have the feeling I could have learned more. The absence of judgment whether my calculations in the project were correct or not was confusing too me since in statistics it's more important to correctly interprete the result then to get the result. Calculation and interpretation of results should become routine and more different, practical, coding exercises would be very cool to have. Having that said, I still much appreciate the Data Science Specialization as a whole.

创建者 Miguel C

•Apr 25, 2020

Since I already had some background on probability and statistics, there was a lot I already knew here. However, the use of R throughout and the contents of week 4 (power, multiple comparison, bootstrapping) were all new and I really enjoyed learning them.

The lecturer was really knowledgeable, but I believe sometimes he was a bit monotonic making it slightly harder to follow. However, the lecturer did make a huge effort in explaining everything carefully.

Overall, I really enjoyed this course and I would recommend it.

创建者 Lars B

•Oct 2, 2016

I liked this course, and found the lectures interesting and would give them perfect marks on being instructive. However, I found that there are some speling errors here and there, and a remarkable frequency of sirens audible in the lectures (is JHU in a difficult neighborhood?), and sometimes it was hard to recover the plots from the git repository.

But, more importantly the curriculum and teach was very good, and the use of examples after the theory makes it much easier to grasp difficult concepts.

创建者 Samuel Q

•Apr 2, 2018

Very good and informative course. Brian's teaching style is not the best. The quizzes were not easy. But then this is a University Level course and students need to be resourceful. The textbook helps a lot as does the rmd. files available through GitHub. The mentors in the forum are very helpful.

My recommendation : add one more course project (or two). These really help with learning how to deal with real data and how to apply what we've learned in the lectures.

创建者 Francisco J D d S F G

•Oct 22, 2016

The course on statistical inference is a crash course on frequentist statistics; in my opinion the contents are appropriate for statistical inference, though most of the concepts that are taught deserve more attention, or perhaps split throughout other courses.

Nevertheless, Brian makes a terrific job to compress all of these topics into a single month course which is impressive - for someone familiar with statistics it's actually an enjoyable course.

创建者 Momoko P

•Dec 8, 2016

I thought the course material was great but I think the grading criteria for the assignment should be more rigorous and check for proper methodology/application of techniques, just because I'd like to know that my approach to a given data analysis is sound and that the conclusions I draw from running tests, p-value adjustment, calculating power, etc. are statistically valid.

Overall a great primer! Thanks to Brian & Jeff for putting this together :)

创建者 Pragyanand T

•Nov 25, 2020

If you are a beginner and you are just trying to learn statistics then this shouldn't be your first course. It is an intermediate course and if you're just trying to learn stats for fun or basics of it, just to understand the data you are looking at, this course is not it and if you know a little stats and are familiar with some of the concepts and understand the lingo then this course has a lot to offer and you'll learn a lot of fun things.

创建者 Jikke R

•Mar 3, 2016

Very challenging and very valuable as a learning experience. I really liked it and fortunately I passed. I did think it was a bit too fast-paced in some places though. It is so much theoretical and mathematical stuff that it would have been all right to spend 2 months rather than 1, and learn a little bit more in-depth with a little more space for practice and assignments.

创建者 Joel A

•Mar 15, 2020

Overall a good course. Swift library was used well in order to review ideas presented in the lectures. I feel as though not enough emphasis is put on the importance of multiple comparisons and too much effort is made to shy away from the mathematics, of which there is none. I would give 5 stars if the course was not so mathematically barren.

创建者 Paul R

•Mar 13, 2019

Relatively, this is one of the best courses and lecturers of the specialization, Brian delivers clear, thorough and well-paced lectures. These lectures on statistics, regression and machine learning are where the rubber hits the road after a lot of prep work to learn R and principles/tools of data science taught in earlier classes.

创建者 Kim K

•Aug 8, 2018

If you have taken Statistics before it may help you move through this course and meet deadlines, or you will need to set aside a large amount of time daily to learn. The forums are a necessary supplement to understanding the details of the project assignments and quizzes. Rigorous and rewarding when you put the work in.

创建者 MARIO R G G

•Aug 14, 2020

Even thou I understand some of the bad reviews that criticize this course by covering a plethora of "difficult" (or at least new) topics, Dr. Caffo and Dr Leek always provided clear explanations, and if you complete the Slidify homework assignments and read the book provided (the times required), it wont be that hard.

创建者 Jorge E M O

•Jul 21, 2016

This course actually helped me to practice and to obtain a better intuition of the most basic Statistical Inference ideas. It covers up little content, but it focuses more on the practical understanding than on the maths. After taking it I feel like I can go and take a much more math-heavy course and I wont feel lost.

创建者 Siddharth T S

•Jun 4, 2020

This is a great course in terms of the content it covers, and the homework exercises, quizzes and final assignment really challenge you, But no way are you covering the course without an introductory statistics book at your side, if you're a beginner (I used Statistics For Business And Economics by Anderson et al).

创建者 Vlad V

•Mar 22, 2018

Very good introduction course to statistical inference. Sorts out and structures such diverse topic as statistical tests and approaches. Quite easy to solve and there is a lot more confidence to what it gives.

Give 4 out of 5 stars - it would be very good to include 2x2 contingency tables explanation here.

创建者 Camilla H

•Oct 23, 2017

I very much enjoyed this class. I have taken statistics classes before and this was a good and challenging refresher. My only critique would be that the Project Review should be more rigorous. Out of 8 points for the two assignments one point was dedicated to effort and another to format/logistics.

创建者 Hernan D S P

•Mar 18, 2018

A very good introduction to the subject. The textbook is very helpfull and the explanaitions are sufficent to gain a practical understanding. If you want to gain more in depth knowledge on statistical inference you will need to complement the course, nonetheless this is a very good course!

创建者 James T

•Apr 26, 2016

This course took me 3 weeks longer than anticipated, and I'm not sure it was the difficulty of the material. A lot of this information is review for me, so I am confused why it took so much time to complete. There were new topics, and I did enjoy working through the homework questions.

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