As for the Statistical Inference course, this course is amazing but is presented in a more complex way than it should be. Once again the concepts are simple and the math not so hard, yet I had to do a lot of research outside the course to be able to understand these simple concepts and derive the not so hard mathematics.

Brian Caffo is clearly brilliant and, I would say, seem to be a good lad too, but something is missing. Too often the details are thrown at us without being properly framed in the context or without having the proper concept being introduced progressively.

I have a theory about teaching since I was 15, and so far it has proven to be true. Imagine that learning is about climbing a mountain in which tall steps have been carved. Each step is taller than the student. The teacher is somewhere higher than the students (not necessarily at the top, if there is such a thing).

The job of the teacher is to throw boxes (concepts) and balls (details) of different size, shape and colors. The job of the student is to catch these boxes and balls and to put the right balls in the right boxes in order to make a staircase out of it to climb (at least) one of the giant stair up.

A good teacher makes sure to throw the concepts first than the details and to clearly specify which balls go into which box, as well as which boxes go inside/over which other boxes.

But most teacher simply throw the balls and boxes in an not so well structured manner, so the poor students try to catch as many as he can, but also miss a lot of them. His hands can hold a limited amount of balls. If he doesn't have the right box to put them, he would either miss the next balls, or put the one he hold in his hand in the wrong box.

Bottom line, the best teachers are those who focus on the concepts (and context) and make sure that the concepts are well understood before introducing details to stuck in these concepts. From my experience our brain (or at least mine) better learn this way. It is as if our brain need first to establish a category-pattern (the concept/context) to which it will associate detail-patterns. But without a proper category-pattern, our brain is having a hard time to properly remember the detail-patterns or miss-associate them to the wrong category-pattern (which create even more confusion).

Hope it was helpful somehow...

Material is too dense for the time spent engaged in class. Difficult to stay engaged with lectures, which spend a lot of time on the underlying mathematical concepts. The conceptual underpinnings are very important, but due to the limited timeframe available to present the material, the application of the concepts was done quickly, almost as an aside. The bridges from concept to practical application are very weak.

I seldom write critical comments for Coursera courses because the many courses I've taken have been quite well-designed so far. This one I feel obliged to write something, which may or may not make a difference judging by how much care was given to designing this course in the first place. From the resource allocation perspective, this course does more harm than good because the minimal amount of knowledge you gain from this course is not worth the amount of time you spent trying to figure out how the lecturer perceives and conveys statistical concepts in such a confusing way.

Bottomline: if you don't need the Data Science specialization certificate from JHU, you are WAY BETTER OFF by taking the Basic Statistics + Inferential Statistics courses provided by University of Amsterdam. I completed those two courses myself. The lecturers there truly made an effort to make the materials as engaging and intuitive as possible. You will not waste your time by taking those courses instead.

If you thought the Statistical Inference course was bad enough, try taking the Regression Models course. It refreshes your understanding about how bad a course can be. Below are some major problems:

1. The delivery of the materials is very dry. I can't tell if meaningful effort was put into creating engaging examples so that students can better understand the material. The mathematical and theoretical parts were poorly explained with inconsistent notations and insufficient elaborations about the concepts. The lecturer often jumps from very basic concepts to very advanced/complex concepts without enough transition/explanation. I had to constantly consult a friend who's very good at statistics to bridge the gaps.

2. The lecture notes are way too chaotic. Many times the PDFs provided do not match what was shown in the videos at all. Several pages in the PDFs were not covered by the video lectures, and vice versa.

3. Stepwise regression was not even covered in this course. Many students ended up using stepwise regression for the course project. Maybe students are just jumping ahead before applying the more fundamental techniques covered in this course, or maybe stepwise regression should have been covered??

4. I wish there were a lecture at the end that walks through one case study and applies most of the core techniques covered in this course. In Roger's Exploratory Data Analysis course he did one at the end and applied many things he taught in the fragmented lectures in an integrated manner. That was super helpful.

Some minor good things about this course that I did not gain from the UvA courses:

- The hodgepodge lecture provides some very interesting materials.

- The simulation examples about covariate adjustment are quite intuitive and facilitate understanding.

I have wasted my money by enrolling in this course. After every swirl programming, there is some issue and I have to restart the Lab session again. I have completed 88% of swirl Lesson 9- Functions, three times now, sometimes I get the output, sometimes error, with the same code. In my R studio, output is coming, but online, it's giving output, but then ">" symbol comes and then nothing. I have been struggling with this course. Pathetic one!!! I wont be able to finish this course like this, not even in a year. I want a refund!!!

Very practical class. Great experience learning both the theory behind linear regression, GLM's and their application in industry. I appreciate how the instructor taught multiple methods to get to the same place using R. One of the best courses in the Data Scientists Specialization series!

The course content was very brief and well structured, Regression being a rather vast topic demands a lot more time. 4 weeks seemed a bit less! Overall satisfied by what the course offered.

Great introductory course on Regression Models. Super practical and well explained. Definitely doing the exercises and final project is a must to get all the learnings!

Loved the course and the content. Only critique is that I would have liked to have a lecture about transformations, and interpretation of transformed data coefficients.

Good introduction to the usefulness and traps of linear models. By the way, having the teacher filmed for the lectures does provide a more engaging experience.

The instructor explains the course in detail. He speaks word by word and if you are an international student, you will be able to understand what he says.

A very good course, goes deeply into the material. The pace of the professor is ok. It's nice that he uses some practical cases to explain the theory.

Helpful!

If there is more introduction about the common problems people may encounter during working in the real world, the course will be better!

Excellent, highly focused course with current R libraries for learning various regression methods and methodology. I highly recommend this course.

This course is perfect to get started with Regression Models in R! I think you would need some familiarity with the statistical concept though.

It's a great course and tought very well. It required effort, you apply many of previously teach concept and requires a lot of excercise

Very Helpful course. I am from a non -stats background and this has helped me a lot in understanding such deep concepts of Statistics.

Learned a lot and enjoyed the course project. Would like to have two course projects because I gain the most out of them.

One of the most valuable course in series. Also one of the hardest, expecially if you are newbie to regression models.

A very well organized course with nice simple explanations and introductions into the world of regression models

Excellent, but I would be grateful if you could translate all your courses of absolute quality into Spanish.

Outstanding material with different levels of difficulty and depth on the subject. Great source material.

I had a lot of fun in this course. The exercises in the text and quizzes help me understand the concepts

easy to understand and full of new idea about using R.

especially 'manipulate' package is very useful

The best course of the Data Science Specilization until now and by far. Very practical and useful!

One of the best courses on Coursera for sure. Thank you so much. Regression has never been easier.