This is a very nice course provided you study a lot for it

Love this course. teach me to understand Linear Regression more, especially swirl class is great.

Builds Heavil

Very hard to understand

The lectures were a bit complex and lengthy.

good content

Very good course. Though basic, it provides you with the first tools and knowledge. The forums aren't what they used to be it seems, but you can find almost any answer there from past courses.

Overall an excellent course, but there were some issues with the wrong function being specified in one quiz (Q3q6) and the wrong answer in another. Apparently it has been that way for years, according to the forum. The quality of the lectures was very high and the information interesting, so compliments to Dr. Brian Caffo on that. However, the estimated time for completion of each week is ridiculously short compared to reality. Five hours? For me it was more like 20 hours, and more if I did all the Swirl exercises. Such low-balling on the time estimates is typical of the Data Science stream. The final project is given as 2 hours but it was closer to 15 for me. i wish Coursera would go back to the stream model where you could bump yourself to the next intake. That is much less stressful for busy working people like me.

Good class.

Great introduction to regression models. A ton packed into the class. Be ready to be challenged, but you'll learn a lot.

For the content covered, I think the course does a good job exposing students to fundamental concepts while also highlighting how much more there is to research in order to gain a solid understanding of this subject matter. The course offers a good foundation, and I hope they come out with a more advanced version of this course for more guided exposure.

Hard class, documentation could be better, but good content.

The lecture is pretty dry to me who had limited vocabulary in the field. It made me went out to find other easier lectures to help me understand. The lecture focus on explaining the basic concept of Regression Models and spend a big chunk of time to explain how the function works. I would prefer to have more time explaining what the numbers mean for the data. The questions in the quiz require us to understand the meaning of the data, so we know what function and number to apply. Maybe it is just me, finding it very challenging to see the connection between the lecture and the quiz.

Very informative, but could be more interesting and concise.

The explanation of the right answers from the quiz were quite handy!

The content is to long, maybe would be interesting split the content in other modules.

Good Course for beggining

This course should be targeted for Data Scientists, in my opinion it is more for statisticians.

Too much about the insight of statistics and some but not enough about how to use the statistic tools.

The instructor is at time incomprehensible. It would be helpful to speak more slowly and pause more often. Otherwise he sounds like repeating something that he's so well memorized after many years of teaching.

This course is better than Statistical Inference, and I think it is as useful. Non credit excersise are still very good at helping with understanding in practice what is going on.

Given the importance of this subject, this course should have been split in two or more or have a longer duration to properly address subjects as GLM or model selection techniques.

Expects a level of statistical knowledge already.

To me, the approach was too much from the theory of statistics and its mathematical foundations; I would have appreciated a more applied approach for this course in the specialization. So starting from examples, questions anout data and then working towards theory instead of the other way around.

I enjoyed the earlier courses more. I did not like the way the materials were provided. There were a lot of very complex ideas were presented, in a very concise and brief manner. Also, there should be more exercises to practice. May be its me, but, I guess, I might need more time to fully comprehend the materials.

the lecture notes is a bit confusing