Keep it up

not bad

In overall the course is good. However, there are some issues that could be improved, as for example:

- Using the NHANES database is come cases is not the most effective as you can spend some times trying to indetify or search for the variable they are asking for. Better instructions or the use of a simpler database could be an alternative.

- Some videos could be improved. There are compilation errors in the Python demostrative videos, in some other cases previoulsy not-explained functions are used (while similar functions already known by the alumn are available) or Python 2 functions are proposed (the course should be oriented to Python 3).

- I found that both parts of the course (stats and programming) are not always perfectly coordinated.

Despite these issues, the course is good and I will go to the next course with them.

Overall good but still have rooms to improve. I knew so little about statistics and Python. The concept is quite difficult but relatively new unlike other typical statistics courses offer. Practice assignments are very good but difficult. More guidance of Python libraries usage would help. Passing assignments were too easy. Strong foundations of using Python especially in libraries such as matplot, numpy, panda, seaborn would really help to better understand the concepts with a graphical presentation in Python. I would recommend this course for those who are familiar with those Python libraries already. For me, I need to learn more about those and would revisit the content here again to better grasp full understanding.

Generally very good content and presentation. Removing a star due to frustrations with a really off topic essay assignment required in week 3 of the course. An online programming/statistics course is not the place to teach writing skills. This is especially true since the online peer review grading system isn't configured to ensure submitted essays are reviewed.

The course material seemed a bit scattered, possibly because of there being at least five presenters. The material wasn't really that focused on data visualization and veered into esoteric (but interesting) topics like non-probability sampling. The pizza memorandum assignment seemed quite pointless. More work with Python labs would have been my preferrence.

The topics that were seen in the course started in a very basic and understandable way but they evolved to much more advanced and difficult topics without a good explanation.Sometimes I felt no connection between theory and practice with Python. The large number of teachers does not allow continuity in learning and creates gaps.

This course started well, but unfortunately, I think they should add more extra example and focus on the topics more in-depth, I can say in each quiz I spend around 3 hours to find related topics in the internet and learn them to answer to the questions and I think it should be cover by the course itself.

It's great course to understand the basic concepts of statistics like uni-variate and bi-variate data.But,the assignment which they give week 3 and week 4 is not that much to implement the concepts practically. Overall ,it is a good course.

The theory material is great. However, the final week has a bit exhausting content but the lab is way too easy. Additionally, there is no real "key answers" for the lab so I cannot double check my work.

Very long videos, even the simplest concept is explained in a slower manner. But this is true for me and a lot might benefit from this pace.

Lectures are boring and very long it should be more practical ,but yes I've gain certain statistical insights.

Python in week 2 is largely unexplained, also course could have dived deeper into statistics

too much works in each week, It may be possible for people, who work only half day

Good content and syllabus, though the later videos could be easier to follow.

I think it was more theoretical and more practice is required.

Too many texts in the lectures

A little easy, and lacks focus

Could be more refined.

phyton part is shit

Advertised as 'level 1' but this course requires quite a lot of Python knowledge. It's prerequisites really should be more clearly outlined. I started this course 6 months ago, stopped to take the recommended Python courses--which I did--but it's still not enough to understand the Python applications in this course. Instructions need to re-write the prerequites of this course because it's misleading as it is.

It was irrelevant and contained unnecessary content. Why are we drowning in theoretical statistical topics instead of focusing on Python? Thus far, the course has been more about statistics than actually working with Python! I am here to address my statistical needs using Python, not to become an expert in statistics. Unfortunately, this course seems to be doing just the opposite.

The ads is misleading and inaccurate! Most of course is delivered by undergrad students without any in-depth explanations and they literally skim read the contents for you! Nothing special! The name of UoM fooled me to register! However, No professor at UoM is teaching this cheap course.

sampling distribution had too less examples but showed teachers face a lot

The courses is supposed to help students learn how to use Python to understand and visualize data. However, the course lacks focus on the subject as well as tasks for practicing Python code. Lack of practice. The peer-reviewed tasks are hilarious - you will be asked to describe how you'd visualize metrics in (Python you would think? No!) words. This is so easy to turn this task into something actually useful: create a notebook with preloaded data and ask students to come up with metrics and visualize them. No-one came here to practice English writing skills, and this shows in the tasks of the students. The quizzes are easy, the final quiz has all answers in hints which are not even hidden. That's actually a pretty good representation of the course creators' confidence in the students' knowledge after the course - we know you didn't learn anything, so we will just give you all the answers. Concentration on the course goal. The course is too short for trying to pack all the information in it. The last week was interesting, but if I wanted to learn about study design, I'd take a course on Study design. A lot of topics can be described as 'Understanding and Visualizing Data', and the difference between a well-designed course with thought-through structure and this course is that the good course is focused around the narrow subject (e.g. using Python for understanding the data) and delves as deep as possible instead of throw in different topics that are related to 'understanding data' in such a short course. And one last thing I would like to bring up is the students teaching in the course. I understand that it was probably the project they got credits for, and the professors thought that it's be a great practice for them. This is a great initiative, but the Coursera students actually pay for this course, and, I am sorry, but the students lectures were bad for the most part - the explanations are not coherent, the repetitions, the 'we are not going discuss that' (then please structure the lection the way the you don't use the function you don't want to explain). While it's understandable that students need more practice in teaching (they are students after all), the question arises as to why one should pay to listen to their 'end-of-the-course project'.