Felt like a lot of the lessons were more about just following the directions or structure of the videos and not really learning the actual language of SAS or Python and how to be creative with it. I feel like I know how to use them at this point, but only for the specific commands we were instructed on. However, material is clear and easy to follow. I am not a fan of the overall Coursera structure of peer graded assignments because it seems pretty arbitrary or you may do the work and just not get the right number of reviewers and then you're screwed.

The course has its positives, but overall does not perform well instructing on the use of the two statistical software offered (SAS & Python). At the beginning they offer multiple data sets to use and formulate a research question, but all the examples utilize only one data set and do not cover the differences you might face with the other data sets - leading to a lot of missed opportunities. Additionally, the tutorials for using the statistical software do not lend themselves towards a thorough understanding and more to a route learning.

The course is a very good introduction to Python for data exploration and management.

That being said, it focuses too much on categorical data analysis, and I felt the transition to quantitative/continuous data was not very well done.

Moreover, some more explanations about several Python functions or coding choices could be better explained.

But it's encouraging to see this type of statistical course for Python and not R!! Again!

A decent start for anyone interested in learning the basics. However, please make sure you add extra efforts from your end in understanding stats concepts if you are totally new to the subject as well as browsing things related to usage of SAS or python. The course touches only the basics so it is up the learner to explore and learn more about interested statistical tool.

They put a lot of effort into this course, but especially for the videos it was a bit too much. So many different visual backgrounds, sounds, music, text floating it... It's as if they just wanted to use everything, while never thinking about when it would start being too distracting.

The slides are excellent and instructions are clear and to the point. But that point is very limited. The main negative about this class is that there is absolutely no student/TA/teacher feedback and you're pretty much on your own learning.

It was good overall, some of the course materials were a bit sparse. You had to do your own research into getting packages working in spyder etc.

I'd probably recommend that learners learn some python separately and read the docs for pandas.

A great idea to create a project based online course. We should focus on application based learning not on lectures. You need improve this course still. 4 weeks are to short.

We need more deep and more weeks.

It is a good beginners course but I think there are better lessons in SAS and Python if learning these is your goal, and the content in data cleaning and visual analysis is very basic.

Course content good but a little too basic in my eyes. I think the addition of more functions in SAS/Python would be useful. Having to do peer reviews is also not ideal.

Nice course to learn Python and some graphing libraries besides doing your own study from real-life data

good but i thought it will cover most of SAS technology. But it has covered basics only .

Course material needs to updated to reflect current software updates.

Very superficial. Too few in statistics, too much in python and SAS.

So many unanswered questions in the forum. Peer reviews suck

The course is good, although it goes a little to fast.

course should've had deeper python content.

The content is OK, but that's about it. Near zero interaction from staff, short videos leave a lot to be desired, and worst of all, there seems to be a problem getting everyone's assignments peer reviewed (which makes no sense).

There is a somewhat comical thread wherein staff direct students to contact coursera directly via a "contact us" link on a page, but get this, the link isn't there. Students repeatedly pointed this out only to get directed again to the non-existant link. In an online forum it's important to actually address concerns. Replys that avoid directly addressing concerns sound like automated messages. And that's the feeling I get here.

This course requires a DIY attitude and a willingness to proceed without feedback.

If you're looking for a good example of online learning, look elsewhere.

Some of the presented python code has been depreciated; this would be particularly challenging for students new to the code. The use of quantitative variables is not well described at the start of the course which would add challenges for students who choose these variables and are marked by students who have not. Having beginners marking beginners is not an effective evaluation technique. I did not receive useful feedback on assignments; typical statements included ".", "good work", and, "needs improvement".

A very beginner level course aimed at starters who dont have much experience with Data. Emphasis was on teaching Data Management with area of our interest. In Order to support this, assignments didnt had clear objective. I would have learnt a lot more through a course had they been more defined assignments.

Not a course if you are looking to be ready for data related interviews. However, this course will be okay if you are working on some other field and want data management as an extra skillset.

It is too small and shallow. Whole specialization is actually one full course split into 4 tiny parts.

I'M only 5 and i do not understand what x=x+5 means so make the quiz eazyer thx

Took way to long and tomuch effort to get assignments evalueted by peers

Some of the python code is not actual and has been deprecated

The highest video production values of any class I've seen on Coursera so far. Unfortunately, that's the best part of the class; the support is simply nonexistent (a post by someone associated with the staff from a previous run of the course suggests getting help troubleshooting Python from Coursera staff), the instructions for the class software are out of date, the source material for the assignments is sometimes unsuitable to the task you're asked to perform (e.g. statistical analysis on a data set aggregated from disparate sources), and the pretty videos are at times completely unrelated to the actual assignments (the very first week with the literature review). Dropping this specialization like a hot coal; it looked quite promising, but the aggravation over the poor organization and lack of support considerably outweighs anything I could get out of it.