It is somehow just an extension on R Programming course, with many unnecessary details that will be forgotten in a few days after the course.

The video is simple and good.

But the final project and some test are too hard based on material presented.

Also staff's support are not good.

Unfortunately the course's lectures are quite outdated, so you won't pass week 1 without all the research done by yourself.

I learned "ask Google" and dplyr, nothing more. Not as satisfied as with the other courses in the specialization.

I started this course but I don't feel that the material covered reflects the material tested in the quiz.

The only interesting part was dplyr. The rest was too confusing, with lots of lists and no explanations.

The information in the lectures is very stale, which makes it extremely frustrating to learn from.

Messier than the 2 previous courses. Lacks explanations for codebook in the peer-graded exam.

The course material needs update. There are code snippets that do not work.

This course could have been better. It was all textual and it got boring.

Final assignment is not well detailed, and may cause confusion.

This course lacks projects to implement the skills we learn.

The course was a bit mixed in terms of its quality.

Guidance for assessments could be a lot better

Very poor instructions for assignments.

waste of time for software engineers

Too many things in one place

Worst class in this series.

too superficial

Lacking focus.

Boring.

2

This course is about getting data from the web and processing it using a computer language and packages in that language that are under active development. There is a github repo with course content and other electronic resources that are made to be easy to update. It has never been updated, even once since the course first went live 4 years ago. There are many broken links, several new features and bugs in packages that make lecture content obsolete or broken, errors found by students, etc. None of these issues have been addressed, even once, in any of the material, including the extremely easy to update content on github. This is disappointing and not very professional. Additionally, many of the notes are not particularly good to begin with. Much of it is essentially cribbed from other online tutorials, examples in the documentation, and in a few cases, someone else's (also broken) lectures. Take this course if you want a study group (the forums are actually quite useful) to help you go through 4 year old lectures rehashing online tutorials from 4 years ago about a topic that changes pretty quickly.

Made it all the way to week four and decided to drop this entire specialization. The data set in the final project is poorly referenced (despite the code book provided). The data set comes in 24 text files you have to merge (which isn't a problem in R) but what is a problem is when you don't understand what the variables and observations are. Perhaps if I worked in the medical field these measurements would mean more, but to a business major, they are incomprehensible with the limited documentation provided. So my assumption was, if I am having difficulty understanding what the final data structure should look like, others will be having the same problem......and its peer reviewed. How can I possible grade someone else

I have to date completed the first 2 courses in Data Science: Foundations using R Specialization.

Today I have cancelled my subscription for the following reasons:

1 Poor course design and delivery

Lesson contents inadequately covered and sourced, lecturers deliver a fast paced recordings with very little examples and references making it hard for beginner students to keep pace and find themselves unprepared for the required quizzes and exams.

2 Course materials needs to be updated and presented to facilitate learning , eg. often times students are referred to static links and too many many times new and un-familiar concepts/ functions are rushed thru with no introduction or explanation.