It was a good course that follows steps clearly and effectively. However, I cannot rate it higher that 2 stars for a very important reasons: Big Parts of the Final peer-reviewed assignment are not even covered in the course!!! I had to scour the internet and find my own solutions (and many others clearly had the same problem as seen in the discussions section). This is a big problem and needs to be addressed as we should be tested on the material actually learnt!

Also, almost every video repeats how the data is 'cleaned' which is good once or twice, but unnecessary the 15th time.

The final assessment was not covered in class, and it was very difficult to figure out how to do.

I did not like that some assignments do no rely on the material that was given in the course. For example, data visualisation with Artist layer was not covered in details in the course and you have to spend tons of time on Internet digging out how to implement that. This is a waste of time, I need a course that gives me complete and structured info, not a course that sends me out to explore the Internet.

It is a strange course and the worst in this specialisation so far. In one week 50% of the video is how the data is prepared. That would be ok if it won´t be the same video snippet 4 times. Also the relation of video vs. reading is 1 to 6. In one week is only 6 minuites of Video and about 1 h of ungraded assignment.

The final assignment isn´t solvable with the given code or examples. It is ok that one had to google for code snippets, but this is far too much.

Final exam was frustrating. It took longer to complete than the rest of the course combined. Questions were included that were not part of the course including the need to reset keys. Peer review was almost impossible since I could not read the tiny screen shots very well.

Data could in most cases not be loaded, making the labs useless. Also, the videos have unnecessarily much redundancy. Really didn't learn much from this course.

worst instructor I have ever seen,

very few instruction but the assignment is extremely hard!!

The course is very inconsistent, it repeats the same one minute in all of the videos, when reviewing the dataframe. Many times some things are asked without providing previous explanation, and the final assignment is also an example, I had to search all over internet to resolve it, because I couldn't find any reference in the content provided.

Compared to other courses in the IBM Professional Certification catalog, this course has some noticeable deficiencies.

First, the overall content of the course rather confusing. The very first lecture focuses around efficient 'less-is-more' figure design, which I certainly agree with. However, much of the course (and most of the tested material) focuses on making extraneous graphics such as waffle charts and chloropleth maps in situations where a simple bar graph would be the most efficient way to present data. Meanwhile, the standard module Seaborn (which is EXTREMELY expansive in data visualization utility) is given only a single 2 minute lecture.

Second, unlike all other courses I have taken in the IBM certification, the assignments and workshop sections of this course are largely unhelpful. In addition to my point above, the workshops focus on manipulating aesthetics of simple graphics (i.e. changing colors in a bar graph) as opposed to showcasing the broad number of figures that Python is capable of generating. This left me disappointed with what I took away from the course in terms of usable knowledge.

Finally, the final assignment is arduous and poorly documented. There is no structured notebook that provides guidance on solving the problems, which is particularly troublesome when rendering uncommon figures such as chloropleth maps. I found that I spent >80% of my time on the assignment chasing down unintelligible error messages, as opposed to developing a real understanding of the logic behind generating graphics in Python.

The majority of other courses in the IBM certification have been very well designed and educational, I just feel that this one in particular has a lot of room for improvement.

I would give zero if the system has. An empty course that you almost cannot learn anything especially when you're going to practice.

The course material is not sufficient for completing the final graded assignment. It required many hours of internet research to collect the details necessary for the final graded assignment.

The course materials are poorly structured. Labs are not well-designed and not friendly to students with little experience. This is not a very effective course.

A lot of the work in the final project was not in the course (in either the content or the lab work). There were plenty of topics covered that could have formed a challenging final project without asking us to do things that we weren't equipped to do by the course. Fortunately, I was able to find what I needed but after putting all of that work into the course and labs, I shouldn't have had to spend as many hours on the final project as I did. If those are things I needed to know, then the content was inadequate. If I didn't need to know those things, the project was poorly created.

I felt that the curriculum was not structured in a manner conducive to learn the material. Too much of the training and lab work was to execute already prepared commands without an explanation as to why we were doing using these commands. In addition, the training material could have used more detailed explanations and then lab work allowing the student to apply this knowledge. Instead, you watch a lot of abbreviated videos and then do a single lab exercise that has the student try out certain aspects of what was covered.

For the final exercise, there was so much that wasn't covered in the course material. This took many extra hours searching for how to do things. While this type of searching might have been helpful in preparing for use of Python/Data Science outside of the course, none of the course material to date trained us on how to interpret the reference material. The final lab should be challenging, but not to the extent where the material presented doesn't provide the method for solving these problems. The curriculum designer should take this into account when building these classes.

Lastly, the tooling used for the course did not work for over a month. This added hours to my training. It also meant that I was never able to complete some of the lab exercises, or use the completed material as a reference for the final exam work. The Coursera help desk was not at all helpful in informing students of the issue and when it would be resolved, and only gave advice to try to recreate the same exercises (without the supporting code) in other environments. This added many hours to the time that I needed to complete the exercise.

so far ive spent the most time on this course . This course has around the shortest estimated time to complete. The number of discussions in week 3 is around 5 times more than the Python for Data Analysis course.... why we may ask?

The plotting of views are overly dependent on syntax. The information gain in trying to figure out that syntax is negative, i.e. up until this course i was enjoying my first experience with python. 12 hours later, i cant get a chloropleth chart to work because something as minor as column orders were incorrect. Very frustrating!

That said, perhaps im spoilt. Im a tableau user and its fairly straightforward to do data visualisation. It is rewarding. and flexible.

The bar is thus set, and so far data viz in python is frustrating.

No mid-lecture quizzes

End of section quizzes test rote memorization

Narration is poorly done

A lot of functions and lines of code weren't explained they were just left to be figured out by the learner. While some lines of code could be understood without much explanation, others were too complex for people new to programming (which most people taking this course are).

ZERO support from our teachers, assignments that have little to do with what they teach us (Videos don't even have any information explaining core concepts) Most of the learning was done by Google. Quite annoying to be honest.

This is the only course so far where the videos have not been helpful. They were repetitive-- we do not need to learn how to do the same transformation on the dataframe 10 times. The videos didn't show or explain any of the code like in the previous courses. The final assignment covered code we didn't even look at at all during this course.

This is by far the least helpful course in the IBM Data Science Professional Certificate series. The videos contain mostly repeated info, so you really only learn much of anything from the labs. But even the labs are very basic compared to what you are expected to do for the final project. If I am paying for a course to teach me something, I shouldn't be teaching myself with help from Google. I can do that on my own, for free. If the "help" offered by the teaching assistant in the forum is code that doesn't look even vaguely familiar right after going through the course, doing all the labs, and getting a 100% on every quiz, then there is a big problem. I would absolutely not recommend this course

Kindly revise the course content and match it with the final project. And definitely re-record videos without annoying voice of the instructor and repetitive sentences about the data.

And seaborn should be mentioned more than just 2 -3 minutes video.

Course does not reflect the quality of the IBM courses.

Need more detailed explaination of artist layer. Very confusing. Questions on topics which are not covered in syllabus.

The course labs had broken links which caused issues with several of the students. The quizzes also had several question choices where two of the answer choices were the exact same, leaving the student to guess. Not to be so critical, although the datacamp classes are much more effective when it comes to learning.

A lot of information that was required to complete the assignment was missing. I had to look up lots of other sources to be able to complete it. I understand this maybe was the course setter wanted but it felt like the material was overlooked.

This course was really well designed. I've taken the preceding courses and I really connected with the format of this course. I liked how the labs really explored different options and played around with the code in a variety of ways to show a more complete picture of what the code instances can do. I especially liked how sometimes the labs would purposely use incorrect code which users might enter, and then explain why that code didn't apply or work for given scenarios.

I liked how the videos (specifically regarding Canada dataset) would repeat the cleansing of the data and introduction of the data for each type of plot because it really reinforced the concept, however it could have been better if the corresponding code were displayed alongside its effects instead of just showing its effects because then it would drive home the code and the concept instead of just the concept.

I also liked the fact that the final project asked students to make connections beyond the individual class scope, as a way of teaching that mimics real-world projects and learning.