I think we need more hands on lab practices.

Informational, but too easy.

Good practice project

not very explanatory

Nice but very basic.

Too hard to follow

Encouraging

good

This course has too many issues:

The assignment rates items that are vague: If the quarterly data is demanded, then it should be spelled out and not just "the revenues"! To grade on this is ridiculous!

I was not able to see any links in the reviewed assignments - but I am starting to suspect I cannot see the Watson links! I could not get a Watson account, as the creation failed repeatedly despite me trying to troubleshoot it!

The used python version is old. Methods such as .append() are deprecated now! Also, who uses that python version locally? I would need a new env just for the course...

Some installs or imports gave warnings and failures, for example, for not using the version the employed jupyter version was expecting.

I got full marks despite some misunderstandings due to the vague new(!) text, because the people apparently seriously had enough. I am angry and embarassed that I deducted points in my reviews, as I followed the stupid guidelines! Do note that the new assignment version has new and vague texts. I lost all my previous work when it was updated too. Since it was some time I did it, I could not remmeber the quarterly shennenigans, for example. Kindly do not ask me to repeat this stupid assignment by changing the tables to quarterly, this is utterly repetitive. At least the weird tip to take the [1] (second) table now makes sense again. I marked it as an error in my notebook, as the yearly was demanded by my understanding of the text!

The exam should have been a new problem with at least some slight variations. It should be clearly spelled out what is expected.

I don’t Understand why this is a standalone course instead to be a part of the course on python for data science, most contents are in common and here they are just repeated.

Main lab (used for final assignment) is too easy and guided, most questions say also how to do easier tasks (e.g., get first/last rows of a dataframe), thus it is not challenging.

At the end of the lab I was expecting a discussion and explanation about the meaning of downloaded and plotted data. As said before, explanation is an important part of the data science methodology.

Outdated code that doesn't work properly in watson studio required for the final project. The function they wrote and want you to use to make a graph is unusable. This means that after completing the project on watson studio you have to go in, transfer the project to your local machine, and run the code there. They need to retest their course material in their chosen platforms. There's no reason that it should not work in IBM Watson Studio as this course is being taught by people from IBM.

Could really use more than a few examples for different parts of the project. A couple of examples of where the graph was used would have also helped. I think at least one extra week of prep for the project with a couple of examples would have helped greatly. Also, I am new to this type of coding I know a little html and php but for me it would help to not only see single lines of code but a full page of code to see how it would run or execute all together.

Too much information is presented at once without being given time to absorb it. Not enough practice with the topics presented. Some topics are just glossed over in a couple minutes. I had to visit other tech-related sites to figure out how to do some things that were asked in the final exam, since the IBM lessons didn't adequately explain them. The speaker's voice volume is inconsistent from video to video.

Good content but light on programming fundamentals, so defining functions and how calling them works are absent. Be wary of a steep learning curve compared to previous courses if you aren't already a programmer. Much worse, the final is just actually broken and your passing is up to luck depending on who reviews it and whether their browser will properly display the charts you work to create.

There are some useful practise labs but the code is outdated and the use of IBM Watson seems to have been removed. It seems this is so the instructors don't need to update their code base which doesn't run on recent versions of the libraries or python.

The course format is not clear as instructions have not been fully updated. Poor quality course which could have been good.

You have to review someone's submission to finish the course. The thing is, if there is no submission available for you, you have to wait! You can not finish the class without it! Ridiculous! I waited for a while. But everytime I check, it says same thing: ''There is no submission for you to review right now. Please come back later'' You gotta be kidding me seriously!

Very informative project, however it was also very frustrating. Lab/notebook should be updated to ensure correct source data is provided when the assignment is started. Looking at the discussion topics, the issue I had was identified over 2 years ago and still seems to be an issue. However, following the topic I was able to finish the project.

One can learn interesting things but the final assignment is a disaster - it is very difficult to sign in to watson studio, one need 1 hour to find a way to do it. Apart from that later for peer review screen shots are needed which on small laptop are nto easy to do. Reviewing others work is also difficult as the photos are reallz small.

If this wasn't part of the certificate, I wouldn't bother. The project was way too easy and boring. I'm certain you could actually get full marks just by copy-pasting. I would've liked a more challenging project. I understand that would require more from reviewing process, but this is just ridiculous.

Final assignments involve matters that have not been seen during the course. Course should have more explanations, both video and text, and then more practice in order to settle what you have seen. It's not at all a beginners course; perhaps it could be for the matter but not for the explanations.

repetitive lectures. multiple typos. non working lab. no explanation of the lab code shared. spent multiple hours to resolve the code errors to resolve code errors shared by Coursera for assignment (learn from other sources than Coursera to resolve code errors)

I think it isn't a good beginner course for Python. There are some things that are not explained and other that are not well explained. However, if you are following the data science learning path, I recommend you do it so you can pass to the next one.

The exercises that are supposed to influence learning are spoon-fed. In order to truly learn the material, one needs to tinker with the material and explore possibilities. Unfortunately, this course does not facilitate that.

Project is rated by peers and the majority of your grade is based on their evaluation. Got points deducted because my peer did not know how to view the GitHub link. There isn't anyone monitoring accuracy of results.

Probably the weakest course I have taken on the platform. Very frustrating.

Increasing from one to two stars because of helpful comments in the discussion boards that finally enabled me to complete the assignment.