This is described as a beginner's course in Python. In the labs, the examples you are given are basic and easy-to-follow examples that DO make sense. However, then, in the activities, they give you wildly more difficult tasks are nearly impossible to create on your own without looking at the solution while writing the activity's code.

If you would like to have a basic course on Python that will actually train you on how to use it and be more of a staggering difficultly level, I would recommend NOT taking this course.

Trash course. It is only about basic images shown on the screen to teach with an AI like voice over. You can't find a part of video where you don't hear any sound for 2 consecutive seconds. If you are a robot, this course is for you, if you are a human, I don't think you would understand anything. Any 2+ hour video on YouTube would teach you the same, and it would be engaging at least, unlike this course which is a complete garbage.

"I enrolled in Python for Data Science, AI & Development course with high hopes, but I was thoroughly disappointed. The content was poorly organized, making it hard to follow. The lectures were dry. The assignments were vague and didn't align well with the course material. Overall, I didn't gain much practical knowledge or skills from this course. I wouldn't recommend it to anyone serious about learning Python."

Quality of this course was pitiful. It is chaotic and hard to follow. Would not recommend to anyone with no previous knowledge about some programming language. Other courses in "IBM Data Science Professional Certificate" is way more better.

I'm not sure I'm supposed to be learning from the Labs. The videos don't exactly show you how to perform the tasks and the practice Labs jump the explanations for several fundamental steps. Frustrating to say the least.

I was expecting more about Data Science, as mostly was a quick introduction to Python. It took one afternoon to do 5 "weeks" of work. Perhaps too easy to be connected with IBM.

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Unnecessarily long. Each lesson you watch a video, then read the same thing, and then read the same thing in the lab. It does teach you Python though

Bluff..AI *is not covered and the whole course material is for only python basics. Need a revamp which will include AI-based materials

My goodness this was wrought with errors. There were a lot of incomplete concepts and poor examples.

This course started well but then suddenly became difficult to understand and I lost it a bit.

It is not suitable for enrolled students with minimal knowledge on programming and Python

Too difficult and too fast. Impossible to do the lab work by just listen/ read it.

Poor organization, poor explanations, multiple errors, ineffective support.

not so deep in python and starting from week 4 started to be messy

Not for someone with zero python knowledge

Failures in grammar, logic and wording.

not Beginners Friendly

too easy

Worst of the IBM Data Science Prof. Cert. so far. I disliked almost every part of it.

Typos throughout all slides and exercises, labs almost never worked online and had to be downloaded, transcripts are off, responses in the discussion forum were late and/or not always friendly (especially to some seemingly knowledgable participants who pointed out code flaws or such - just followed some of the threads and was irritated by some responses), but amongst all the flaw that makes me think about quitting this certificate this course is part of: absolutely no clear framework obvious. What are the main questions this course answers apart from just giving us a bunch of information? Where are we and which goal will we reach, i.e., what will we be able to do with the knowledge? As someone who has been teaching at big international universities, I would argue that the story of this course is lacking or absolutely unclear and there was not much though given in terms of pedagogy. I passed the final exam right away with 100% even I didn't feel like a learned a lot I could really apply. That being said: I don't want to participate in a course just to pass and get the badge. Instead, I would like to learn something that could help me right away. Unfortunately, for this course, this was in no way the case. I very much hope the next one will be better or I will quit and leave Coursera.

I struggled to learn with this course. I have some experience coding with Python already and feel like the Python beginners course from University of Michigan (Python for everyone) was much better at learning Python for data analytics even though the course objective was more to learn about Python rather than data analytics. I didn't like the clunky and cluttered feel of programming in Jutyper. Previous courses I've taken in Python, the video or lecturer would usually code in the shell or an IDE and you'd see the code being done, you'd go practice in an uncluttered IDE where you could debug things easily and I felt like I learned quite easily. This course kind of just lectures through the code and uses visuals to represent the program which I feel is not a good way to teach coding/programming. The beginning weeks were easy because I knew all the content, but I could see it was taught using poor methods in my opinion. When it got to the material at the end which I'd never learned before, I could really feel how slow and difficult it was to retain the information being presented in the videos. I think it would help a lot by doing videos in an IDE and provide a textbook to easily refer back to the content instead of having to click through a video to do so.

I hate giving a bad review for this course, especially considering the immense amount of content covered in it. However, the uncountable mistakes in the labs, with dysfunctional solutions, hints that are in the wrong place and a general lack of practical exercises, especially in the end of the course, make me feel like I have no choice. There are far too many instances where code goes completely unexplained as well. I've spent way too many hours trying to understand what I did wrong in some exercises, only to find some of the given solutions to be wrong themselves. Furthermore the course completely fails to teach the fundamental basics of python in the sense of how to write python on ones own computer, what software is necessary and how a script can be run. The sole focus on code means that I leave this course with basic theoretical knowledge of how to write code, but zero knowledge in how to actually do that. I think the course either needs to be reworked extensively or rebuilt from the ground up. The sheer amount of inconsistencies and errors unfortunately leaves me no other option but to come to that conclusion.

I recently took the Python for Data Science, AI, and Development course, and I must say, it was not the experience I hoped for. The course is poorly structured, making it extremely difficult to follow and understand. There are significant gaps between the theory presented and the practical exercises, leaving learners struggling to bridge the two on their own. Throughout the course, I found myself relying heavily on AI tools to make sense of even the most basic concepts. Unfortunately, the explanations provided were either too vague or overly complex without sufficient examples or context to truly grasp the material. This course left me feeling frustrated rather than confident in my abilities. I wouldn't recommend this course unless you already have a solid understanding of Python and just need a refresher. For beginners or those looking to gain a deep understanding of Python for Data Science and AI, I suggest exploring other resources that are more structured and beginner-friendly.

Pros - The video lectures are easy to understand and follow. The vocal clarity of the speakers is great. Cons - This course offers a very high level explanation of some concepts and then introduces brand new, more complex, concepts in the lab. This is okay, but in order to be really successful you need to be industrious enough to do further reading on your own. The lab is 100% dependent on the Jupyter notebook, which has some bugs in the libraries provided. I prefer to use Pycharm on my own machine, and had to figure out how to run some of the code locally (the asynchronous functions in particular did not work out of the box). At the end, I feel like I am prepared enough to take a multiple choice test on data types, data structures, and some API related concepts, but I do not necessarily feel prepared to actually do any coding.

The worst course I've taken so far. I used to learn on udemy before switching to Coursera because of more well-known providers. This entire IBM Data Analyst Professional Certificate has been mostly way too easy. Explanations don't seem to be technically accurate and simplify concepts a lot. Especially the in the Python module, the basics of programming are skimmed through while the exercises are designed at a significantly more difficult level. A lot of students seem to be frustrated with this part and apparently, nothing is being done to improve this course. Why people are still taking it? To have some certificate from a reknown company on their LinkedIn profile. Do people actually learn in this course? Yes, but only because nothing is being taught here and students are forced to do their own research to solve the exercises.