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.

Again, IBM must rethink who is in its curriculum and instructional design teams. The practice quizzes merely copy the questions from the videos, which ask you to evaluate what a code block will do. This would be fine IF THEY PROVIDED YOU WITH THE CODE BLOCKS. But, they don't. They rely on you recalling what the code blocks were. At BEST, this is problematic and fails to account for the lived experiences of the student, who may be taking the quiz well after watching the video or doing the labs. At WORST, it shows that this course is nothing more than a bald cash grab designed for people to speed through, thereby making the whole certificate meaningless. Source, I'm a curriculum and instruction expert in both training (Ph.D. in education) and training (active teaching professor).

Basically just an interactive advertisement for IBM's new product. Videos aren't very helpful as they show snippets of code without the context in which they would be applied in an actual program. Labs are okay but they use the aforementioned IBM product, and it honestly isn't that great and it isn't something someone using python to do data science would use in a professional setting. The information given about Pandas and Numpy is embarrassingly lacking so the point where doing the final lab is nearly impossible unless you already know what you're doing or you search for information elsewhere. Not why I pay money for Coursera. Wish I could have my money and time back honestly.

Terrible. Rushes through concepts in 3 minutes, then dumps you in a "lab", which is a garbled word doc. I literally checked twice to make sure I didn't have the playback set to 1.5. It's like Hemingway but not in a good way, where it is succinct to a fault. I found myself having to listen back to video multiple times because it shifted concepts and I was still processing the last concept. To truly learn something, you have to process it deeply for retention. Some of the early material that reviewed data science as a whole, this pace was okay, but for the Python, which is foreign to me, I couldn't keep up. I'm moving to datacamp.

terrible course. i hope not all beginner python courses are like this one. the issue is not that its difficult but there multiple recurring instances of undefined items, unexplained code etc. for a beginner its a disaster. by module 5 you have no idea what is going on. whoever designed this course should be replaced and the course redesigned truly for beginners. for the record i am a finance professional with advanced degrees, i have prior basic programming experience and i also got 95% for this course. terrible course (just shaking my head), waste of time and money

I'm not sure which parts of the lessons are advertisement and which parts are actual exercises that need to be completed. You are instructed in each segment (so hundreds of times) to not forget to press Shift-Enter for your instructions to be run which is annoying for something so simple. Then the lesson throws you into the deep end by telling you to get an account of gethub using gist to save your jupyter thing and you end up completely lost after clicking on too many links. I might have two accounts of gethub or none--I have no idea.

I was not a fan of this course. It feels like this course was hastily put together. The practice quizzes for the first 3 weeks are just repeats of the questions brought up during the videos. There are often concepts that are either not brought up or explained fully, that are then being quizzed on. There are typos throughout the labs. If I hadn't already had a fair bit of python knowledge coming into this course, I'm not sure I would have passed it as well as I did. I would recommend learning these concepts with a different course.

This course claims its taught Joseph Santarcangelo but yet every video is a AI women's voice that is just reading you bare bones information. This course provides information but does not teach. Coursera should be ashamed for providing lazy AI content so they can claim they are providing a course to take your 50 dollars. I do not recommend this course and instead please leave this site and go take the "Complete Python 3 Bootcamp" on udemy. I did that instead and I felt like a real human was teaching me skills.

I had been taken several courses with Coursera but this data science specialization lacks good planning and clear directions to complete. I asked many times the same question. I was stuck in the last assignment of week 5 and requested help but the responses were not adequate, I contacted the teacher assistants and even the instructor and just received an email to contact Coursera services. They just ignored me; I'm not sure how many students they have but several others have the same issue with Week 5.

I'm really disappointed with this course. I'm totally new to Python and I can confidently say that I merely grasped around a 20% of the whole content. Some videos are extremely short and fast. If you are new to this programming language, then it will be extremely hard to follow in those cases. And regarding the Hands-On Labs, some of them are good, but a lot of them teaches stuff like you are already familiar with Python, and not a begineer.

The third party tool is completely unreliable and it makes this course dissapointing and frustrating.

I wasted too much time trying to make it work, since it was either under maintenance or issuing bad gateway errors.

From the discussion boards, I saw that I was not the only one getting this type of errors, so it would be nice if a better tool could be used or maybe provide alternate instructions to use those Jupiter pages in Watson.