Too basic.

it is good

Too basic

too easy

Too easy

It's Ok

Shallow

hosome

爬虫部分很烂

great

good

good

cool

good

I was very excited for this course, but felt underwhelmed overall. I have some programming background, so expected the course to seem a bit slow having been introduced to topics such as basic data types in Python before. Week 1 material, for example, was simple enough to go through in less than a day. As the course progressed to more complex and interesting topics (e.g. APIs, Webscraping, etc.), however, the course seemed incredibly rushed. Instead of understanding the set-up, it felt as though the lectures simple flashed multi-lines of code. The labs similarly seemed to grow in complexity, but shrink in explanations. The labs often incorporated libraries or methods/functions that had not been referenced in the lectures, often with little added explanation/introduction. Similarly, the incorporation of the Watson engine seemed like unnecessary advertising. It was not necessary to understand Python for data science and AI as part of the exercises. So much time was spent explaining how to add arrays, while it took 4 minutes to go through summarizing dealing with csv, JSON, and XML files. It also seems that later lectures had more typos in slides, again giving the impression that the later sections were rushed. Even the final lab randomly referenced a library for processing image files, which seemed to come out of left field. The course could've stopped at Week 4 or maybe split the focus between pandas and numpy between Weeks 4 and 5. Or more focus on exception handling or classes and objects might've been useful. Use of Jupyter notebook is helpful (it really adds to nice-looking labs), but I think it would've been better to have files/resources that could be downloaded and saved locally. That way, any files written/read could also be inspected more closely. Given the title of the course (and IBM as the provider), I would've expected more. With the exception of the weak Week 5, the rest of the topics seemed more like an intro to programming (using Python as the language of choice), rather than a focus of Python for Data Science, AI & Development.

This is a course useful in getting you onto the ramp to Python and the Python libraries "Pandas" (provides tools for data manipulation and analysis, in particular the "data frame" matrix as known in R) and "NumPy" (provides multi-dimensional arrays and numerical computing). It also explains basic skills in loading and writing files in various formats from and to disk, getting pages from the Internet, how to perform client-server communication in the REST approach and how to scrape websites. Sadly, it feels like a "0.8 version" of a proper course, or a course which has been abandoned. Content is affected by confusing text, typos, omissions of important points, code in "non-pythonic" style, in some cases code that doesn't work, or looks like it deserved a refresh to latest notation (the matrix multiplication with the '@' operator isn't even used). Sorely missing are chapters on "gradual typing" in Python, assertions and unit tests, all of which I consider a minimal must-know. On the other hand, the "coding notebook" practical exercises (based on Pyodide running in your browser) are the right way to go. Drilling into details and looking up documentation on the Internet is unavoidable, indeed it will give you the understanding that you actually need. The course seems also unsure about whom it is addressing. I doubt that aspiring data scientist need extensive explainers about how vector and matrix operations work, those should be assumed known. Still, all of this should be easy to fix by IBM in a fortnight (I left them extensive notes, too). On the other hand, the questions that Coursera throws at you in the "graded exercises" are rather embarrassing, and need serious rework, I want more interesting question than multiple choice questions about syntactic details, word definitions and Python one-liners (which cannot be written in Python btw.). Make it better, guys!

Generally, the course is very informative and useful.

However, the Python for Data Science and AI course is anything but helpful.

The course requires the students to open a free IBM Cloud account, which is practically impossible in my case for unknown reasons.

The IBM Cloud service is essential part of the certificate program, as it is used in most of the courses, but, however, impossible to use.

IBM Watson Studio is a tool developed for Machine Learning and a part of the IBM Cloud service and an essential part of the course, but can not be used without a cloud account...

I think the above sentence says it all.

Through threads in the forum I requested a solution for my issue, which many other seemed to have. I received a few answers to my inquiry, suggesting a few workarounds like using a non-generic e-mail or at least a GMail, changing my network settings and so on.

None of them seemed to work.

The solution that was suggested at the end was to download the file from a suggested link, run the code locally and upload the result to GitHub...

Seriously?!... o.0

A few e-mails sent to IBM Cloud support remained unanswered.

The saga above, as well as the lack of exercises, non-working code in the lectures and LAB really made me question my choice of certificate...

The course does not contain much more information than the one you will find in the book by Murtaza Haider, parts of which are included in the reading sections of the course. If you are wondering if it is better to spend $35 each month on Coursera, or to buy the book for $20 and learn the same stuff... Well, I think you have your answer.

I would not be recommending the course to anyone, as I am not sure If they will cope with the frustration around the process of finishing it.

Sometimes I got freaked out when I completed a course with flying colors and still felt like I didn't understand anything. Unfortunately, that's exactly my experience with this course, Python for Data Science and AI offered by IBM.

If you are attracted by the title, or by the name IBM, then I advise you to stay away from it. There is nothing remotely data science or AI in this course. It should have been called Introduction to Python ,or Python for Beginners, but there are much better courses on this topic, such as those offered by the University of Michigan.

Here is why it is bad: All the videos feature the same robotic voice rushing through basic concepts as if someone is just reading from a textbook. I wonder if the narrator is an AI, not a real human. I could have overlooked that if the content is actually good. In contrast, its scope is very narrow, even for a beginner course. There is no walk through of common Python challenges and mistakes to solidify the concepts. and again, you are going to pick up more Python knowledge from other courses.

Worse, the exercises just promote rote learning and the ability to use IBM's products. There is just not enough practice exercises to help learners understanding. Most of my time spent on this course is actually dedicated to figuring out how to set up IBM Studio, or whatever it is called. I doubt anyone is going to benefit anything from this course.

I've always admired IBM's achievement, but it takes another skill set to deliver a good online course.

What the course covered isn't bad, but the presentation is far from polished. There are many errors in the text and in the video narration, and the videos aren't well edited. The errors are mostly small, but they disrupt learning and give the impression that the course was written in a rush, without sufficient proof-reading or testing. In addition to typos (including in the video scripts), the logic of the course also isn't great: methods and JSON, for example, are both mentioned many times before they are explained. Methods relating to specific data types are discussed in week 1, but methods as a concept isn't introduced until later. JSON data are referred to a lot, but it is only in the final week's lab that JSON is defined and explained. There are also long-reported bugs in the lab interface, which are not adequately investigated or fixed (reports of being unable to share to Github Gists since early last year, but the only responses to these are workarounds or "it works for us"). None of this inspires me to try other IBM offerings.

In short, rather than teaching students to code in Python or analyse data, this course is more of a "taster". It gives you some idea of how Python accesses and handles data, but it lacks the depth and practical components to really give a student the skills needed to tackle data science problems - to even know where to start. There are way better ways to learn Python, and I presume way better ways to learn data science.

The course content was helpful, but brief. I found myself browsing for additional information to grasp some of the elements of python. For example, the numpy 2d elements went by very quickly.

I found using Jupyter awkward, and I feel the user would be more engaged in the course if they had the choice to operate on their own IDE; install their own modules; and create their own minature projects to get a better handle on Python, and get better at coding in general. For example, I would be able to tell you what is missing from a block of code that covers an example of a function, but I would struggle to write the same block if prompted.

Although I have a very good knowledge of the theory behind python now, I came out of this course only marginally better at actually writing code. If that was the intention, then I woudl give 5 stars, but since this is the prerequisite to the next course called Python Project for AI and Application Development. It sounds like this course (Python for Data Science, AI & Development) was meant to get you well versed in actually writing Python.

The screenshots in the labs for making accounts in IBM cloud website is outdated.

There are too many repetitions.

The labs are nothing but running the already written code, hardly one or two lines of code is asked to be written, that too in not all the labs. The already written code, which has to be executed are same as what is shown in the video, not even 1% of difference. It should be about the same topic but a different problem, similar to what is shown in the video, not exactly the same. Instead of giving out fully written code, it should have blanks to fill in or hints given to the student for help. Just by executing the already written code doesn't help in learning. Writing is as important part of learning (especially code writing) as reading. Writing helps brain exercise, reading does not. Also, personally, I don't like this presentation form of learning; it's kind of boring and mind gets diverted very easily. I am having to rewind too many times.

It seems like he instructor created the topics and given notes to a junior to work on the course creating project!

The first three courses in the Applied AI specialisation are much better than this one. During weeks 1-4, it remained a mystery how you're supposed to apply the content of the videos in practice. The videos are ok, but by far not as good and easy to follow as other videos here on coursera. In week 5, the final exam felt like completely out of the blue and not comparable to the weekly quizzes (which are far to short and easy to really be informative about where you stand).

I would not recommend this course, I myself only followed through with it because it is part of the specialization. I would say I got a first impression of Python, but so far I wouldn't trust myself with writing any code and I don't feel like I've gained long-term knowledge.

If I ever really needed Python in my job, I'd start over and take another course.

Note: in my experience, this is an exception. I've liked the other courses I took on coursera (some of which were also by IBM) much better!

I wish there was a better way to implement being able to practice writing codes before swiftly moving on to other types of code. I found it easy to read codes but had a lot of difficult writing the codes in python when prompted and found myself having to either look up codes or check cheat sheets even though I had done a lab practicing reading/writing X amount of codes. Week 5 also felt like absolute hell. I felt like I wasn't fully grasping the material let alone being able to confidently work within python on HTML, URLs, etc. I think it could be better taught and organised given that this is virtual and so there is difficulty trying to gain clarity on how the info in the videos are explained. Perhaps there should be a week solely dedicated to a bunch of hands-on labs to practice in python. That way, it does not feel like information overload and not knowing when to apply certain information in practice.

The lectures are very focused, which is positive. Unfortunately, there are no lecture slides to download. The lecture voice is possibly machine-generated and there is no indication of empathy, which is kind of a cultural shock after having passed four of Dr. Chuck's great python courses. For unknown reasons I had to open each quiz twice to be able to submit. IBM's Developer Skills Network hosts the Jupyter notebooks and I wasted a lot of my time facing missing notebooks, timeouts, dying kernels, and slowly starting Docker containers. I'd rather like to download the notebooks and run them on my local machine (I found out how to do this by end of the 4th week). The notebooks are filled with IBM advertisements. A registration at IBM's Watson is necessary, but the setup descriptions are outdated and the setup is not uncomplicated.

This course was sloppily and hastily put together with very little concept of how to teach well. Hands down one of the worst courses I've attempted. The only good thing about this course, is that it gives you an idea of all you should learn for data science ON YOUR OWN. But you can't just rely on the explanations and samples on the video lessons to actually learn the content, especially with ALL the typos that will seriously mess up your understanding and learning. If you follow along the video and run the code along in your python environment, many WILL NOT WORK. You will end up just going to ChatGPT to ask what's wrong, And then you will learn that this course is just such low quality. The labs are much better, as you do get hands-on, and you can run on their cloud platform. But overall, I found this course very subpar.