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.

Not impressed from an educational standpoint. The videos showed the code sometimes so briefly you did not get a chance to see what you would be dealing with. Then the syntax of it being a method being applied wasn't fully explained until very late. In the hands-on labs, you mostly ran the codes as they explained again but when you finally got a chance to start trying to type the basic codes they had you doing examples that weren't basic. Often throwing something at you that hadn't been covered or covered well. It's the equivalent of teaching basic division and then the first practice you do is asking students to divide fractions or mixed numbers. It did not meet the goals it stated half the time of what you'd be able to understand or do.

It is not structured well. For about 3 weeks, it is very basic Python and programming, after then just some hints on some Python libraries. The videos of the first part are redundant with the lab contents, afterwards they only cover a part of them. Labs use Python programming elements that aren't taught neither in videos nor in labs themselves. Videos' tempo is too quick, they are cut too tight, a bit of time might be needed to grasp what one sees in them. Tests are pretty easy, sometimes faulty or hard to figure out what the author expects as right answer. A 100% final exam took about 3 minutes to click through. There are barely any ways to report issues or review contents. I expected way more serious content from IBM.

1. I wish the labs had more doing and less reading. Especially since the labs and the videos cover pretty much the same stuff.

2. There are small errors everywhere in this course! It would be so much better if they just had one human take the time and go through the entire course from beginning to end, fixing any mistakes. For example sometimes the quiz questions in the videos pop up right before they explain something, not after. One of labs contains a table that is illegible. One of the labs asks a quiz question not covered by the lab itself. And the first to graded quizes are before the labs and they clearly should be after. I'm only in week two and these are all the mistakes I've found so far.

Not exactly a beginner friendly course. Videos are of poor quality like watching a bad powerpoint, voice sounds robotic. Sentences are cut off midway for a quick quiz question. The skills labs were oke, good for extra info on how to write code though the assignments just jumped to a more experienced level than beginner half way through the course. The quizes are to easy...

First course in the specialization just feels like a promotion for IBM software, a lot of info on how to open and read files in different programs without having to use them.. Just explain what programs are used in data science and why. Then when you actually have to use them you can dive deeper.

I was really unimpressed with this course. There were many grammatical errors throughout the lessons, labs, and the final assignment. The lessons move very quickly and the quizzes are not challenging. I would have liked the quizzes to feature more code-from-scratch questions. The labs did have coding questions but were also very easy. I never felt like the material 'stuck' with me. The most frustrating part of this course was the final assignment which tested us on concepts NEVER introduced during the course. The discussion forums reflected this as many other students were very confused as to how to complete the final project. I would not recommend this course.

The videos are basically power point slides with an script that's read by a paid voice actor. You can tell because they sometimes place the emphasis on the wrong word, making it doubly difficult to comprehend the topic. Most of the labs are just pre-written code for you to run, rather than actual home work assignments that require you to write your own code. There's another course offered by U of M. Even though it doesn't offer an IBM credit, you feel like you've learned something. If I had to pick from the 2, I would pick the U of M course, and then take your code and publish it on Github and use that as a "badge".

Felt like being taught by a substitute teacher without full knowledge of the subject matter. In the rare case where subtitles were available, they were presented separately from the video and made them nearly useless. Questions often referenced a specific context from a video but then did not explain that context when referenced later in the chapter. Most questions contained the answer in the question itself (ie: You have a red ball, which word represents the color?) I will not be continuing with coursera, despite the financial aid I received, this does not deliver adequate value to be worth the expense

The instructions for IBM Watson need some work for those of us who are not actively involved with that environment. I also utilized my "free" processing in a previous class (due to the same instruction complaint) and had to contact IBM support to resolve over the course of 5 days. I suppose the good news is that I am now much more educated on IBM Watson through trial and error coupled with customer support. It should be noted that a set of instructions from IBM support and a 7 minute phone call resolved my issue which I am pleased with. However, it took ~5 days to log a ticket and get to resolution.

The title of this course is way too ambitious; this was more like a Python 101. Also, I disliked the way of just briefly presenting various things like functions, methods, libraries etc. without proper context. It would have been great to see how to implement these various stuff and why. Consider for example, previous module regarding presenting a case in Health industry - that was a fitting addition. Sure, there were some examples, in the labs but not in videos. Besides, one of the weeks had some cryptocurrency related stuff as an example. Really? You thought that was pertinent demonstration?

The IBM Watson space didn't work at all -- big disappointment. There were several places in the material that appeared to be pasted in from other courses or referred to materials that had not been taught anywhere previously. This created a lot of confusion working through materials and I even lost points on a couple of quizzes due to quiz questions being presented for which graphics were missing or course material had never been introduced. I"m concerned about whether there is a human who has responsibility for maintaining and correcting the errors in the course material.

While IBM's lectures have an irritating voiceover for lectures and can be stereotypically useless at times...they are usually really beneficial in one regard or another. However, this particular Python course is horrific and acts as if it were to help guide you through it, then pushes you into the deep end saying that you should know how to swim. I recommend taking a Python Bootcamp if you want to gain anything of substance out of this course, but then again, after the boot camp, you won't need it. For a "professional" company, this course leaves almost all to be desired.

The week 4 content is a complete 180 from the earlier content in the IBM Data Analysis Certificate. Spelling errors, poor video quality, incorrect information and exercises requiring you to use commands in Python that will not be introduced until later in the course. Some staff were also unhelpful in the forums; replying to questions about exercises and concerns with only a "Thanks for your feedback". I was very satisfied with the course until this point. Other parts of the course were well done and unfortunately made this one part stick out like a sore thumb.

This was by far the worse course. There is no logic going from one section to the next, the time estimates are way off. New sections should build on the previous sections. Also, the Notebooks simply repeats what's in the videos so why having me do both? Also, even by simply copying/pasting the code from one Notebook to another (to see the results), it took me more than the estimated time. And that copying/pasting is not good practice. I finished this section with sub-standard understanding and retention considering the amount of time (and money) invested.

The videos and quizzes were fine. Some parts of the hands-on labs were ok, the final project was fine. However, the hands-on labs are NOT for beginners. They would start out just fine but then most had a last project that was just ridiculous- using functions and syntax that was not in any of the videos or earlier parts of the lab. It was so frustrating for me and the discussion boards are full of tons of other students with the same frustrations. The earlier courses were good, this course was poorly constructed and executed.

unfortunately I'm having to supplement this course with Harvard's EDX CS50 intro python as the content here is thin, rushed, and leaves gaps. It also introduces new characters and syntax inside the practice questions that it doesn't cover in the short "lecture" videos. IMO python should have been introduced right from the start of the Pro Cert and not 4 subjects deep, so by the time you got here you would be more established at with it, and not having to reach into other programs to address shortfalls with instruction here.

Contents are simple and easy to learn. However, It covers basis aspects of Python programming

But I expect more in-depth practical examples and exercises. I think the course should includes more projects. The most confused part is about IBM Watson I personally not quite 100% sure what is the goal to include that platform in this course and moreover what does it actually help the future ai/data engineer or scientists. Because most of the time, I was talked about configurations and stuff which I am doubt it will be helpful!

Very disappointing unfortunately. In the video lessons and tutorials all the examples are far away from the "real world" examples. I didn't have the impression that the learning material helped me prepare for the final assignment. Also the explanations in the tutorial are very vague and misleading and partially using obsolete screenshots that don't match the current naming and navigation. I had the impression that I spent more time dealing with the lacking precision of the descriptions than with writing the actual code

I took this course as part of the Data Analyst Professional Certificate and have never worked with Python before. I thought this would be an easy(ish) introduction to Python, however this course is not for beginners. The first two weeks are easy to follow, but the videos and exercises for the rest of the weeks were too fast and too shallow. I had to supplement this course with one from CodeAcademy to feel comfortable with the material, and I still don't feel that I have a firm grasp of Python, Pandas, or Numpy.

This course was just irritating on so many levels. While it does give you an introduction to Python (again many free tutorials online) some/most of the end section exams have 3 questions which is just crazy. Many questions within the videos appear before you get to the answer in their explanation. I found this frustrating on many levels. The course uses Juptyer notebooks without ever explaining the interface. This is the first Coursera course that really makes the question the quality of the course on here.