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This course is.... odd.

It's a jumbled mess of videos read in robot voice, "labwork" which is "optional", and way too easy to complete (many of them just involve hitting shift+enter over and over again)... assuming that you can get the lab to load in the first place, quizzes which insult your intelligence with how easy they are, one random module (APIs) which is WAY WAY more complicated than anything else in the class, and a final assignment that's surprisingly a nice review of the class. I actually had to go back and re-watch the videos to complete it. However, everything I needed WAS in the videos.

And here's the weird thing: all the nonsense about setting up IBM Watson, IBM Cloud Services, IBM Speech to Text, IBM Translator, IBM... (you get the point, you will sign up for a TON of IBM services, and then have to figure out how to get them to work together)... is probably the BEST part of the class.

You can learn Python lots of places (and you should. Please don't count on this class to teach you Python. Go to codecademy fer cryin' out loud.) But where else are you going to get hands on time working through frustration and technical problems getting IBM Cloud services to work? The course doesn't always hold your hand on basic things like getting the labs to load. Yeah, that's awesome. You'll HAVE to learn how to use the tools.

Please don't take this class if you never ever plan on using IBM again in your life. But if you are thinking of applying to any job that uses the IBM platform, this course is money.

But yeah, the python part sucks. There's better and cheaper pretty much everywhere on the internet.

Whilst the course seems detailed enough and helps you learn step by step - not that I'm a good judge as I'm COMPLETELY new to coding - there are many things that need to be improved to make it 5 stars:

1. when using PYTHON software I discovered that should actually be using Jupyter lab and then took 3 weeks and 5 forum messages to learn that the codes weren't working because I was supposed to use JupyterLITE - this was never mentioned in the course or explained properly.

This led me to feeling very frustrated and demotivated and losing my time! it took a lot of effort to re-start and push myself nonetheless.

2. there is just ONE person in the forum replying ONCE a day and NOT on weekends - this really slows the learning down, albeit they are trying to be helpful always

3. not all codes show how the execution looks - for someone who's learning from scratch I don't know if I got it right or not, only that the code worked

4. there is a big leap from basics to complex without what seems like enough explnanation in between - using Google to research and analysing yourself can only take you so far when you're a complete beginner.

5. finally a (small) number of codes are simply wrong - this makes learning difficult once again albeit I do question the codes more and try to analyse when looking at them.

6. I don't know if I "absorbed"/understood all the relevant information sufficiently, whether I will ever need all the codes from scratch or it's merely understanding the mechanics of codes

To be honest, this Python class is NOT that beginner friendly. If I have to give a grade on this class regarding to "teaching quality", I would give a C+ or a B- at best. Here is the breakdown:

* Week 01 & Week 02- Easy to understand, the quiz also pops in the middle of video to solidyfy my understanding, which is very good. I rely 0% of outside resources on Week 01 & 02.

* Week 03 & Week 04 - Topics are getting more challenging, and the explanation quality starting to go down. But it is still "pass". Thus, I begin to rely on outside resources such as Youtube videos and Udemy Python class that I purchased (which is cheaper than Coursera) to understand Week 03 & Week 04 materials. But I can still pull this off and get through Week 03 & 04 using the videos and labs in this class.

* Week 05 - API, HTTP, URL, webscraping topics are so difficult to understand by listening to the videos in this class. I rely close to 100% on outside resources such as Youtube tutorials, Udemy class info, and another Coursera Specialization such as "Python for Everybody" in order to pass through Week 05!!!

Conclusion: Week 05 is the Week that drag down the rating score of this class A LOT!!! I feel that this class has good branding because it's from IBM, but when it comes to "TEACHING QUALITY": Udemy, Youtube tutorials, and "Python for Everybody" Specialization. This class has a lot of room for improvement....like I said: I give this class a C+ or B- at best in teaching quality.

Great course! However I feel I must give it only 3 stars because the website used for the labs is consistently not working and my course was delayed several weeks because of this. The labs are crucial to passing this course and understanding this material, so it is a significant inconvenience when the labs aren't working. Communication about the labs not working was not ideal either. I would expect that coursera or the instructor would send an email instructing students on what to do during the outage to continue but there was no communication or work around while the lab environment was down. If you decide to take the course be forewarned that this might happen to you and the course might be delayed several weeks and there will be little that you can do to get the information. The videos are not quite as helpful in this course as they were in the other courses. The real learning in my opinion happens in the lab and that isn't reliable at all. You need to watch the videos to pass the quizzes and the quizzes are extremely easy if you do this.

It's a good overview of the needed information you have to understand to use Python for Data Science, however, it definitely needs you to support the modules with information from outside of coursera, for example youtube videos, datacamp, python books, etc. Having no previous experience in programming, this course left me with more doubts than knowledge, however, after reviewing each module topics on other resources that had deeper explanations of everything and more practical exercises, I ended up with a full understanding of Python and its data application. The course content is correct but its development lacks depth and practical exercises.

On a summary, good course but don't doubt to support it with more information. I highly recommend getting the basics first before going to libraries and topics directed to data science, for the basics I used "A Smarter Way To Learn Python", great book with great examples.

As a course with "many cooks in the kitchen", there is naturally the good, the bad, and the ugly. Ultimately, this course accomplishes its goal - student learning and preparation for advancement. However, I encountered many inconsistencies, discrepancies, and apparent poor assumptions made about the learner along the way. From an educational perspective, certain approaches in the various labs could benefit from refinement after careful consideration of the most effective methods for student learning. From a product perspective, there is much to polish for this to be truly worthy of a company like IBM (ie, spelling, punctuation, and grammer). Perhaps putting this to a qualified technical writer would help to realize its full potential. In any case, I'm happy to have taken this course and would nevertheless recommend it to those who want/need to learn about Python.

I found this course to be very educational a great resource for becoming familiar or reviewing python and applying it to the Data Science framework for basic visualization. I would, however, recommend taking other basic Python courses first because the labs don't review the language well and rush through them in the videos. It can be difficult to understand the labs and really get the full benefit of the course without having some python knowledge coming in.

I would also recommend more interactive questions and coding in the labs. The labs didn't fully reinforce the lesson or apply the concepts learned in the videos. Towards the end of the course, there were no quizzes for the labs (Pandas, Numpy arrays, etc), and I found that I struggled in the final project because of the lack of application.

Take this review with a grain of salt. Let me start by saying I do not have experience programming and the course does recommend Python experience before enrolling.

I found the videos hard to follow, as there was no clear outline or natural flow to how the information was presented. The presenter spoke too fast and there was no time to digest the information before moving on to the next subject. I found myself pausing the video constantly and referring to the transcribed text below the video. That being said, the workbooks were terrific and seemed to cover almost everything in presented in the videos. For the final project, I found myself googling how to perform certain functions in Python, because I couldn't find examples to a lot of the code in the very detailed notes that I took.

Most of the lessons are clear, easy to understand and interesting. It's the positive things for me.

Now, the negative ones:

-The lessons about API (even more part 2) are difficult to understand and not seems to be a beginner level.

-Most of the exercise of the lab are the same examples that you can find in the lesson's video and it's redundant and not usefull (it should be better other examples and real exercises with real python program to write since the beginning to the end).

-You have to create some IBM clouds account. More than, you lose a lost of time because a lot of things are not clearly explain (i'm thinking to the last optional part of the exercise of week 5)

To resume, it's a good training, but you can find better (for example the ones of the university of Michigan)

It's definitely packed with a lot of information and the labs were actually really helpful to understand concepts. Would have loved to see more real-world case studies using larger data sets vs some of the watered down examples we got. The final module and assignment could definitely be improved. Although I took copious notes, I found that some commands needed were never reviewed/ reviewed well enough, and I had to do some extra research online in order to complete the assignment (Python cheat sheets etc). In the future, maybe a module in building dashboards with practice would be useful before assigning students to make their own. Or at least a study guide of concepts to review before going into the assignment (in the same vein as the real-world example point).

This course started off great. The exercises and quizzes for weeks 1 thru 4 were based on the lectures or videos and all was well. Then came week 5 and the final assignment. First the reading on How to Setup IBM Cloud Object Storage needed to be updated. I contacted an IBM Developer and they told me I needed to upgrade my account. I suggest adding the Watson Studio Setup to the Instructions page of the Final Assignment. OR include a link to the Setup Instructions instead of including a link to the Wikipedia definition of GDP. The lectures did not include the information needed to complete the final assignment. Add to the grading rubric for the last question the following for 3.5 points: URL that is incorrect and does not display the dashboard.

I took the first star away because the exercises in the labs often asked for code that wasn't covered in the course. I would stare at my screen and notes for a bit trying to figure it out and then when I revealed the answer, I saw concepts that weren't covered. There were also some discrepancies between instructions and the actual ask in a few labs, which I'm guessing result from updating content in one place and not another. I took the second star away because I've completed this course very unsure if I've really learned the basics or not. Fortunately, what I've learned is that Google is your best friend when trying to figure out how to write code for specific situations, so I will use that until I get more comfortable with Python.

The learning curve required here is quite steep, I am glad I had paused this course to complete the Python for Everyone Specialisation, offered here in Coursera, before endeavouring to complete this one.

The familiarity with coding gain there, made me understand better and take advantage of this course, instead of the feeling of loss I had at the beginning, particularly with the level required to complete some of the hands-on labs.

I recommend you get acquaintance with Python at a basic level, to get more out of this course.

I have provided feedback to Coursera regarding this area of improvement, either to include the PY4E course here or to at least tell people about that option.

Hoping the best for your learning experience.