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.

The final assignment was not put up in the week 5 bucket. Instead we had totally different 'housing' related notebook with instructions for using seaborn which we never covered in the course. The final assignment was later sent in as a response to a question in the discussion forums.

The directions in the python notebook was confusing.

The support staff was being helpful but with so many issues, the frustration is all around in the group of people who took the course.

I spent two days getting answers [which I never got] to my questions. I found out the answers by my own effort . In the process, I believe, I have a better understanding of IBM Watson Studio, IBM Cloud, Storage, Services and resources. I am happy for that.

The first few weeks are helpful if you are a complete beginner with Python, but after that it felt like if I wanted to learn any of the presented material I had to mess around on my own and effectively make my own labs. In these later sections, the labs would often use modules or specialized functions without introducing or explaining them at all, in sharp contrast to how much they hold your hand explaining the very basics in the first few weeks. It was jarring and I found them difficult to engnage with.

The latter videos are also really fast (don't leave the code written on the screen long enough to read it) and the hands-on labs are not very interactive. Not terribly impressed.

The best part of this course is that they provide learners with an easy to use cloud Python environment ( https://labs.cognitiveclass.ai/tools/jupyterlab/lab/tree/labs/ Jupiter notebook). The exercises in that environment are good. One can almost skip the lectures and only go through the exercises, there will be little if any loss.

This course forces the learners to sign up and use the IBM cloud environment, and I don't like it. It's completely unnecessary.

The sequencing of material beyond the basics is somewhat random. Anyway, the course covers a little bit of many Python essentials. There may be better Python courses, I just don't know them.

The course is fine, but i feel that in the last two weeks they trow at you a lot of information without much context, so is really difficult to understand the topics covered in those weeks. There are a lot of tools and libraries that came out of nowhere in the labs without any explanation, i had to use google a lot in order to understand why they were usinig a especific tool or library. additionally It would be nice to have a way to practice more.

It is also disencouraging that the certificate says that is a non-creidt course, sound like it doesnt worth anything.

Finally the forums didnt help much when you have a question.

Labs were over all okay, but several had typos and questions about things not covered in the course. often labs would use code in examples that had not been covered, and I spent a significant amount of time researching and trying to understand what was being done so that I was aware. This is definitely an okay course, however I will be taking at least one other Python course before moving on to my project course, because this one did not make me confident in my abilities to move forward. Though I do feel confident in my ability to read and understand python. I am not comfortable with my ability to write it.

3 stars for content and presentaion. I like the format (videos, text, labs, and pop up quizzes).

No 4 stars though. For a representative of "Higher Learning" this course has a herendous amount of mispelling and some grammar fails. Maybe it's just my OCD coming out but I would expect better from my students if the situation were switched. When the syllabus was handed out for this course apparently SpellCheck101 was not on it. I considered taking the time to report all the instances as I went along but I'm not being paid to proof read.

Other than that its a worthwhile course.

DAS

Die Kursinhalte sind zwar alle wichtig und "gut" verständlich, aber es sind mir persönlich zu viele Fehler darin enthalten. Z.B. wurden in manchen Notebooks Änderungen vorgenommen, welche die Übungen schwerer verständlich machen. Auch manche Fragen im Test sind nicht richtig. Was aber das schlimmste ist, ist die mangelhafte Visualisierung im "Fragenkatalog" oder "Examen". Wie soll ein Anfänger die Codezeilen richtig lesen können, wenn er keine "Einrückung" sehen kann oder die "Leerzeichen" so ungünstig sitzen? Wenn das korrigiert würde, wären die Übungen vile besser.

Decent introduction to Python and some of its basic coding principles. First 4 weeks are put well together, provide sufficient step-by-step explanations and insightful, helpful labs. The final week seem less organized, video lectures are not as thorough and explanatory as in the first weeks and felt rushed. The topics covered in the final classes (i.e., APIs, REST APIs, HTTP requests, Webscraping etc) seem more elaborate, with no proper background information that would allow a beginner to keep up with the more complex coding employed in the respective labs.

Die Fragen der Quiz waren manchmal merkwürdig. Manchmal war die Formulierung der Antworten so komisch, dass man einfach raten musste, was konnte der Autor als eine richtige Antwort meinen (denn genau genommen gab es Keine richtige Antwort zwischen den Varianten). Manchmal war die Auswahl der Varianten so ausgewählt, dass es nicht möglich war, falsch zu beantworten(egal ob man Kenntnisse erworben hat oder nicht), weil die falsche Antworten VIEL ZU offensichtlich falsch waren.

Ich hatte den Eindruck, der Autor hat sich bei den Quizen nicht viel Mühe gemacht.

The course was informative at the beginning. However, the quizzes in the middle of the videos are too easy and oddly placed. The final quizzes are pretty straight forward too. There's no practice involved as such. The final Dashboard project wastes a lot of time trying to focus on IBM buckets, credentials and setup and whatnot instead of actually teaching how to use python for data visualization and AI. I really believe the course could've been more in sync with the course title. Many of the topics were left off in the middle and were not taught in-depth.

For those who are looking for AI development specifically, this is not the right course for you. It focuses on basics for the first 4 weeks and APIs in the last week. It never really talks about AI packages or development methods, as I thought it would.

If you are a total beginner in Python, this course along with some more rigorous practice on your own, could suffice. But if you have done a few real projects this will be too basic for you.

The lab modules DID NOT work for me from week 3 onwards! Come on IBM, please do something about the cloud system!

Like many others have said, the interface on IBM cloud is not the same as described in instructions. I ended up spending more time in setting up (or just getting to the place I'm supposed to go) than doing the work. I know I can ask for help in the forum, but I may or may not get a response and if I do get a response, it may be days later. On top of that, the response may or not help in resolving my issue.

The material is quite easy. I'm completely new to Python, but still I feel the exercises are too easy.

The videos are clear and concise, however.

The course is well done, the Jupiter notebooks are clearly explained, etc.

However, despite the rather ambitious title, the course is extremely basic. I think it took me 2-3h to go through the entire course. Based on the title, I was expecting the course to cover also advanced topics such as ways to treat large datasets, etc. It is basically just a very basic introduction.

Also, some assignment require signing up for IBM services (for free), which is a bit of a hassle. It would be easier to just use notebooks and automatic grading of assignments.

Focused too much on demonstrating IBM technologies. A couple labs were great, particularly the Strings and Classes ones, where you were demonstrated concepts and then challenged with an open ended question, having to explore the libraries or use resources to write the script and learn through practice. Otherwise, multiple sections on how to navigate IBM Cloud and create projects is not particularly applicable knowledge. I appreciate the work put in to the course structure, but it felt like I had paid for an advertisement at times.

A decent introduction to the basics of programming in Python, I found the Jupyter notebooks for the labs to be pretty useful. However, the final peer-graded project is problematic in that it outright states that sharing your Jupyter notebook is OPTIONAL, but upon submitting and looking at the rubric for peer grading, I found out that it was one out of five required points for a 100% score. Not a big deal, but I can be a bit of a perfectionist when it comes to assignments, especially finals, so that was irritating to say the least.

The final assignment at the end of week 5 has little to do with what's taught in weeks 1 through 4. This caused a lot of confusion and hence made it very difficult for a lot of the students (as seen on the forum section) to complete the assignment. For help on the final assignment, here's a link to a helpful post by Aronis Mariano, without whose help I would probably not have been able to finish the assignment.

https://www.coursera.org/learn/python-for-applied-data-science/discussions/weeks/5/threads/TkYpqAysEemS0Q4U5mc4kg