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.

This course offers a broad, practitioner-oriented introduction to Python, covering less common topics like web scraping. Despite the promising content, it feels as though it was developed in a rush and thrown over the fence, lacking thoughtful consideration for the learner experience. In contrast, I found the "Get started with Python" module from the Google Advanced Data Analytics specialization more professional and enjoyable (5 stars). While informative, this course needs an overhaul to improve its delivery and structure, especially if it's aimed at beginners.

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

There were non-credit parts of this course (specifically on API, and the process to create credentials in IBM Cloud for the final assignment) I was unable to complete because the instructions were erroneous or incomplete. When posting questions regarding these sections in the forums, I received 0 responses. Thus I feel like there are large gaps for me in the course material. I only hope that as I am doing the Data Science certificate program, there will be enough repetition that I won't be completely confused and behind.

Overall, it is a good course to discover the data science field since it introduces the main libraries used for Data Science (Pandas and Numpy). However, this course misses a seperate section of the Matplotlib library used for data visualization.

In addition, the sub-section "Working with Files" should not be included with Webscraping and API section and should constitute a seperate section. Consequently, the sub-section "WorkingWithDifferentFileTypes" should be included in the "Working with Files" section.

Thank you

A decent quick overview of the basics, and I found it worth my time, but only just barely: the videos are narrated by a computer voice (they couldn't even find a human to teach it?), some outdated Python 2 techniques are still included, and the whole thing is a bit of a plug for IBM's online services, including Watson, which you have to sign up for to complete assignments. But yeah, it takes you through the motions and teaches you enough Python to drive out of the parking lot, so to speak, so overall it's fine.

Having already learned all of this stuff before, this course was a little painful to work through. The content is there, though I'm not sure if a "new to Python" learner would be able to grasp everything in the course. As someone who has used Python for a few years now, I feel like this was an okay review of important topics, but it wasn't presented in a super interesting way. And the quizzes never displayed correctly, either on the app or online, so I kept having to retake them because of formatting issues!

The course is great for people who have previous experience with python but not so much otherwise. The pacing of the course starts off strong in the first week or two, but quickly speeds up. This would not be an issue if adaquet explanation and practice were given before moving on. Oftentimes terms were used in modules that were not explicitly defined until multiple modules later which is disorienting when you are trying to learn a programming language. Great refresher course, but not fully for the beginner.

Even though I have got through this module I still don't feel I have learnt much on the basics/workings of Python.

I think the Python section could be structured better, maybe even including an intro to Python module. Students who have knowledge of the language could pass the module by way of assessment at the start of the module and get credit for it rather than going through it all.

I think this would help beginers get used to the basics of the language which seems very different to Java & Javascript.

I enjoyed this course. However, I felt that there was a huge jump in the complexity of the subject matter between Weeks 3 and 4. I'm a newbee to Python, and Week 4 absolutely did not feel like a beginner's course, whereas the first three weeks were easy to digest. The videos were mostly good and informative, although I found that they rush through the material very quickly. I had to rewatch some of the videos multiple times to understand the concepts. The final project was challenging but enjoyable.

Some of the weekly unit courses were really good. They were straightforward, well-articulated, and the lab work was understandable, useable, and gave me opportunities to practice writing on my own and learn why/how things worked and why/how they didn't. Some of the videos and labs seemed to skip important information and steps, with typos everywhere, and the labs for these particular lessons felt like hitting shift+enter to run the code without much or any opportunity to write the code yourself.

Easy to follow, but I find it not satisfactory for either novice user or advanced user.

For advanced user, it is hard to go through learning what a loop is.

At the same time, it is surprising how some strange unexplained pills appear in the code you are learning, as a sign that the course wants to tackle too much without getting deep into the roots of the language.

It didn't appeal to me, as an advanced programmer in other languages, I expected more and clearer answers to the root of the language.

Good for learning basic Python concepts and how to use them in theory. However, do keep in mind that it doesn't teach much about how to use a text editor or any type of software to code and run actual programs on your system. The course uses the IBM Watson notebook, which is implemented in a way with very little practical value. I feel like I came out of this course knowing a lot about Python but to gain practical knowledge and be able to make my own programs I had to research outside of it.

Im not a person who reviews things but I felt this may be helpful to some. As someone with no knowledge in programming whatsoever I found this difficult. Clearly, this is to be expected when diving into something new and my review is based off how i feel after completing everything. But I found this to be very challenging. Practice makes perfect, I didn't feel like this was beginner education. Hopefully over time ill understand better. Good luck to you all!

This course is good for python beginners and to also receive a hands-on training for the same.However when it comes to Data science,this particular course does not offer or rather meet its expectations.

The final assignment requires an account creation on IBM's Watson Studio,not following which you wont be graded.So that was like a turn-off for me because assessment mostly relies on successful creation of a Watson Studio account rather than the core subject