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

Pretty solid foundations and labs. Would have given 5 stars but the course lacks coherence regarding the videos and different services(cloud.ibm, skills network labs) that are made available to students.

If one is already acquainted with the subject matter it is not difficult to navigate around these wrinkles. New students might find getting lost on silly steps of what the video shows and what the website actually shows adds unnecessary frustration.

this course offers a good python scaffolding for working with data, but it suffers from a lack of proofreading such that the narration doesn't always match the text onscreen (or the reality of what's happening). also, i would love to see line breaks in the code that appears in quizzes, as well as more informative function and variable names. as a consolation prize, there was a section of video that completely redefined the word "square" for me.

O curso apresenta uma proposta interessante, no entanto, a ausência de legendas em português nos vídeos dificulta o entendimento. Além disso, alguns textos, como os presentes nas avaliações, possuem erros de tradução, o que também prejudica a compreensão das perguntas e respostas. Por fim, embora o curso seja anunciado como focado em inteligência artificial e desenvolvimento, percebi que o conteúdo é mais voltado para ciências de dados.

The course did not provide what I expected from a Python course for Data Science. The parts of Pandas and Numpy which are very important in Data Science should be more focused with better explanation and exercises! Moreover, there are so many quizzes that are duplicated with the ones already shown in the video lectures, so as some practical assignments. Anyway, it delivered some valuable information that could be helpful for learners.

Although I learned a lot because I am new to Python, the learning approach used in this IBM course is quite poor. The videos are rather boring, the labs are often packed with too much content, some of the lab content is out of place, and the modules end up feeling rushed. In my opinion, this course needs a massive overhaul to make it more interesting, more user friendly, and to iron out some of the frequent errors and typos.

This course was very difficult. I did not understand what I was learning most of the time as it lacked the practical applications to data analytics like the other modules. I felt the formulas of python were just presented, and I couldn't pause the screen to maybe copy the formulas on screen as it just went black. I will have to supplement this with outside resources to ensure that I fully understand the basics of Python.

I think the information provided is good, but the level is not for someone with no prior experience in Python. A lot of the content felt way too much to handle. I'll have to fill the gaps with other sources and complement it with a lot more practice in order to feel confident with this technology. It is a great overview of Python for working with Data and if this was the intended goal of the course, it is excellent.

While I learned a lot in this course, it could have been a more enjoyable process. The videos and labs basically had the same content. However, the labs are a better learning experience (can run code and see results) but come later, making them less valuable and boring. Please consider having the lab content be more unique, as it was in the previous course. Also, please update the materials as Watson Studio changes.

The videos and labs were helpful and the course does help one learn some Python fundamentals. However, I believe that the labs and quizzes could have been improved. More exercises within the lab environment would be beneficial to promote learning. The weak point of the course is that the tutorials for using IBM Cloud were weak. More information and walkthroughs (including videos) should have been provided.