Very good course

awesome course

Awesome lesson

very good.

Very good.

all awsm

Great

The structure is excellent, with videos, readings, and quizzes. The labs are a great way to practice the material learned in the videos and readings. Additionally, the breadth of content is great. I feel like it covers a great amount of Python to get started on your first projects. That said, there are a lot of errors in the material and the labs. I reported most as I came across them, but it can be frustrating when something is explained incorrectly or you are given contradictory information. There are many grammatical mistakes in the lab explanations, as well. The mistakes do not ruin the course, as most of the information is phenomenal, and a lot of detail is packed in. However, it significantly hurts the experience and makes the process frustrating. This is on top of the frustration of labs including code that is not explained. There are some functions used in the sample code, and keywords used, that are not explained in comments or the lab preamble. Maybe the idea was not to overexplain, but I found this is very frustrating when it happened. All that said, this course is still worth 4 stars. It's a lot of information with a lot of resources given. I'm glad I took the course (although with the caveat that it was free since I completed it during the 7-day free Coursera Plus trial).

It is very fundamental and easy and simple at the beginning. The lecture and lab are not easy to understand for beginners regarding pandas/numpy, APIs, and web scraping. The lab and exams have some errors. When learners post on the forum, the staff who answered questions keep asking for screenshots or more details or share with GitHub. Incredibly, the staff who are responsible for answering the questions are not familiar with the course content. It is easy to locate the exam questions. Also, it is easy to figure out that the lab has errors because BeautifulSoup needs to parse data from HTML and when you install and import html5lib, you should use it as BeautifulSoup(s, "html.parser") instead of soup = BeautifulSoup(s, "html5lib"). I don't understand why someone responsible for answering the questions is not even familiar with the course content.

This course was an uphill climb. There were many concepts and programming pieces that I did not know. I was able to follow the programming pieces and understand how to "read" the script. Staying on track with the theory allowed me to gain a great basic foundation into Python. Since I am not a data scientist or engineer, I don't think I need to know Python deeply. I do look forward to undersanding what level of Python will be useful as a data analyst.

I did feel that some of the answers may have been incorrect on the quizzes. I saw some information on that in the Q&A of other participants. It would have also been nicer to have the Python level objective stated more clearly that a participant will see a much deeper level than their beginning knowldge and to digest the understanding over the techinical in some of the learning.

Great for foundational learning. Modules 1-4 give you a great understanding of how data flows in Python, while module 5 is kind of a mess. Importing and using random things to complete a "learning" task without explaining why we're using those left a hole in the knowledge path where the foundation of the module felt shaky throughout. I'll make it up on YouTube, but I shouldn't have to. I will say again, that the first 4 modules of this course are on point and are well worth it but you won't walk away from this course as a programmer. I'm walking away understanding how to read the data flow in the program with confidence and knowing exactly what I need to study next to maximize my analysis.

The "Python for Data Science, AI & Development" course comprehensively covers essential topics for aspiring data scientists and AI developers. Its well-researched and informative material is particularly valuable for those with some programming experience. However, the mixed format of videos, written notes, and labs can make the content feel disjointed and scattered, highlighting the need for better structure and more hands-on exercises. Complete beginners may need additional external resources and practice apps to fully understand the concepts. Overall, the course provides a solid foundation for further exploration in data science and AI.

Learned a lot of things in the field of working with data and files. Requires a previous Python experience (even low-level). This course is especially useful to deepen your knowledge about libraries in Python if you want to pursue a career in Data (Science/engineering). Some things are not completely explained, but it doesn't affect your global experience. Just take notes while pursuing this course and don't hesitate to ask AI (Coursera Coach or Chat GPT) to clarify vague understandings. Globally a very good course for those who want to work in the data field.

The course was overall a very good introduction to Python with content that was easy enough for beginners to learn, but also contained challenging content for those who have experience in other languages. Overall the content also reviewed the primary libraries that are frequently used in Data Science. However, there were many issues with the IBM Cloud labs which was frustrating and disappointing. Overall I was still able to attain my primary goal for the course, which was to attaiin a working understanding of Python in Data Science applications.

As a newbie in python programming, the course was really informative and very practical. I did understand most especially the challenges that I was getting in the exercises on every notebook.

The only thing I did to gain more understanding of the language was to supplement my knowledge with other youtube videos so that I can fully understand Functions in python, ojects and classes and REST APIs.

All in all it was a wonderful lesson.

In general the modules are great with nice exercises and examples in the labs. However, the week 5 (APIs) could be better explained. In general the videos are too short and go straight to some complex codes that the students are not used yet. I confess I will find another course for APIs and webscraping, once I was not able to fully understand how to put in on practice.

The course is very informative. The Quizes are very easy, which have both Positive and Negative side. It would be better if the questions are different in Practise Exam and Weekly Exams. Also the quizes sometimes refere to the examples mentioned in the slides, which can be ignored by providing full information in exam rather refering.

There should be a summary section at the end of all week courses where all the learning items are summarized. A downloadable pdf copy would be more helpful.

Also, few questions of assignments were confusing, where expected end result format was not clear. It would be better if provided a sample output what is expected.

Great course! Would give 5 stars if the final assignment was better aligned with learning material, it really threw me off and was very hard to decipher in both logistics and technical questions. The last assignment really needs to be improved. Otherwise, great intro course.

Excellent course presented well and labs were good hands-on way to learn... Could have done without the Numpy sections tho... Once it started talking about adding/calculating vectors without explaining what they were, i had to skip over it... But rest of class was great

The quizzes could have been a little tough. Also, the course could have contained writing few programs, and also output like questions should have contained some complex and bigger codes..Quizzes were very very basic. Otherwise, course was fantastic.. Thank you so much

The Reason im giving it 4 stars is that they tried to teach basic python in very short period of time and with less explanation.

Im suggesting everyone should take "Python for everybody" OR "Python 3 programming" by University Of Michigan as prerequisite.

I like the project, practical orientation of the course compared to more academic approaches. The slides on

usage of the Watson Studio features and sharing could be improved. Spent more time messing with that than the programming. Seemed excessive

The cloud storage question on the final is just a ploy to get us to use IBM products and shouldn't be part of the grade. The course was a good pace and nice, slow introduction for new Python users.

Everything for this was great, up to the last part of the project. The instructions to load to the IBM cloud were some of the most confusing and overtly difficult exercises.