Lot's of good content. But the labs are very superficial and they just repeat what shown in the videos. The quizzes are too easy and they do not push you to actually apply what you learned. The final assignment was ridiculous. Just using Pandas to import some data, nothing more, no loops, no if statements, no analysis of the data. Continuing being quite dissatisfied with this certificate.

This course was not well designed. It needs intensive editing and rewriting.

The author emphasize the use of IBM cloud products, but the course needs to elaborate in this area if this is the desired target.

Not enough explanations were dedicated to the Python language itself. To succeed you must know some Python before you learn the course, or learn during the course from other sources.

Bad videos.. Many methods and functions in the lab are not explained in the videos

To complete this course I spent most of my time reading documentations and searching to understand what is said as there is no enough explanation or even resources to read from.

(Maybe this method suits you but I didn't like it)

Classes in IBM Data Analyst track seem to be for marketing purposes. They force you to sign up for IBM products, provide your personal information and credit card number, and then it doesn't even work. You really don't learn much in the actual classes, they just seem to want to advertise IBM products.

Absolutely worthless course. Waste of time. You will only get begginner knowledge after listenin to videos with tedius useless information. I knew python before and i watched in horror and disgust how horrible topics were covered and how unnecesarily complicated they were forced to become.

Examples in the videos was not good, at least giving some examples for necessary functions in videos would be good, If you don't want to put new videos than at least give students the documentation link to guide them so they can learn themselves with parallel to labs.

Signed up for Applied AI and suddenly IBM and Coursera decided to update the program to AI Developer program. Thumbs down, I only committed my time for 7 courses, but now you added 3 more courses and I can't continue anymore. Wasted my time big time!

It all makes sense till about half way into the course then it turns into some kind of gibberish and you get completely lost. The videos are just text to speech and the labs are very bland and not interactive at all. Waste of time and money.

The entire Data Science Certification course is misdesigned. It is not at all beginner-friendly. It seems like the entire thing is rushing down on covering the concepts within 5-10 minutes of videos with insufficient demonstration.

You won't walk away with anything more than a superficial understanding of Python. The introduction is cursory, the course breezes very specialized python code for data science and there is very little hands-on practice.

One of the worst courses/attempts at teaching something I've ever seen. Datacamp Python is a million times better and this random book I bought was way better.

Labs are garbage (only half of them load, most of the info in the lab is not covered by the video, we don't use an interpreter), videos are too fast

Weeks 1-3 were really good, But the lessons in weeks 4-5 were really were incomprehensible and it was really hard to understand things

Trash. Have someone who knows how to put together a course rewrite this. The ordering is wrong and it skips steps. Very bad.

It should be much more better explained for a true beginners. Also, there are many corrections needed.

A lot of not-working codes and tasks unable to solve based on the lessons taught before

Lab is not working. I am not able to access any of the hands-on lab exercises.

its very bad, not clear and the videos doesnt explaine any thing.

Lots of ambiguous statements, and sometimes inconsistent content

very poor course fuck off coursera

worst python course ever

assignment problems

IBM's Python for Data Science, AI & Development course on Coursera is an exceptional learning experience. This comprehensive course equips students with the knowledge and skills required to excel in the rapidly evolving fields of data science, artificial intelligence, and development using Python. The course curriculum is thoughtfully structured, beginning with the fundamentals of Python programming and gradually delving into more advanced topics like data analysis, machine learning, and AI. Each module is well-organized, with clear explanations and hands-on assignments that reinforce learning. What sets this course apart is its practicality. Real-world examples and industry-relevant projects allow you to apply what you've learned in a meaningful way. The instructors are knowledgeable, and the peer-graded assessments encourage collaboration and deeper understanding. Furthermore, IBM's reputation in the tech industry adds credibility to the course, making it a valuable addition to your resume. Whether you're a beginner or have some Python experience, this course provides a solid foundation and the confidence to tackle complex data-driven projects. In conclusion, IBM's Python for Data Science, AI & Development course on Coursera is a game-changer for those looking to thrive in the world of data and AI. It's an investment in your future that offers both knowledge and practical skills to advance your career.

This course was a perfect introduction to python for data science. I already have a B.S. in political science which required a few semesters of statistics. We mainly used Excel and SPSS. I wish I had taken a course like this because I’ll say that I much prefer Python to SPSS and Excel. I find Python more functional but far less user friendly. What helped a lot here was that I have a background in windows and pc hardware. I also have a little experience with Linux and .bash scripting. I’ll admit, this course would have been much more difficult without the computer knowledge I already had.

I’m currently working full-time trouble shooting large 3D printers 40 hours a week. I’ve been pondering what to go to graduate school for. This course has helped with that decision. I’m leaning toward a masters in the applied data science.

I plan on taking the other data science and applied data science courses on Coursera as well. Any and all continued learning I can get will be valuable.

What was most challenging? Learning the syntax and structure of the python language. I’m still learning it and it’s going to take quite a lot of effort to master it. Attention to detail is an absolute must in programming or coding—albeit a short script or manipulating a data set.

Also, I found that the Anaconda suite was the best choice to complete the course. It was a little more user friendly than the bare-bones IDLE/Python combination.

Content Quality: The course should cover a broad range of topics from basic to advanced Python concepts such as data types, data structures, control flow, functions, classes, modules, and popular libraries/frameworks (e.g., NumPy, Pandas, Flask, Django). Clarity and Explanation: The instructor should be able to explain concepts clearly and effectively, catering to learners of different levels. Complex topics should be broken down into understandable chunks with practical examples. Engagement: The course should keep learners engaged through interactive elements like quizzes, coding exercises, and projects. Projects and Exercises: Hands-on projects and exercises are crucial for reinforcing learning. They should be diverse, challenging, and relevant to real-world applications. Community and Support: A supportive community or forum where learners can ask questions, share insights, and collaborate can enhance the learning experience significantly. Updates and Relevance: Python is an evolving language, so the course content should be regularly updated to keep up with the latest language features, best practices, and trends in the Python ecosystem. Reviews and Feedback: Checking reviews and feedback from previous learners can provide insights into the course's strengths and weaknesses.