Plenty of essential stuff has been skipped. There was no consistancy and unfortunately it didn't go gradually. I wish the authors assumed that this course has been developed for beginners i.e. people who probably see all of this stuff for the very first time.

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.

Pretty disorganized. It did not seem like staff were experts. Lots of mistakes in the code.

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.

Python for beginners... it teaches nothing about data or AI or development

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

Labs don't work

Very very basic

I recently joined the "Python for Data Science, AI & Development" course as part of the "IBM Data Science Professional Certificate" specialization on Coursera, and I am thoroughly impressed with the quality and depth of the content. This course is a fantastic introduction to Python, especially tailored for those interested in data science and AI development. The instructors did an excellent job of breaking down complex concepts into easy-to-understand segments, making it accessible even for beginners. The hands-on labs and assignments were particularly beneficial, allowing me to apply what I learned in real-world scenarios. The Jupyter Notebooks provided a practical environment for coding, which helped reinforce the learning experience. I appreciated the comprehensive coverage of Python libraries such as Pandas, Numpy, and Matplotlib. These tools are essential for data manipulation and visualization, and the course provided clear, practical examples of how to use them effectively. The integration of AI topics added a unique dimension, highlighting Python's versatility in this cutting-edge field. Overall, the "Python for Data Science, AI & Development" course is well-structured, engaging, and highly informative. It has equipped me with valuable skills that I can apply in my current projects and has significantly boosted my confidence in using Python for Data Science and AI Development. I highly recommend this course to anyone looking to enhance their programming skills and dive into the world of data science and AI.

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.