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Thank you for giving the opportunity to learn Python.

As for my review of this course, I suggest proofreading the labs before publishing them because they have many spelling errors. Since one of the recommended qualities of a Data Scientist is to be detailed oriented, it would be better for all the English and non-English speaking students to have instruction without errors. This will set a good role model for them to be more aware of their work.

Additionally, it would help students who have no prior knowledge of Python to be given some context before starting the labs. There are some labs that expect more than what is explained in the videos.

In regards to creating an object in Watson Studio, I highly recommend including Alex Aklson's video in the curriculum. Screenshots that are provided for the labs are helpful, however, the video is more comprehensive, and the step-by-step process eliminates confusion. Please devote more time to the subject of Numpy as it seems to be a vast subject and needs more instruction and examples.

Overall, this was an informative course that had an enormous amount of material to cover. Thank you once again and continue teaching thousands of students like me around the world.

I thoroughly enjoyed this course. It skims the surface of basic Python you need for Data Analysis, which is exactly what I was looking for. You get a general understanding of basic Python elements, syntax, useful libraries and some examples of really simple data analysis.

The main disadvantage of this course is a couple of exercises at the end of hands-on labs that do not correspond to the course material by their level of difficulty. To me, as a person with zero programming background, it felt like I've just been explained addition on examples like 2+3 and then asked to add something like exponential numbers and square roots. Judging by the discussion forums, I am not the only one who felt this way, which was the only thing to keep me from thinking that I am too dumb for this and giving up. I believe those tasks are great as extra challenges but must be marked accordingly.

The other odd thing is that really useful info specifically for Data Analysis process is contained in optional videos and labs, so I advise future learners to draw attention to them despite their being non-compulsory to finish this course.

Thanks to all the course authors and moderators.

The course itself was fine, and the project was helpful. I’m thankful to IBM to come out with this course. But the Watson Studio part could be very frustrating. It is not really relevant to Python study, but you will have to use the Studio for your final project. I found he Studio to have very complex layouts, very hard to nevigate, a lack of guidance on the studio itself. I was to give 3 stars as my final project experience was so ruined by the Watson Studio, I definitely spent way more hours on figuring out Watson Studio than the Python part of the studio, and not feel it’s helpful to know Watson Studio as I probably will not use it in the future. But my reasonableness and fairness side told me, the very end of an experience in general puts more weight on one’s overall experience on something, so a bad ending of it could potentially make me to give a biased opinion, towards the down side, to the experience. So trying to overcome that bias, and being appreciate for IBM to put this course together and Coursera to offer it, I gave 4 stars.

The Coursera course on Python for Data Science, AI & Development is an exceptional resource for anyone who wants to learn Python programming and its applications in data science and artificial intelligence. The course starts with the basics of Python programming and gradually progresses to advanced topics such as data manipulation, visualization, and machine learning. One of the key strengths of the course is its practical approach, with numerous hands-on exercises and projects that allow learners to apply what they have learned in real-world scenarios. The instructors are knowledgeable and engaging, and the course is well-structured and easy to follow, with clear explanations and examples. Additionally, the course provides a supportive learning environment, with a dedicated discussion forum where learners can ask questions and get help from instructors and other learners. Overall, I would highly recommend this course to anyone who wants to improve their Python programming skills and learn its applications in data science and AI.

The Python for Data Science, AI & Development course by IBM is an excellent starting point for anyone looking to build a strong foundation in Python for data-related fields. It covers key programming concepts, including variables, loops, functions, and object-oriented programming, making it suitable for beginners. One of the best aspects of this course is its hands-on approach, where learners apply Python to data manipulation, web scraping, APIs, and working with libraries like Pandas, NumPy, and Matplotlib. The course also provides an introduction to machine learning and AI concepts, making it valuable for those interested in data science. The structured learning path, interactive Jupyter notebooks, and industry-focused projects make the learning process engaging. Additionally, earning an IBM certification enhances credibility for job opportunities. Overall, this course is a great choice for beginners and aspiring data professionals who want to develop Python skills for data science and AI applications.

Python for Data Science, AI & Development" provides a comprehensive overview of Python programming tailored for data science, artificial intelligence (AI), and software development. The course covers essential Python concepts such as data types, control structures, functions, and object-oriented programming. It delves into libraries and frameworks commonly used in data science and AI, including NumPy, Pandas, Matplotlib, and scikit-learn. The course also explores advanced topics such as machine learning algorithms, natural language processing (NLP), and deep learning with TensorFlow and Keras. With hands-on exercises and practical examples, it equips learners with the skills needed to analyze data, build AI models, and develop Python-based applications effectively. Overall, it's a valuable resource for anyone looking to embark on a journey into the exciting fields of data science and AI using Python.

This course was amazing for me. It was my first experience on Coursera and it was fabulous. I would like to thank IBM and Coursera for providing me with such an opportunity to gain some extra skills along with my studies.

The course content was good, but I would like to share some thoughts on the content.

1) There were few programs in the labs for self-practice.

2) Questions in the practice quizzes should be increased and should cover the whole topic.

3) Additional links for self-practice should be provided for good practice and knowledge.

4) Functions should be taught in detail.

5) The videos on the libraries should be more.

6) Libraries should be taught more than just the introductory level.

7) Libraries should be taught thoroughly.

I hope that these suggestions should be taken into account.

In the end, I would like to thank IBM and Coursera for giving me a chance to learn and build some skills.

This course was fantastic up until the final project. I could not have finished it without the help of the folks in the discussion forums. The project was challenging, but then getting it into the right system so that it could be graded by my peers was a real headache. My suggestion is to provide more examples of how to access a website and use tuples with variables and numbers. The lesson only teaches us to use tuples with numbers.

The explanation said that the project should only take 1 hour. It took me nearly 3 hours, and most of the time was spent googling the terminology and other people's code so I would know how to start. It took me about an hour to figure out how to load the Album Cover project. I think having all of the labs and projects in the left panel was confusing. We are only used to seeing an intro, the project, and the peer review section.