I loved the course, although I do believe that the best parts of it come through in the 5th module in due course to the lab sessions and discussions. And that I would like to see more of it because it allows the individual to practice everything it is that he's learnt.

Good start course for Data Analysis and Visualization with Python. Provide a good start point and quick way to learn this domain. Also demonstrate how simple machine learning applications can be implemented by popular Python packages.

Un muy buen curso introductorio que te da muy buenas bases del análisis de datos y una buena introducción al aprendizaje automático

Excelente course.

BUENO

1

El contenido del curso es de alta calidad: las explicaciones, los vídeos, los ejercicios. En algunas ocasiones me encuentro que la traducción al español no está bien hecha en los tests (especialmente con las palabras técnicas), a la vez que hay ciertos test que preguntan por contenido que aún no se ha dado en el curso pero que sí aparece más tarde.

This is a great course for anyone who is already familiar with the basic concepts and has some hands-on experience in the field. If you don't know what numpy and pandas are, I recommend you to take the first course of this specialization.

ML programming assignment didn't have enough introduction in the course. All videos could have also a text presentation (not just transcript, since the code is not there) because some of us prefer text-based learning - it's faster.

overall very good, but I missed more practical content and a bit more sophisticated and diverse. Most of the examples are the same over and over. Thanks

Es wäre besser, wenn mehr geführte Übungen gemacht werden und nicht nur wenig sagende Videos.

Great!

This course provided a solid foundation in Python-based data analysis and visualization. I appreciated how it introduced essential libraries like pandas, matplotlib, and seaborn in a clear and beginner-friendly way. The hands-on exercises helped reinforce the concepts, and real-world datasets made it engaging. One area for improvement would be a deeper focus on real-time data handling or integration with tools like Jupyter Notebooks. However, overall, it’s a great course for anyone starting out in data analysis or looking to strengthen their Python skills.

It's a good course, but some parts were largely repetitive. I wish there was more interactivity.

Too many repetitive sections (definitions, overviews, same practical examples, numerous identical ethical concern topics) and not enough actual coding. Covering topics once is enough. Twice is okay for reinforcement. Anything more is a waste and the time should be used on actual coding lessons, assignments and reviews.

This Python data analysis course is disappointingly boring and lacks engagement. While it promises to teach practical skills, it falls short by offering very few programming assignments, which are essential for truly understanding and applying the material. Instead of interactive coding tasks, the course relies heavily on short video lectures that are too superficial and fail to explain concepts in depth. The lack of hands-on exercises makes it difficult to stay motivated, and the overall format feels more like a passive viewing experience than an actual learning journey. For a technical subject like Python data analysis, the course should provide far more coding practice and detailed explanations to be effective.

IN THE LAST : Programming Assignment: Activity: Your first machine learning model implementation it didn't tell me which part of my code went wrong and the expectation for each cell I have no idea where to troubleshoot to give the code it wanted I want to reset everything back to original also don't know where to reset the assignment it must at least tell me which code is wrong and which code has met the requirement I have no idea where to trouble shoot when it just say not all cell have met require results