I could have used a little more explanation when it came to Pipelines and Polynomials, but I figure there will likely be more of that to come in the later courses.

The course is a good start for beginners. The course contained everything useful form churning of data to regression. Pretty decent explanation with practice labs.

Good For Begineer approach to Data Analysis with Python .. Data Visualization with python is more understanding about visualization experience after this course .

Great coverage of fundamentals. Seems to be missing some content around diagnostic metrics for classification problems (e.g. binary class) using AUC/ROC curves.

The course is well organized and the content is presented is an accessible manner. Exercises could be more challenging and cumulative to help increase retention.

Most of the course is very easy to understand, although the exercises in the notebook can become complex, the exercises do not always run and must be done again.

Fun course! Lots of interesting content. It could've been more interesting and challenging with addition of a couple of marked assignments or a capstone project!

The course material is great enough for a beginner. However, some of the presentation method is confusing. The narrator also seems like a laymen for the course.

Great content, but awkward, untimely popping of questions during video lectures, very annoying. Labs are very useful and productive, but videos are too short.

This Course was very informative and beneficial and conceptual too, being newbie i personally feel that this course has taught me alot. Thanks to team Coursera

Muy buen curso, por mejorar: varios errores en los talleres y también no fue posible ingresar a estos durante varios días lo cual atrasó el proceso de estudio.

I love how engaging the course is with its labs and how it is well-packaged in such a manner that encourages beginners to learn... keep up the good work guys.

I found this module very difficult to understand as it was loaded with high end concepts and coding. Might have to redo this course to understand even better.

Course content is very good but I feel it can be more improved if the training is provided at slower pace. Also the examples should be in detail. Overall good

I think it would be good if the units had activities to deliver mandatory since that would allow to strengthen the knowledge acquired. Thanks for the course.

This course is too complete, but have too many questions between videos, its feels like a brake every time, I think is more easer at the end of the videos.

The course content is really great and method of teaching is very specific .Much details very covered during the course and really i gained a lot from this.

Good course if you are beginning data science. You don't need much of python experience but will be better to have if you want to quickly finish the course.

One of the difficult courses among other other data analyst course. But the hands on labs in this course are the best tools to understand the concepts !!

Very detailed and guided course that provides an overview of data analysis in Python with short assignments after each video and interesting lab courses.

insufficient statistic, as the name of the course is Data Analysis, i would expect more classes about the different distributions of data, pdf and pmf..

One of the best courses in the IBM Data Science Specialisation.

I would like to have more quiz questions and opportunities to practise what was covered.

There must be a slightly high level of Quiz, assignment and Project and must have to add some more advanced concepts about statistics and probability.

I would have given it 5 stars but they barely went over polynomial regressions and pipelines and it was a major portion of the end of class assignment.

pros: well organized, clearly explained each step, useful

cons: frequent errors in both videos and the lab, especially on the questions part in the lab