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There should be an Handicap assistance in the course as some of the visually impaired people are finding it difficult to read the assignment codes with their screen reader nvda.

The assignment notebooks code settings need to be modified to support this.

Covers lots of materials. Lab is at end of each week, but I did better following along with coding during each lesson, A good framework, but with a lot of jumps and not much depth. With additional studying from other sources, I got a lot of knowledge.

A lot of concepts are packed into this little course. The course materials are a bit too concise for the concepts to be elaborated properly, so I need to search a lot extra online for concepts behind. But in general, they can be used a starting point.

the course was great and informative, however the pace and information in this course is not sufficient for a person who is new to the python libraries and analytical features, if i may add MSE and R^2 and plots are still not so much understood by me.

Some questions in the peer-graded assignment are not clear and answer box of some questions are not visible so I could not get any point from them. You should better check the contents of the tutorial and make sure that there is no technical issues.

It is a well structured and quite valuable course; it could have been a masterpiece, if it had provided more connections, explanations, insights, in other words programming background related to all those different topics touched over the weeks.

This Course is very helpful for the beginners. This course is very detailed, and well explained. You will go through all the important things required for data analysis. This course's Lab is very strong, I must recommend you to do this course.

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Very informative course... very well designed... a bit fast-paced but concise and clear

it's just that if the final project could have been little more challenging so that there are more opportunities to apply what we learnt in the course.

Great course. I felt like I can run my own models and test them now. There were some strange errors throughout the notebook that were raised in the forums but not addressed in the documents. Aside from that, it was pretty good for a MOOC.

ton of new stuff to learn from.... super informative course...this course will introduce you to a lot of useful and important stuff and the best part is that each topic is explained first then comes the coding part which is just awesome.

This was a challenging course that covered a lot of items. I believe I need more practice in these items (Linear Regression, Polynomials, Ridge, Fit, Predict, etc.) in order to have a much better understanding of the course materials.

Model Development and Model Evaluation content requires more intuitive examples, maybe adding some flowchart to explain the reasons of every step in Modeling and Evaluation. I am making this suggestion to make the course even better.

Interesting class. Clearly designed to cover a lot of ground but not always in the detail some may like. Emphasizes showing some basic analytic work flows, but does not always explain how or why of a particular step in the workflow.

This was a very comprehensive course, but it could definitely use some revising on the labs that caused output issues. Additionally, some of the peer-graded material couldn't be uploaded due to syntax. Other than that, very helpful!

It was a good introductory and pocket course for Data Analysis with Python to me. The concepts were given pretty much straight forward, and the assignement didn't diverged much from what had been already seen throughout the course.

At the end, the final project link was wrong and nobody from coursera or ibm give me an answer. Fortunately, course partners share the right notebook and thus, I could finish my course. The topics are very well for this course.

this was a good course but for beginner like they show explain little bit more regarding python in the video with coding, they are just showing the slides of the codes which make this very difficult to pick the idea clearly.

this was one of the better courses in the 10 course package. a bit more focused and less slapped together than the previous courses. also the lab load and have no glittches, aleast for me, unlike other courses. overall good

This is course is a great way of understanding Data Analysis, model training and evaluation, as well as a further indepth understanding of Exploratory Data Analysis, using Python. It was a great time learning through it.

Very nice and practical course. It gives you the tools to perform a regression analysis on a dataset. Perhaps, I might have focus a little more on the mathematical theory behind some method, like correlation, p-value...

This course helped me a lot in solving my basics about data cleaning, Visualization, Techniques for getting better result and most important how we can judge whether a model is good or not. Thanks for this great course.

this course finally gets to some key Python functions for data analysis. Some of this may be difficult without a basic stats background. Only knock is there are MULTIPLE typos in the slides and labs. Needs to get fixed.

This course went fairly well, I just hope that the information will be repeated in the next course in the certificate program (IBM Data Science certificate) as I don't feel like the information has really sunk in . . .

The Model Training and Evaluation weeks could have been more elaborate. Instead of just telling to do something, it would've been better to explain why we are doing it and how is it working internally, at a high level.