It is a great course. The one thing I believe could be better, is to deepen the scope of the mathematical concepts. Indeed, it is a course that assumes knowledge in that area, but it would be great to include links to papers or articles that explain those concepts.

Content was decent. Do ungraded labs provided as practice exercises if you want much exposure and and free flow of code while using the data analysis libraries. Overall, the course is helpful for an intro and intermediate level. Will definitely work as a refresher.

I think that you missed more detailed explanations on how to analyze the results, especially for those of us who are not mathematicians or with advanced knowledge of statistics. But, is a fact that In the end it was the course i've enjoyed the most. This is awesome

Great notebooks and clear content except two points :

1 - polynomial regression and pipelines have not been enough thorough and detailed. Quite complicated to aprehend

2 - final assignment question 8 - nothing to do. answers were already in the downloaded notebook

The course is quite challenging for me as a beginner of using python to perform linear/non-linear model development. It is good in terms of the plenty of content for people to learn but it is quite hard also as it would be better to have more practice / examples.

This course teaches you important python liabraries like pandas, scikit-learn. It also provides information about regression and helps us to build a model for a given case. Overall its very nice course for getting idea about how to do Data Analysis using Python

I think this is a decent course that introduces data analysis on a basic level. The first 3 weeks were really well written, the last 2 weeks have some faults in them though, like values referred in the text which does not match with values written in the code

Very good introductory course on data analysis using Python! It is best for people who already had some level of analytics experiences before as it sometimes goes a little bit fast. But very good in general, covers a wide range of topic, with good exercises.

Good to start with, this course provide you with the step involved in Data analytics but no logic behind these steps are provided. If you are new to python library this course will be helpful for you as it involve use of pandas, Scikit, Scipy and matplotlib.

Really enjoyed the Labs, specially the last ones that were long and covered a lot of material in depth. I think the course would have a better user experience if it wasn't for the many spelling mistakes and small bugs, specially in the Jupyter notebook Labs.

Toughest course so far. I liked being able to visually see the statistics behind data analysis, which was much more helpful than the textbooks I had to use to earn my math degree! However, the final week was still a challenge to get through and understand.

I liked the structure and pace of this course. The videos and exercises were helpful and the final project was a very good measure of what we had done in the course. I took off a star because there were more typos in this course than some of the others.

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.

Bom conteúdo na abordagem das principais funções para análise de dados, porém, carente de fundamentação teórica em relação às análises. Por não haver pre requisito, os fundamento poderiam ter sido abordados, ainda que de forma superficial.

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.