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.

This course was very tough and took me the full 6 weeks. I struggled during weeks 4-5, but then felt adequately prepared for Week 6. The final project provided me the opportunity to put into practice what I learned.

if you could update the curriculum to the latest versions of macOs and windows, it'll be easier to grasp and simpler to understand, it was indeed helpful, but the pace could be increased if the above is also done.

Tuve problemas con crear la cuenta en IBM cloud con mi correo personal primario , no pude encontrar soporte ni orientación de que hacer , me toco ingresar con otro correo , no se porque no fue posible con el mio

This course is pretty good and give a good introduction to data analysis with python. However, there is a problem in the course's methodology : functions are given without any introduction...just implementations.

The course was good in introducing Pandas, Numpy and Sci-kit to beginners. Adding more graded programming activities would be a great addition to this course, as there is only one graded programming assignment.

Certain parts were too fast, and there were some technical issues with the labs at times, but there's always the possibility to look up the blurry parts online. Overall it was interesting and well put together.

The course was clear and well explained, but perhaps too little dense, a good introduction, but it certainly requires other courses to delve deeper into the topics, especially regarding machine learning models

This definitely could be more project based than it was, and focus more on applying coding skills than just reading them and watching videos about them, but it's a great overview of some useful techniques.

The final assignment had some errors in submission with some questions not allowing for upload of the answers (Question 3, i.e.). Did not feel great about this error. Otherwise, great course - very useful.

Overall a great course. All essential Data Analysis processes are covered in this course. A small nitpick is that Week 5 material was a little less in depth. Moore examples in videos will be a great help!

Los procesos de practica asumen que el manejo estadístico, es solo dar el resultado, pero creo que el contenido es bastante profundo y la practica debe ser mas concentrada en evaluar diversos escenarios.

Great course for introductory data analysis with Python. Very good for fundamental understanding of overfitting, underfitting, precision, accuracy and using grid search method to optimize fit parameters.

Some Statistical terms and concepts were covered quite briefly due to which some amateur student has to refer additional contents like Youtube. Same with me. Quizes can be made tougher to raise the bar.