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.

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.

pros:very easy to understand, even the statistics knowledge

cons: incomplete python sentences in the video require extra efforts to undertand, such as no previous sentenses for an object (i.e. x_data)

Thank you very much to the instructors. I liked the course but it could have been better designed. More exercises ascending from easy to hard & real and teaching quiz questions would make it perfect.

Great course to learn the basics for Data Analytics using Python.

I really liked the framework and data analysis template or process given in the labs. I will use them as a reference for my real job!

Really interesting course, if one wants learn programming language. Well designed and structured. Only suggestion is, if the small videos contains example that be really great to understand it well

Very good course that goes straight to the main topics needed to work on data analysis using Python. This will kick start my learning process which will be followed with a lot of coding practices.

Good course, sometimes moves a bit fast in the final modules and the labs are quite tough but great course and would recommend to broaden your knowledge of coding, data analysis and visualisation

Although good to learn the know-how of basic data analysis techniques, the quizzes are predictable and you don't end up coding as much as you should.

A good starter course to wet your feet in DA!

Very clear and easy to learn. The lab helps a lot, it gives me an intuitive instruction of the class. But some of the points seem too shallow, hope the course could provide some deep knowledge.

Good Course. Very good overview of Python libs -Pandas, Numpy, Matplotlib, Scipy, Scikitlearn and Seaborn. I really enjoyed learning about them and seeing the usage. Highly recommended course.

T​otally overwhelmed with the course contents and easyness in teaching. The course will make you familiarize the fundamentals in a way that you will never forget when you used in a real world.

It will be helpful if a video is added on:

1) how to store multiple results from different models in single dataframe

2) how to automate the process. More example needed on Grid and Pipeline.

Great course, you can understand in a general way the use os Python to analyse raw data and organice it to create a better model. However I couldn't use in a proper way the external tool.

Theory and examples are good. Suggest having full and complete Python course code with more examples of each coding. So we can get more ideas and understanding of the Python environment.

Questions while listening to lessons in some of the lectures are coming before theory explained by the teacher .Better if question is at least 10 seconds after related theory explained.

Los temas tratados son muy útiles y se desarrollan de gran manera. El herramienta de LAB es la más completa del curso. Considero que se puede aumentar la rigurosidad de la evaluación

This was pretty good, I think maybe the best in the IBM machine learning certificate. I took Andrew Ng's course prior to this, so to watch python sklearn in action was a real treat.