Very Nice course , will teach you in detail all the techniques of EDA with practical code.

Was able to learn and practice many topics in this course. Very useful for Data Analysis.

Good instructor, good knowledge level, minor mistakes in some of the notebooks provided.

Good introduction. Quiz questions mostly on terminology and not understanding.

Very interesting course, good for getting ideas to deepen the topic!

I​ enjoyed the course, the example code/labs were awesome

G​ood introduction and Exploratory Data Analysis course.

A good background in math and some python is needed.

I wish the hypothesis part was a bit more detailed

B​etter than other IBM ML certificate series

Quite interesting course for beginners

It was nice learning experience.

Very well structured

Good content.

The course material should be provided to allow better absorption of the large amount of information presented. some of the topics needs to be discussed further with more examples and concept declaration especially the hypothesis testing section.

I liked the course content but I would like a more interactive approach that show us how to do hypothesis testing in python. The teacher just reads the courses.

I think, instructor went too fast in many cases. Some topics needs to be restructured with more real life examples and interpretations.

I do like the course in generall. But some slides, are very text heavy, which i do not prefer.

The videos are not well explanatory enough.

This course is too surface. You must have a solid background in statistics and be familiar with pandas/numpy python libraries, otherwise you will spend a lot of time just to learn these libs. Also there is some basic info in lectures but assignments contain much complex and harder tasks which were not discussed in the lecture. And the tasks already have answers , so there are questions and solutions in one place, it is very weird and annoying

Frankly, the presenter is a poor educator and the course materials are weak. The examples are limited, some explanations verge on incorrect (description of p-values), and several of the graded test questions are ambiguous and encourage rote learning of the teacher's preference/positions, rather than testing the underlying concepts. I expect better from IBM.

Poor explanation of many concepts. Felt i the instructor was reading the material in a neutral manner and was not emphasizing on key moments. Also lack hands on opportunities and practice to help understand the concepts.

Overall seemed more like a summary of various titles and definitions.

Course videos are way too long.

No Jupyter support, so for the coding exercise one has to download the notebooks and either use Google Colab or locally installed Jupyter notebook.

The course starts well. Then it goes to statistics and not so much to machine learning. The assignment is not so geared towards machine learning.