I like the part that how to work on feature engineering. I understand machine learning is statistic, but I felt week3 week 4 are teaching me how to use python to analyze the data. I was hoping to learn more about machine learning models and optimize their outcome.

I really liked this course, has been extremely useful for me as a starting point for next IBM courses. One suggestion to improve - some concepts are covered a bit superficially, in my view, e.g. Hypothesis Testing. Maybe going a bit more into theory would help.

1- The Lab videos are not clear enough, the font is too small, so hard for eyes to see what is written on the screen. 2- Most of the time Jupyter lab (individual work) was not opened. moving to Spyder is easy but why not to fix this problem? Thanks.

The information was good though basic. I thought the info on hypothesis testing and probability was probably not necessary for an ML course where this should be assumed. The teacher was clearly reading off a script which was at times not so engaging.

The first part of the course was very good, in the second (week 4) I had a hard time understanding it and it seemed to me that too many concepts were given for just one week. I loved that application examples were made to reinforce the concepts.

The course is great but in my opinion the teaching material will be even better and more exciting if it has less text and more graphical visualization of the topic being explained. I feel like the instructor read the slides for me.

The course was great, but as around Week 4, I faced difficulty in understanding the concept, especially when it was implemented in code. I hope that more brief description, especially for code can be included in Week 4.

It is fairly difficult for a beginner - although the level is intermediate for this course and there are a few prerequisites, somehow I still feel that a lot is pending to be explained, esp. in the DEMO/LAB exercises

Excellent content and examples. Would be great if another example for hypothesis testing is added to demonstrate this concept with a typical ML dataset (maybe use one of the previous datasets used during the course)

Great instructions, wonderful demos and insightful comments on the results. The only part that I did not find well explained is the part on hypothesis testing. Some details could be added on t-test and z-test.

Well explained concepts and spoke at the right speed. But, some of the hypothesis testing, probability, and Bayesian statistics material could've been explained better with more visuals perhaps.

Overall insightful and a good introduction to the field, but could spend more time teaching about Python libraries and their functions. Jupyter Notebooks often failed to run (broken URLs?).

It was a very code course, however, it would be nice if the code was available on a notepad while videos played to make things faster. Also, some of the online notebooks were not working.

It lacks a deeper view of the topics and applications on programming for a real world Exploratory Data Analysis (EDA), but gives the basic tools and understanding to introduce you to EDA.

The first week of this course was very informative and with a lot of examples. Although, the second week was difficult to understand, the concepts nad the examples were not clear.

From books we learn a little, but actually we learn is from practical environment, that i found here. I really enjoyed learning this course from the Coursera platform.

Ciência de dados evoluiu, teria que ter um curso nocode para essa certificação, ou uma certificação nocode, Com uso do Knime ou Weka por exemplo

Great course. Just some concepts should be explained slowly and carefully but they are just skimmed through... overall a good course for EDA.

Good introduction. The time estimates to complete assignments are off.

Need a lot more material and direction for assignments to aid learning.

I know some basic statistics knowledge is required, but sometimes the analysis story is unconnected, and sometimes make the story confusing.

The course is good. What it lacks to get the 5 stars is to be able to download the slides of the classes or to have a documentation.

It is a really insightful and interactive learning experience. Furthermore, the trainers and coaches were very knowledgable.

It was good course which help me to understand data cleaning , Feature engineering , EDA , and basic of statistics .

The Week 4 was too hard to understand for beginners in Machine Learning/ Data Science related Statistics.

Really good introduction, it can improve with better references than wikipedia to deepen in the subjects