Comentários e feedback de alunos de Exploratory Data Analysis for Machine Learning da instituição IBM

ADVICE BEFORE YOU DO THIS COURSE -- Look at the assignment and choose a data set that you can work with. Try and replicate the techniques from the explanation videos on your data set as you go through the course and then you'll be pretty much have a completed assignment by the time you finish the videos.

A slight problem with this course is the hypothesis testing bit of the assignment. The problem could be as deep as the ocean. If you choose a data set that you know you can get a good binary test from you'll cut down your completion time without losing any valuable learning experience.

Really Poor Teaching. Concepts that were clear earlier was made unclear due to poor intuitive examples. Few concepts were taught really well. But especially around the Hypothesis Testing part, the quality dropped very steeply.

Not clear pre-requisites. Instructions far off from the learning objectives mentioned in the beginning which makes it difficult to catch up.

The concepts are not explained in details. The instructor seems to read from a transcript which may not be the best way of teaching. However, content is great and it can help build a strong foundation.

One jupyter notebook is not able to run because a dataset and a python module needed for running the notebook is not provided. Lots of classmates ask about help in the discussion forums, however, no TA or any help is provided.

Does not go into detail and explain how to really code for hypothesis testing

A very good course if you take it seriously! Good practical tasks where you learn much!

Excellent presentation. Learnt quite a lot.

The Course is quite detailed and well explained regarding the techniques and fundamentals required for exploratory data analysis. Sometimes although I found the contents being spoken in the video hard to understand because of the flow and the accent, but then reading the subtitles helped. Also, one suggestion would be to provide a presentation or some pdf documents for the most commonly used Python commands for various libraries like Pandas etc. for data handling (starting from data reading, cleaning upto hypothesis testing and further). This is because to makes hand notes of all the commands from the demo videos takes quite some time.

All in all, big credits to the team for such a well prepared course material!

great course content overall. couple thoughts related to improvement opportunities: 1.could you consider sharing more python sample code for each section? These samples do not have to be talked through - just there available for students to download and keep. 2. I had trouble submitting my course assignment initially due to the confusing instructions on the webpage. The page said Additional Comment box was Optional but it turned out that one would still have to put in "No Additional Comments". Otherwise assignment could not be turned in. This was a frustrating experience that could be avoided for others if the webpage instruction was more clear and consistent.

The course was really helpful in understanding basic ML concepts and the computational framework we can use for EDA.

But a lot of students had problems with ghost reviews where they received 0 points across the rubric. It took me two days to finally get my assignment graded properly and lost significant time in correcting the problem. Coursera should really do something about this issue.

The only reason that I do not give it 5 stars is because the website of coursera is not good enough to handle the peer review assignments at the end of the course.

This particular course as many others in Coursera, provides minimum possible knowledge with the lowest level of course quality. I will elaborate my point as following;

1) Instructor does not even know the actual mathematical foundations of what he is presenting. He provides example notebooks supposedly process a particular data which does even not exist. I personally and very discretely provided my comments regarding his conceptual mistakes in his presentations without receiving yet any feedback or observing a change in course material.

2) The final projects, even though presenters make money out of this course, are evaluated by peers. With that in hand I have a PhD in Physics, but somehow a random course taker who did not even acquire 10% of my math and coding throughout his/her education is evaluating my final project. Moreover, this person does not even understand well what is written in my project and gives me some random grades. As a result, I don't even get a feedback at all about my grade and or details of his/her grading.

Now, let me put these together. Coursera was a go-to place back in time. Nowadays its quality is not even close to be called 'mediocre'. I had the belief that at least some information can be gained and somehow it was worth taking class(es) back in time. After this horrible and totally not valuable experience, I do not think Coursera is doing a notable or at least an average job. I also have no faith in the comments that you guys publish here from your course takers. I have no reasons to believe them. I would like to clearly indicate that I am neither planning to take another course from Coursera, nor I am planning to suggest anyone to take a course from Coursera in near future.

I feel like the instructor's inability to explain things in detail stems from the fact the he doesn't really understand it as well. feels like:

Boss: "hey I need you to present this tutorial"

Instructor: "Sure thing boss, I just need to read it right?"

Boss: "Yes, but you also need to pretend that you actually understand it"

Peer reviews are also filled with a bunch of trolls who will give you a grade of 0 just for the fun of it - this was the final nail for me. I cancelled my subscription.

In my opinion this course is really bad, the content was not that good and honestly it is not up to the level of a Professional Certificate.

Peer Review needs some moderation, someone marked all zeros, for one of my assignments. We are doing Machine Learning clearly an algorithm for such can be made available. Overall a great Introduction and hands-on guidance towards the Tools and Statistics involved for various business applications in the real world.

This is a real useful course which helps even a rookie to understand the nuances when it comes to Artificial Intelligence, Machine Learning. Interpreting Data etc.,

Subjects were taught well by the experts. I thoroughly enjoyed the learning session.

Very! very!! interesting course, I really enjoy it, I will continue to put more effort into acquiring new skills as much as possible. Thank your IBM and Coursera for giving me this opportunity to learn through this platform.

Thorough, clear video lectures, and good, meaningful exercises. An excellent introduction to the topic of Exploratory Data Analysis and figuring out the general characteristics of any given dataset and its features.

Thanks Coursera

my life has changed after Corona crisis and founding you!!!

Recommended for beginners as well as for those students, professionals who want to get their hands dirty in the data science life cycle.

Excelente como primera iniciativa en el mundo de Coursera empezar IBM. Claras las explicaciones de todos los videos. Muy buenos notebooks para el seguimiento de los temas aprendidos. Excelente!

The instructor from videos is amazing. Great tutor. So far the courses from IBM Machine Learning Professional Certificate are really, really good.

I really liked, that you need to spend time on the independent work which consists of data preprocessing, EDA, and hypothesis testing.

This is a well-structured course with easy-to-understand lectures and practical examples that help a lot in real data analysis life.

Very well curated course. Walks through all the topics in detail. Would be better if the professor had a little bit higher voice.