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Learner Reviews & Feedback for Exploratory Data Analysis for Machine Learning by IBM

354 ratings
87 reviews

About the Course

This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You will learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing. By the end of this course you should be able to: Retrieve data from multiple data sources: SQL, NoSQL databases, APIs, Cloud  Describe and use common feature selection and feature engineering techniques Handle categorical and ordinal features, as well as missing values Use a variety of techniques for detecting and dealing with outliers Articulate why feature scaling is important and use a variety of scaling techniques   Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience  with Machine Learning and Artificial Intelligence in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics....

Top reviews

Apr 16, 2021

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!

Mar 30, 2021

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

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1 - 25 of 88 Reviews for Exploratory Data Analysis for Machine Learning

By Tusarkanti N

Nov 6, 2020

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

By Kevin S

Nov 8, 2020

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.

By Christopher W

Dec 31, 2020

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.

By Nihar D

Oct 19, 2020

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.

By Shangying W

Sep 5, 2020

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.

By Charley L

Nov 18, 2020

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

By peker m

Nov 30, 2020

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.

By Arnold D

Nov 28, 2020

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.

By Tao K

Mar 19, 2021

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.

By Ufuk E

Feb 28, 2021

It does provide useful information but not much. There is very less hands-on practice provided.

By Ferley A

Jan 24, 2021

if you really make the exercises and the final assignment the course really contributes you to better understand Data Analysis

By Verena D

Oct 12, 2020

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

By Iddi A A

Dec 7, 2020

Excellent presentation. Learnt quite a lot.

By Isa B

Feb 21, 2021


By Ashish P

Dec 26, 2020

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!

By Priyanka B

Nov 30, 2020

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.

By Darish S

Dec 1, 2020

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.

By Sashank T

Jan 25, 2021

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.

By Abhinav M

Oct 25, 2020

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.

By Sarath B S

Nov 26, 2020

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.

By Orah R O

Jan 22, 2021

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.

By Bishmer S

Jan 25, 2021

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.

By Nosaybeh A P

Jan 24, 2021

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.

By Chien N

Jun 16, 2021

A​ solid introduction to data analysis. There is a small note: the instructor uses a new version of pandas. If your notebook produces errors which are not suppose to appear, please update your pandas library.

By Luis P S

Apr 17, 2021

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!