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

1,715 ratings

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


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The course includes hands-on exercises that allows us to apply the learned EDA techniques to real-world data. This practical approach helps solidify my understanding.


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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.

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1 - 25 of 351 Reviews for Exploratory Data Analysis for Machine Learning

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 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 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 Charley L

Nov 18, 2020

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

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 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 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 Pulkit K

Oct 9, 2021

Excellent course . Covers all the necessary information for beginners. Although I noticed people from non-statistic backgorund have a lot of misunderstaning about hypothesis testing and p-values which is briefly talked about in the course. I would recommend bootstrapping for non-statistic background students ( - Although in 'R', still an excellent site that teaches about bootstrapping in very simple language for beginners. I highly recommend it for all non-stats students)

I have one more suggestion, it would be really nice, if the course can add some examples about usage of hypothesis testing in machine learning besides research purposes like A/B testing, binning of categorical features and so on.

By Minh L

Sep 22, 2021

Excellent, very detailed. However, if the lessons can be expand for hypothesis testing and some of their common test like T test, Anova 1 and 2 way, chi square, would be better further.

By Iddi A A

Dec 7, 2020

Excellent presentation. Learnt quite a lot.

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 Cevdet U E

Feb 28, 2021

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

By Sneha R

Sep 1, 2021

not very clear and not detailed. jumping through courses without teaching basics

By Zach S

May 22, 2021

As with every IBM course, they tell you "not to hard code" but every project/practical exercise from IBM is littered with hard code. To the point where the projects are unable to be completed, without the help from one or two forum posts from a random student who has spent the time to find a solution. This is a growing problem with IBM's courses. I've learned more from other students, finding workarounds for your mess, than I have from the actual course work. Also, the content for this course, and any examples of code, was produced in Jupyter Notebooks. You didn't even create content in your own IDE, IBM Watson Studio, which says everything a student needs to know about IBM products.

By Abhinav S

Jan 10, 2022

This course is not good at all. It is like the teacher is just the reading the screen and you wont understand anything. Not recommended professional certificate too.

By Noor-ul-ain S

Nov 23, 2021

The course is exceptional and a huge learning opportunity for Exploratory Data Analysis. The final project is the best part of the course and helps to apply the concepts to real life data.

By Ajay K S

Aug 16, 2021

IBM courses are most valuable courses, quite a lot of learning happens here. I recommend students when it is time to chose a Brand IBM can be considered in top 5 List. Happy learning.

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 Alice Y

Sep 8, 2021

I leant EDA in the uni with R. This course teaches the same thing in python and adds some extra stuff, really good course.

By Verena D

Oct 12, 2020

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

By Aadish j

May 13, 2023

Best course for learning exploratory data analysis

By Иса М

Feb 21, 2021



Jul 11, 2022