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.

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.

Although I had done such data analysis elsewhere in Coursera, this I found very comprehensive and systematic. I wish the topic of statistical significance tests was covered in some detail based on real data, rather random data generated for the purpose. I feel this area should receive more attention from the designers of the course. Thanks for all efforts put in by the faculty and all support person in the background. Thanks a lot..

I am on the IBM machine learning specialization professional certificate track and this course is my first course in the track. It is a very simple course, but it touches on the most important topics before performing any machine learing related work. I highly recommend to complete the machine learning specialization certificate after completing the course.

Very friendly and extraordinary course for those who are looking for machine learning profession. The Data analysis and other process were well taken throughout the course. The Teaching members are well qualified and understandable so that we can have a clear thought on a particular concept. Finally an awesome course that no one should miss!!!

This is the first course where you will learn how to use Jupiter Notebooks. For this purpose, you will learn machine learning concepts and more. It is not designed for beginners to learn. The prerequisite is that you should have some knowledge of mathematics, as some mathematical formulas such as linear algebra will be used in the course.

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.

Thanks to learning on Coursera , I'm able to add my courses to my Linkedin and resume that make me stand out from my peers.

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 course helped me to understand basics of AI /ML, Data Analysis and Hypotheisis Testing. Indepth explanation of some topics were plus point of the couse. Now, I am capable of doing Data Analysis with 100% confidence.

Thank you @Joseph Santarcangelo, @Svitlana (Lana) Kramar (Instructors) and IBM

This course has to be the best Data analysis course on Coursera. The explanation is to the point. A prior knowledge related to statistics, probability and discrete mathematics is very important, because the instructor assumes that you already know about the same. Superb course altogether!

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.

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.

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!

Very detailed course of Exploratory Data Analysis for Machine learning. Ready to take the next step in data science or Machine learning, this is great course for taking you to the next level.

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,..it would be better further.

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.

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.

Very nice course which explains beautifully about data cleaning and the statistical approach and then statistic model and then it ends with the hypothesis testing.

T​his course was really good for me because it went into depth on what I believe is the most important part of ML which is the data analysis and preparation.

This is by far the best course I've encountered. It has an in-depth explanation of the codes they provide. Smooth and easy to understand language.

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.