Awesome. A must take course very handy at giving the foundation of data analysis with python and what a nice introduction to linear regression with the library sklearn. For more it looks more like an in-depth course in linear regression. Kudos, the explanations of concepts were well approached.

This course starts with Importing the dataset in Jupyter Notebook, followed by Data Wrangling, Exploratory Data Analysis, Model Development and Model Evaluation, and end with the final assessment applying all the concepts learned.

Thanks to IBM and Coursera for this great learning experience.

the course is too good to be true! it is an elaborate explanation of all the terms with the logic behind it. Seamless experience with the inbuilt code writing lab. I would highly recommend it to all who are at the doorstep of data analysis. This is the first step towards it, and a mighty one!

The hands on examples for practicing on IBM cognitive lab, videos and lecturers made are great and helpful. The course contents are clear, precise and lecturer is very knowledgeable.

Joining and getting help from course mates and moderates in discussion forum is Excellent!

Ashfaque A. Joarder

I loved this course!

Though, for a beginner like me, it can be kind of confusing to be shown things that are not covered in the course (i.e, plots in which a lot of characteristics have to be set...), this tends to happen in labs.

But for the rest, everything was crystal clear!

Best wishes!

The first "week" was way too simple. I believe things like "what a file path is" should belong to another course. The last 4 "weeks" gave a good picture of where to start with data analysis. The whole course can be completed after 5-10 hours (depends how long you play with the dev tool).

This course builds your fundamentals of data analysis ,from how to load data to data cleaning, removing missing values, data interpretation, building models, testing them using pipleine to check if model gives proper output , splitting data sets as test set and for model learning. etc

Hi, thank for providing this course. I have a concern about my certificate of data analysis with Python. There is no Coursera logo, and it is different from the other certificates that are related to this course. Please check kindly and let me know how to solve this issue. Thank you.

This course core purpose is to teach the student how to perform analysis in detail. I have taken a lot of courses related to data analysis but no one teaches in detail and gives great examples. I highly recommend this course to all student who wants to learn data analysis with python.

I have just completed the IBM Data Analyst course "Data Analysis with Python". The course has a lot of practical knowledge with real-life examples and datasets for analysis. Recommended for students of Data Analysis! #python #data #dataanalysis #datasets #IBM #Coursera #HabibManjotha

This course is actually harder than expected due to the python programming however I felt I truly benefited from it. I have learned and used Python before, but the python code in this course sets a new high bar for me. I'm going to go back and study all the labs in this course again!

This was a good course. It had lot of content you will find data analysis with pandas library along with analysis there is regression machine learning model also and model evaluation section. Overall it was a great experience. Content was nice and I recommend to everyone to enroll.

Really a step up in terms of difficulty compared to "Data Science with Python". Since the final week's content is judged on quiz and not a stand alone assignment, one must revise this course from time to time for the libraries referenced and model analysis approach. Great resource!

This course was presented clearly and explained statistical concepts in a way that made them relevant and practical. The assignments were challenging, requiring review of notes and Python techniques made during the lectures, thereby reinforcing the learning outcomes in a good way.

This course is very good start for students who are planning to go into machine learning specifically.Students who have no Idea about regression and math find bit hard but little more effort from student side is needed. At the end you will have a zeroth tool for machine learning.

very good and goal oriented cours, no loss of time , covers rapidly subjects that really increase students knowledge on python tools for data analysis. very interesting and useful cover on regression for tolls as well as explaining the statistical concept in a very simple way.

THE RATING IF POSSIBLE TO GIVE 100 THEN I CAN GIVE 100 FOR SURE , BUT OUT OF 5 IT DESERVES 5 WITHOUT ANY DOUBT I CAN SAY THIS COURSE CONTAINS THE ALL BASICS + ENSURES A GOOD UNDERSTANDING OF ALL THE THINGS NEEDED TO DEVELOP THE DATA ANAYSIS SKILLS ALONG WITH PRACTCAL APPROACH .

It's a great course. I enjoyed a lot. Easy to follow.

However I miss go deeper with the meaning of the numbers (for example the R2 each time we calculate it) and to get deeper insights of the data after having on the table many interesting number for the analysis.

Thanks for all.

Everything is terrific! This course has in-depth lecturing while providing a great resource for practicing.

The only thing that bothered me is that there was a misleading ipynb url at the final project section. There were two different urls directing to two different projects.

I have a specialist diploma with business analytic. However that was using software to do the visualization and analysis.

I guess it helps on my journey. I can relate the machine learning concept to this course.

This course is fantastic. Teaching you step by step progressively.

Great overall experience. Although it would be great actually understanding the Python language in ways where you're actually learning about what each code means and does, I think this is more of an introductory course in terms of just understanding what Data Science is.

Good course giving exposure across all expect of data mining and data analysis with regression modeling and evaluation model through visualization and correlation, Rscore and much more. Very well organised modules with jupyter notebooks for each assignment and practice.

Really good course in Data Analysis for beginners. The videos and labs are very well planned and structured. Personally, I can say for sure that I have gained more knowledge about Data Analysis and am even more motivated towards Data Science after completing this course.

This course give a great introduction to the Python Packages and methodology to visualize the data and also evaluate the Model. This is good introduction course which gives concise understanding of concepts and all important python libraries required to get the job done.