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.

This is how all the courses should be set up. Weeks are set up in an organized fashion of Videos, Lab, Quiz. No major things missing from instructions that waste your time, like the other IBM SQL course. Shout out to Joseph Santarcangelo for planning a beautiful course.

Very good introduction course into data analysis. Would be nice to elaborate more on Ridge and pipelines. Explanation of the math behind the library functions or references for further study would also help understand what method is more suitable for certain tasks.

What an amazing way to learn such a special set of skills from best platform of Coursera.

The IBM Skills Network Lab is really fantastic.

Also on IBM studio is speechless.

I will definitely recommend this must have course to all passionate learners around the globe.

Help to understand the process of doing projects on Data Science , As i tried to start with one Data set but i was not sure what to do, how to do. with the help of this course, I came to know the step by step process of Data Wrangling and making models. Thanks

Covered a lot of great material. Sometimes I went to other sources on the internet to get some more detail on parts that I did not understand. I all turned out well. The quizzes and labs are important. I must have taken a lot of work to put all this together.

This is very condense course. However the quizzes after every chapter are very timely, and one at the end of the week for entire week, helped a lot. The labs seems very real world, downloaded some notebook am sure will be using them in real world later on.

Well-structured. Offered clear instructions in the subject of model development, model selection and model evaluation. This lays the foundation to help me to learn further on these topics. The exercise for each unit is a good summary, helpful for review.

Very good coverage of basic skills needed for Data Analysis. Not very challenging if you have previous experience programming and have a general understanding of basic statistics. Would've liked a more "interactive" course regarding the codding assignments.

Complex but interesting course. I learned a lot and what was taught is very useful. For a beginner this course will be almost impossible so an advanced understanding of the concepts is required. My review of this is 8 out of 10 with complexity of 9/10 .

It turned out to be a very structured course with very high quality and detailed explanations. Great Python practice, parsing new libraries, and most importantly, explaining the statistical modeling and forecasting framework for a wide variety of tasks

The labs that included Jupyter Notebooks were very well done and left me feeling like they weren't impossible, but challenging enough to solidify my skills. Labs are extremely important for me in an online learning environment, and these were great.

I am highly impressed with the teaching methodologies and how the course is structured. By now, I can import, determine data types, conduct data wrangling which involves replacement ir dropping and regressions. Thanks to Cousera for the opportunity.

This it the first course I take it help me to understand Data Analyst way such as Systematic Thinking, Coding concept,This is great

And to the future learner You should have a basic of coding in PYTHON it may help to easily complete this course but

This course is an excellent continuation of the previous IBM ones. Actually there should be one whole course teaching the basics of statistics so that what is taught in this model makes more sense for those who have never studied statistics before.

I really enjoyed this course. The content was well described. Quite complex, it required me to explore other external material, probably because I lacked the sufficient statistical knowledge to run through the course smoothly.

Thank you Coursera!

Great Course which gave in detail information and Hands on lab about different visualization libraries available in Python such as Matplotlib, Seaborn and plotly for charts and dashboards. Useful course to understand and learn data visualization