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.

I just finished the IBM Data Science Professional Certificate. Out of the 10 classes, I enjoyed this one the most. It was a little hard to keep from getting confused at times, because there was so much information, but I liked how it was all explained so well.

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.

this course is really helpful in understanding data analysis with Python. the assignments are designed in a way that learners actually learn all the steps easily and thoroughly. theory and hands-on , both are covered in a thoughtful manner.

thanks

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

Great course. few things to make it perfect:

more mathematic explanations required.

more details and explanations about the code itself - the methods and the opportunities inside it.

make the videos more interesting and not so monotonic and boring.

I thank Mr. Joe Santarcangelo 🙏🏼 who helped me learn how to prepare data for hashtag#analysis, perform simple hashtag#statistical analysis, create meaningful hashtag#data visualizations and hashtag#predict future trends from the data👨🏼💻.

The videos explained carefully the structure of the codes and explained the result. The graded quizzes and particularly the final project is also very helpful because it will let you review your notes and test your understanding of the course.

It was a wonderful experience in learning the course. It was completely a practical approach. I enjoyed the process of assignment submission and reviewing peers assignments. I would highly recommend the course for everyone. Thanks once again.

Outstanding course! Valuable information and methodologies all with clear and concise presentation. The labs are detailed and filled with awesome examples. Coursework is intuitive and easy to understand. I would highly recommend this course.

I have really enjoyed doing this course. The videos, the assignments, the exams, all of them cover very interesting and challenging topics, such as data modeling with different techniques used in machine learning. Thanks IBM and Coursera!

Its very nicely designed course .Its designed such that you get brush up your fundamentals and get to know the real taste of statistics and probability applying practically to real world. i really enjoyed and earned a valuable knowledge.

La calidad de los cursos de coursera es excelente. Obviamente tienen detalles que se deben trabajar como algunas presentaciones que no coinciden con las voces en off. Sin embargo con suspicacia se pueden solventar estos infimos detalles.