This is the best course I have taken yet in the Data Science family of courses. John Rollins, descriptive Data Science and CRISP-DM methodologies are very useful to not only logically lay out a process but takes into consideration human behavior as well.

The course is useful primarily for its systematization of all approaches in data science. A step-by-step development of research is demonstrated, starting from the statement of the problem and ending with the found solution to the problem. Useful course.

While being familiar with the process of research methodology I did very much enjoy learning the data science terminology associated with it. The only issue I ran into with the particular module was that the instructions for the labs were out of data.

It's really a lot of information than I expected, but it is certainly helpful for helping me to further understand data science. My only suggestion is to include more explanation to the code in the labs to make it easier to interpret for the students.

The methodology was really great. Though this is somewhat a modified version of the CRSIP-DM methodology being used in Data mining. The labs were wonderfully set up to understand the topic. The case study was the most interesting aspect of the course.

This was one of the trickiest courses I have taken yet. I have had to re-read the documents and watch the presentations to get the concept clearly. I highly recommend it to anyone willing to be patient to understand the under-workings of data science.

I was a bit reluctant to do this course, however, after starting I could not stop. I think this course presents the DS methodology in the easiest way to understand for beginners. The examples that the instructor shows are clear and easy to relate to.

This is not a general overview, it's a complete scann to Data Science Methodology that allowed me to see the complete development of a Data Science project with the usage of the tools and intervention of roles previously reviewed in the past courses.

Very interesting and useful course. Final project is also very useful, since it allows to apply immediately the learning skills creating a new brief data science project. I am very impressed by this course and I would like to thank the instructors!

Its a great introduction to the data science methodology. The only thing I wish is that they go a little bit slower in the videos. They're talking about something and I'm reading the slides and then it just shifts over to a new slide fairly quick.

Interesting course, only video-sound was a bit low. Learned the basic steps Business understanding, Analytic approach, Data requirement, Data collection, Data understanding, Data preparation, Modelling, Model evaluation, Deployment, and Feedback.

Good introduction to the methods used by data science. It was a clear walk through the different stages of the process. A good outline to keep available when tackling basic data science problems. I will print out the method and use it at work.

It is a great course in understanding the concepts of how data scientist starts with a business problem and transform that into a solution using data. It takes you through the journey from the problem until the solution and how you go about it.

The course is really good which makes me have a new vision about Data Science, especially the part of Ungraded External Tools. Although there are a few bit of confusing concepts, I have learned so much from the course. Thank you, instructors

This is a good course, teaching on the example of a case study the 10 stages of standard data science methodology, including hands-on labs. The final peer review assignment was helpful to practice the stages in a different, chosen scenario.

its good to know about methodology before going deep to a better ideal, i think this course should be after next courses, because practical labs was hard, if there was practical lab that we can do it all by myself, it will be more awesome.

This course was actually extremely useful in understanding the mindset of a data scientist. As someone in academia, there has always been an inherent disjunction between scholastic and business methodology. This course bridges that gap. :D

This is a very informative course on how the data science methodology process is carried forward when a real problem is encountered. Each phase has been taught with good relatable examples which simplifies the learning process. Thank you!

Very thorough, thanks to excellent narration that had just the right enough repetition. Helpful use of diagrams to reiterate concepts. The Jupyter notebook labs were a fantastic way to illustrate the stages of data science methodology.

Fantástico! Curso super eficiente, traz rápida assimilação da abordagem de Data Science, introduzindo, simultaneamente, Jupyter Notebooks: exmplo e na prática. Os exercícios peer reviewed criam uma deliciosa oportunidade de interação.

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

An amazing course which teaches you the path to choose in order to solve data related business problems. The approaches mentioned in this course are very logical and awesome and can be used to solve most of the data science problems.

This course is excellent, as it helps you understand the way of working, how you should carry out a data science project, and how your final report should look like. This course will help you in making a good report for the Capstone.

Really it is Worthfull Course. Helpfull Training Method. Nice Videos & Specially The Labs Are Worthfull & Helpfull.

I extend my thanks and gratitude To Coursera & IBM To give me chance learning. i Say Best Wishes & Regards. u r great

That was great fun learning a lot of stuff regarding the Data Science Modeling. This is a perfect course to understand and come to a problem solving model for any data scientist. Really changed my perception of solving the problem.