This course is very special as it gives the practical knowledge of how does a data scientist think while doing his job. It teaches us to create different visions to see a single problem with a different mindset. The practical example of "Congestive Heart Failure", teaches the realistic thinking of a data scientist.

This course is the third part of the multi-series course of IBM and whatever I learned in the previous two courses, all those were implemented in this course.

I think methodology is the most important part. I've been being experienced since my undergraduate level that any problem can be solved when it's methodology is clarified. Many of our teachers asked us of the method first, then insisted to do. So, from this course arranged by 'Coursera', anyone having some story-telling capacity with some science-background including math, can learn a lot for solving today's business problems lie in marketing or other areas.

This course is incredibly important in my opinion as it really focuses on the methodology, and not technical aspects, forcing you to think like a data scientist and teaching you on how to approach business problems that can be solved by using data. This is a key course/lesson that is usually missing from other Data Science tutorials and courses, which tend to focus more on how to perform a certain step, and not show the great picture of what all the steps are.

This course was really well designed. The explanation built around the hospital example and the external working tool (notebook) with the cuisine example was very helpful for understanding. Especially compared to the rather mediocre designed "Tools for Data Science" course this course was ingenious from a didactic point of view. I also very much appreciate that the external learning tools were jupyter notebooks and non of those dysfunctional IBM cloud tools!

I have previously dabbled in various parts of the full data science process. Including data collection, data understanding and data preparation. I have also separately worked on data modelling and data evaluation on Kaggle. However I am very grateful for this course, as it has enabled me to be able to appreciate the big picture view of data science and has provided me with a framework to use for future data science projects. Insightful and very comprehensive!

This course was very interesting. I had zero knowledge about methodology or the techniques used to solve problems. It was nice the course was structured around "cooking". The only problem I had were the videos were recorded a little low, so I needed to raise the volume on my computer to hear. There were some videos that did not have any close caption or the transcripts were in Turkish. Overall, a great way to learn about Data Science Methodology. Thank you.

I loved this course. I am very new to data science and I was stuck on what is data science and how to start with data science. This course gave me a very good insight right from how to start analyzing the problem and what are the stepped to be followed in each and every stage. The main highlight of this course is they use a case study to explain what happens practically in each and every stage. It helps in properly understading the concepts

I enjoyed the content of this course and felt like it gave good examples and case studies. The assignments were helpful to get a feel for using Jupyter and python for data science, and the real world data set was useful. I did not understand their differentiators for descriptive vs predictive approaches, and earlier in the course they had a third- classification- which seemed to have been merged into one of the two others later on.

This course takes you through the mind of a data scientist. How a data scientist strategically thinks to solve a problem? The methodology of problem-solving will be embedded in your mind, more relevant to data science problems. How a data scientist should behave at various stages and how it can be effectively done, what are the alternatives, and what is mandatory? These questions are answered when you follow this course. Good one!

First, I really appreciate the helpful support you've given me, I am interested in Data Science Methodology and this course helped me a lot to understand those concepts in an easy and right way. In this course, I learned how to use the data within the decision-making process, how to apply the data correctly to the problem at hand, a methodology that can be used within data science.

Thank you once more for your help in this matter.

It is extremely important to have a methodological guide to help you establish the steps to follow to execute data science projects, most people want to dive head first into programming, cleaning data and building fancy machine learning models, but they don't know how relevant it is to have all the phases of the process clear. Don't be like the rabbit in Alice in Wonderland, running without a clear goal.

I really enjor it. Make me thing as different way in systematic but creative way. There are different ways to solve a problem based on the question we are getting. I know that we only cover some of them for trying not making the course that large. Would be wonderful to have optional resources beyond the course on how to attack different kind of questions as well as evaluating which kind approach use.

The IBM Coursera course "Data Science Methodology" was a wonderful learning experience. The systematic approach, hands-on exercises, and real-world case studies gave participants significant insights into data science processes. The course offered fundamental skills while emphasizing ethical issues. It was a well-rounded education that prepared me to take on data science projects with assurance.

The data science methodology is a fabolously designed coursework. Before this i did programming courses on courseera this is first time i have undertaken a course that equips me with the theoretical knowledge of data science methdology. This course clearly states the different stages under which a data based project should must undergo for systematic and succefull completion of the project.

Good Day

I am personally thankful and grateful for this opportunity .

Thanks and Warm Regards.

ABHIJIT SENGUPTA

Portfolio URL : https://about.me/abhijitsengupta

Website : www.pactolianconsulting.com

E - Mail : abhijit@pactolianconsulting.com

Kolkata , India

Skype : abhijit.sengupta357

Ph. : + 91 33 25907110

Cell : + 91 9163863607

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+ 91 6290750012

This course helped me get more familiarized with structuring an efficient methodology to use data science in different scenarios. Seeing data science as an iterative process that is always subject to improvements and recognizing how the different stages relate to others helps to understand that it is a process that never ends and provides an efficient solution to specific problems.

The Data Science Methodology really opened my mind to the meticulous process of 'solutioning' as a Data Scientist. Understanding Business Case and what the business owners want, dimensioning to know the approach analytics, knowing the data set to work with and how to analyse same, and ultimately modelling and also learning from the model via feedback so as to make things better.

A quite good course, I like methodology and theory behind data science. I am glad the peer review is 10%, and the quizzes total 90%. I think this helps students to have objective standards and not be dependent on anonymous peer reviews.

Just a little point at the end: The example of the US health care system does not seem to be completely useful for students of other regions.

Thank you very much for someone in the forum who can answer some simple questions. I have learned more skills of using Python and Jupyter Notebook in this course, which will benefit me a lot. In the future, I plan to learn more about programming, mathematics, probability theory, statistics and the art of communicating with people, and STRIVE to become a data scientist

The course it's very essential because in any aspect of web programming, engineering studies and even this, it is very important to establish a methodology to understand how to approach a real life problem, as well as to understand the different stages that are presented when a Data Scientist wants to use the different approach and techniques taught in this course.

For the case study presented, specially during modeling and evaluation phases, more elaboration would make the course better. Specially during modeling evaluations and ROC.

Also the course being conducted in a recorded slides is not appealing for student engagement. Would be great to have some videos with people explaining, like in the in course 1 of this program.

This is a very easy to understand course, I always have trouble staying focus while studying but this course is very fast and keeps you interested in the subject, it explains the methodology very well and its easy for you to retain all the information, and its very friendly for people without english as their first language, I highly recommend it, thanks a lot!

This course made me realize that people tend to jump to execution mode without even understanding what the business really want from them, and by not having a proper understanding they either produce a system that adds no value to the business, or they waste unnecessary time. The tools from this course is very helpful in any field that you are working in.

While the course is brief, exploring only one methodology in depth (predictive), it is well done. I could understand the exercises well. One fix would be a quiz asked what percentage was on a previous table. I did not take the time to memorize the values in the table as I don't see how that is relevant. A better quiz question could be been formulated.

It helps me in understanding the CRISP-DM, importance of Business & Data Understanding, iterative process from Feedback, Modeling, Refinement and Redeployment. The analogy with cooking task made it easier to grasp the concept. 'Top-down' and 'Bottom-up' approach to data science also explained very well. Thank you IBM and Coursera for this course.