good

none

Nice

Good

good

ok

<3

NA

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5

P

First, I would like to thank everyone at IBM for putting this course together. It’s like ordering a meal at a famous, beautiful, expensive restaurant. The customer orders the food and then they get what they ordered. However, they didn’t see and hear Gordon Ramsey in the kitchen and the fight it took to bring the best dish to you. When we as students come to the course, everything is already prepared. We don’t see the hours of hard work and extreme attention to details that go into it. So thank you all for what you do behind the scenes. I’ve learned a lot so far and I really can’t wait to keep going. In this course I really like the simple approach it took in the beginning and the illustrations and comparisons to cooking. It made it really easy because who can’t identify with wanting to have a good meal? Hopefully I can add a small touch about what I’ve observed to the vast knowledge of IBM. In the course, it was explained well what the data methodology is and then how that knowledge was applied in the case study. However, it was difficult to understand why that knowledge was applied the way it was. It felt like a math equation was shown on the board and then right after that, the answer was shown, but what was missing was the steps in between of why that was the answer. Another part that made it difficult to fully comprehend was the labs. I was looking forward to actually working with data, but everything was already there and it felt like the answers were shown to me without helping me understand why this conclusion had been reached. It’s easy to pick out things to work on because nobody is 100% free from flaws and really who am I to attempt to suggest anything to an industry that doesn’t need my viewpoint? I do hope that this was received well. This course still was a very hearty meal and left me wanting more. I look forward to the next course! Thank you again for all of your hard work!

The overall content was interesting and helpful in understanding the steps a data scientist would go through when taking on a new project, but I had some issues with this course.

Many of the videos did not have a transcript which impaired learning and made taking notes much harder. 

The quizzes had questions that were confusing and whose content was not covered explicitly in the course.

The lab code did not work in the virtual environment - I hadn’t had an issue with this until this course and I noticed that many other students posted in the discussion forums with the same problem. 

The lab open ended questions were not well thought out or intuitive - the “correct” answers to these questions seemed out of context and was not taught in the course content. 

This is the first course in the IBM Data Science Professional certification where the video screens had different content than what was being said in the video lecture. This makes it very difficult to know which information to take notes over and what is or isn’t important. It’s much more helpful to have the slides match the transcript to avoid confusion and make the learning experience easier. You cannot take in a slides information AND listen to someone saying something different at the same time - ineffective. 

You have to stress more the importance of this module. It is the one that really makes a person Strat to think like a data scientist, to understand the ration between the different components of the elements of the field. Also, I found that there is little explanation about confusion matrixes in this module or before hand, and those are really important. Finally, the applied tasks where excellent; but the final assignment was far more difficult than what the individual tasks prepare you for. There is a lot of additional learning one has to do on the side to really deliver. It would be nice to have a suggested study extra material, I personally used Kaggle of my own decision, if it was not for that I would have been overwhelmed by the final assignment or would have presented something very poor which I might not have really understood. So a guide for extra studies in order to reach the skill requirement to match the difficulty could be awesome. I don't know, maybe I'm just a nerd jajaja.

This is the first course in "IBM Data Science Professional Certificate" which seems to be useful. Unlike hand-on courses, which present tools, methodologies and technics, this one gives a solid overview of Data Science problematic areas and describes successful real-life Data Science project.

Let's say, tools, technics, algorithms are related to tactics, while this course presents strategy. Both are equally important for problem solving.

Excellent tactics without strategy becomes a waste of electricity, disk space, time and money with only partially useful results. In fact, one may create a good classification models just to qualitatively prove known things, but these very good and precise numbers won't help you to resolve business question being asked. Excellent strategy without tactics is even worse - one may know where to move, one may know how to move, but is not able to perform even a single practical step, because execution is compromised.

Concept wise, the course is good. Case study wise, case studies should be more understandable with clarity. The dish is based on Japanese. There can also be another example for case study taken, as the domain knowledge here is related to catering. I guess there are no students with catering base.. haha. Atleast some other example case studies in the domain of IT or banking can be given. There should be more explanation on descriptive analytics also along with case study.

Also the Final Exam Rubric should be more assessing. From 3 marks there is no 4 marks for the last qustion. Its either 0 or 3 or 5. All 5 stages are mentioned in last question, for which good explanation should be given. But most explanations given by peer students are quite irrelevant, even though they have mentioned the 5 points. So there should be 4 marks provisioned for partial answer with all 5 stages mentioned.

Some terms are being assumed to be known for the students. It would be better if the videos are more interactive in which a real person is being shown while explaining with supporting graphs and pictures and numbers. Some methods are being used in the case study like the decision tree which to some extent is not fully explained how is it the best method and what would happen if another method had been selected instead. Some graphs and pictures presented in the videos should be available in a different section for later used such as the diagram of the Data Science Methodology under a section designed to provide the students with additional materials.

Overall good experience, but would recommend including some notes/slides at the end of each week, so that a student can can prepare more easily for the quizzes and assignments. Also, please focus more on the mathematical explanation rather than just giving a theoretical explanation for everything. For instance, in the hands on lab, I had a hard time determining what is the input data and what are the labels for a decision tree model. Also, the data formats are not properly explained like what is .csv format, how to access elements of it and how to play around with it in general.

Having a manual guiding you how to proceed is always a big relief. And this course does exactly the same. It gives you a manual (methodology) using which you can unearth the questions you seek answers to and systematically complete the objectives for which you seek Data Science's help. Filled with examples and labs, this course, to a large extent, takes you to the journey a Data Scientist takes while solving a problem. Steps involved in Methodology owe a bit more elaboration though, this would give a better experience to the learner. A great course overall, loved it!

The Data Science Methodology course was exceptionally well done. It was served up in bite sized morsels that were easy to ingest. In fact, they were so tasty, one would often find oneself going back to take another bite or two! Delicious and cognitively nutritious!

I believe the Data Science Methodology is crucial to leveraging the advantages of Big Data, Artificial Intelligence, and Automation as we driver ever headlong into "The Age of Cognivity!" Not a lecture, just an observation!

I think the methodology could be more detailed, this was only the surface - basically methodology 101. We could've learned more during the Lab assignments, but we don't understand Python yet, so it wasn't that useful, I think. Completing the final assessment was not easy either, because we needed to come up with an own idea and problem. That would work if we had a deeper understanding of the methodology. Otherwise I suggest to give a predefined case study and problem to work on.

The course was interesting. It was a bit technical as there were some python applications in the lab works and since i had no prior knowledge of python, i was unable to understand the function of most of the codes. Also most of the terms were quite technical and not properly explained in the case study. This made the case study a bit difficult to follow. I suggest that a simpler example be used that can be easily followed even though one does not know many terminologies.

A very informative course on the Data Science methodology that should be used. I learnt a lot from this course and am confident to say that I want a clear understanding of what a person on the Data Science team in a given company must focus on, how one should go about to provide answers or useful insights for the stakeholder's concerns. However, I found the course work to be a little unstructured and confusing to follow at times. Overall, a very informative course.