The course is very interesting and meaningful, data visualization is one of the most important aspects of storytelling. In the course, I was able to better understand how to develop dashboards using Python (instead of using commercial solutions such as Power BI, etc).

I believe the dashboard's deployment outside the Jupyter notebooks environment was not covered and is fundamental (make the dashboard embedded in HTML for example).

I understand that this module is much more technical and difficult (IBM Data Science Professional Certificate). However, I have a critic regarding the course's flow. The concepts could be presented in a more smooth approach, even if less material was presented, focusing on qualitative aspects and capabilities.

This course was easier to follow than many of the others, partly because of much repetition, which is an essential yet often overlooked element of effective teaching. This is also one of the only courses where the instructor introduced themself in the video, which I really appreciated. If I was grading only based on the lessons and labs, I would give it 5 stars. However, the final project involved a lot of code that wasn't covered in any of the lessons. I know it wasn't just information I missed based on the countless questions in the forums. Thankfully, with the help of the other students, I was able to understand the concepts necessary to complete the project, but that's no excuse for not including the information within the course.

This really isn't a class, it's a lab, and that would be fine, but we have to watch a few one-two minute videos that should not exist - they are meaningless and waste of time and just end up saying - make sure to do the lap. Delete the short videos and just say - do the lab. The content of the class is very simple, which is fine, and this is one of the classes that doesn't create a very difficult exercise as a test (yea!). Although I will say for me, it took me hours to figure out the box plot, the little no-line nuances, etc. I don't know if was easy and I just could not find the right commands and parameters. All in all not a bad class - because WOWOWOWOIEE - I had no idea making stunning maps was so easy.

The subject matter of this class was very enjoyable. However, the level of presentation of the material was not in depth enough . As a person who is not from a computer science background, this class was extremely challenging; not because it was too difficult per se, but because I was not given the tools needed to be able to confidently complete the Final Assignment. It took many days of researching, watching several videos outside of the Coursera platform, and a lot of trial and error, to be able to complete the course. In addition, the labs had trouble loading (not Coursera's fault, as it was through another site) quite a lot, which hindered my progression.

Pro:

This course significantly improved my visualization skills in Python.

Contra:

The videos are too short ( 4 min each)

Course is unbalanced: week 1-4 are very easy and much faster to complete than 4 weeks. Week 5 (graded exercise) took longer than weeks 1-4 combined.

Information for completing week 5 were not given in thecourse material.

Conclusion:

I learned a lot because I did a much reading on information required for the exercises but not provided by the instructors.

=> The course could be much more efficient.

This course is not suitable for people not willing to spend much time on literature research.

All others will enjoy the course.

Disappointed with this module. The Labs would not execute and had issues. Throughout the course there is a request to advise of errors (including spelling errors) or problems in the modules or content. I don't understand how entire Labs cannot execute, due to the starting cells not running. I logged a few issues (that other participants encountered too, backing up my issues) and had responses after a few days saying there were "fixes", but you had to run x/y code ..... This still proved difficult. I think the fundamentals definitely need addressing (modules/labs that run). The videos (teaching) are very good however. Thank you.

The course material is good, but the notions in the exercises are sometimes just shown and not explained in enough depth. The exercises during the course are way easier than the final exam, that I found too difficult for the content of the course. Also, the difficulty of this exam is not comparable to the other exams in the same certificate (I am following the professional certificate in data science), that have been much easier and much better aligned with the content of the course material. I would cut on the material of the course and keep it simpler, plus simplifying the exam to actually reflect what has been taught.

This first part of this course was good. Week 4 and Week 5, especially the labs and assignments were more confusing than helpful. The Dash related labs seemed to have been added to this class from another course and used a platform and layout that was new. I needed more help understanding how the lab/IDE environment worked. After completing the labs, I have no idea how to actually use Dash to make a web-based application outside the lab environment. Additionally, the Dash labs assumed a lot of html knowledge and in the end they just tested my ability to understand the assignment and cut and paste effectively.

The final project/assignment was very problematic with the Theia software. The skelton code provided had some glitches. It was frustrating because I understood the concepts, but I had to debug the provided code then figure out the Theia interface. Hopefully, it will help the next class if they are instructed ahead of time not to use Edge/Explorer, Firefox seems to work best with Theia. Also, save your code in a Wordpad or MS Word doc once you think you have it correct, that way if you get knocked off Theia, you can just paste it back in without restarting from Step 1.

The final assignment is good as it pushes us to solve the problems with small help. I think that could be said explicitly to use skill labs in the start, can be hard for some people to understand what to use to execute the tasks. Also as we do not have the notebook link some pictures are too small to understand the answers.

Other thing is the repetition on all the videos about the dataset preparation, it can be showed only on first video and use the time to explain better some concepts.

I think the course is good and has a lot room for improvement.

Explanations were clear and gave a good basic start to doing data visualization with Python, but the final assignment required searching on the Internet in order to accomplish the tasks; i.e. it is not possible to complete the final assignment using only information found in the course. You can take it 2 ways: that this is actually realistic for the real world (since there will always be problems you can't solve with what you already know), or that they didn't give a solid enough foundation so people actually know what to do with what they learnt.

I would recommend that there should be more contents in the lecture videos and the lab sessions. It would be good to have more practical tutoring on the code. for example, in the lab it only mentioned how to do annotation on an ungrouped bar chart, but the assignment requires to annotate on a group bar chart, which is hard when i just followed the lab steps, and i ended up doing hours of searching, alghough it's a helpful process. So it will be good if the course can add more details on different methods of using the libraries that were covered.

The course videos were good, the labs seemed great, and then the final project hit. WHAM! It was way harder than the course materials and had many requirements that were not in the course material. One of the biggest things I learned was how to find my answers elsewhere! For completing the project, Google and the discussion board were more helpful than the course material. You should either add content to the labs and videos or adjust the final project (at least add hints to the assignment)... or you will continue to create frustrated students.

Week 1 and 2 are OK, but the week 3 videos are completely useless. Basically, each one says "there's a package that does X" but doesn't tell you how to use the package. Then, the quiz questions are about the syntax for using the package. The explanations in the labs are minimal, which would be OK if there had been more info in the videos. Unlike previous courses, there is not a notebook template for the final assignment, so you'll be doing it all from scratch; plan to spend a lot more time than the "average of 1 hour 16 minutes".

This course felt less well organized and structured as compared to the other courses in the IBM Data Science track. The videos were sparse on detail, and while the labs did have a lot of good information, they were missing crucial material that was necessary for the final assignment. The final assignment also didn't include a Jupyter notebook template / starter code, which combined with the missing information from the labs made the assignment much more frustrating than those for the other courses in this series.

Almost good. But not much explanation given, quick brief on basic functionality. Most of the videos are 3-4 minutes long, where 30 seconds is logo + ending and additionally one minute in almost every video - explanation of the data. In almost every video. So, total explanation of particular functionality is close to 1:30 to 2 minutes. Plus, lecturer is soooooo bored with what he is explaining, that you want to go to sleep in 5 minutes. Final assignment was quite good. That is why it's 3 stars instead of 2.

The course staff was terrible. They don't answer me what I am asking about. They have a lot of good materials, but lack of insructions and explanations. Also, I think they need to update Dash instruction such as creating a new files. I saw many people asking same questions and lab staff had to spend their time to answer these questions that they shouldn't have to. Thus they lack their time to spend more for advanced questions and their answer, for many times, didn't make any sense.

I was glad to learn the tools and techniques taught in this class, but the typos and grammatical errors throughout the curriculum caused confusion and distracted from the learning process. Some of the videos are helpful, but others present concepts without context and seem to be aimed at an audience that has already mastered the material. Overall, I think the coursework was appropriately challenging and the final project gives you good hands-on experience to build on in the future.

The labs were very informative, but the videos didn't add much of anything to my knowledge. The final assignment was incredibly difficult, and the course was all but useless for completing it. Almost everything for the final assignment had to be looked up elsewhere. When a final assignment tests over material never covered in the course, what purpose does it serve? There are many other reviews that have the same complaint. Something needs to be done to rectify this problem.

That`s a good course. I realised the Instructor efforts and his great skill and capabillity wich Python visualization. The final assignment pointed to activities that couldn't be deployed in another (or resident) Jupyter notebook, just only in an IBM cloud notebook.I expensed too much time trying to discover it. Some instructions should be better explained during the course. This is an important subject to be dealing in just tree weeks. Thank you.

The part of using the artist layer is a little ambiguous. Now after I finished the course I don't feel that I know clearly the difference between using the artist layer or using the scripting layer. In both cases we use plot function of a dataframe.

I think dedicating a week or more to discuss the actual functions and the way of using the matplotlib library may be better than previewing more visualization options like waffle chart and word cloud.

The course material itself is interesting, and the design, e.g. course video, quiz, hands on labs has the potential to be a good framework for learning. The execution is incredibly poor, with labs that are frequently vague about requirements, poorly written and constructed, and on more than one occasion have the answer to a question already in place. I know this is relatively introductory level but the lack of proofing is pretty problematic.

Good course overall, but I found the learning pace quite odd. The first part, on more basic visualization techniques, feels very slow, but when it comes to more advanced stuff and dashboards it feels rushed. I feel like this course could have spent more time explaining dashboards, I'm not sure I would feel very confident to implement a dashboard myself. The exams were very copy/paste so you don't get to do a lot by yourself.

The scope of this course is very praise worthy. But knowledge delivery, keeping in mind that even beginners(with no to low coding knwledge) are taking, seems very short sighted, ill formulated & pre formulated coding in the notebook files are having a lot of errors! The same applies for some other courses also. Its a request please share sufficient knowledge through video modules, they describe the content the best.

Sorry to say but this course is actually worse than the others in have learned before.

I understand it may be hard to teach only the different tools for visualization such as folium, bar/pie chart. However, the speaker in this course speaks the same "WORDS", just like replacing the variable names when coding under instructions.

I did learn something in this course but just don't like the way we been given.