good

good

Good

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I have mixed feeling about this course. I think the purpose of this course (visualizing data) and the different ways of doing it is really motivating and awesome, specially when you realize all the things you can do (types of charts , maps etc...). This is actually awesome!

However, on the down sides:

-Each video repeats the steps on how the database used in each course has been "cleaned". I agree with the feedback from other people, reminding us one or two times is fine, but in each video... This is too much!

-I would have liked more practical exercises, specially to plot multiple linear regression models (and polynomial of different degrees, in particular), to display on a chart, and to make predictions. That would be great !

-Labs: they are of unequal difficulty: some are relatively easy to complete, some require more thinking/research and time, while some have no question at all or very little. Maybe it would be useful to re-organize the labs ?...

-Week 3: as everyone mentions, the "artist layer" method is only briefly covered in one of the lab. It would have be really useful to spend more time on it, and on all the things we do with it. Like others, I spent lot of time searching online, and it took me a full afternoon to complete that part of the final assignment !

To summarize: it's a very important and interesting course, but video lessons should be re-recorded with deleting all parts repeating the initial database processing, and adding more topics such as artist layers, etc. Also, maybe split each lab in 2 since there are few labs in this course, but if we follow them correctly, it requires quite few hours to spend on each lab (at least for "beginners" like us starting learning about this topic).

Thank You !

The rating of 3 is because there are some excellent points to this course and some issues. First, no doubting the Instructor knows his stuff and he has a good style, but for EVERY lesson to repeatedly go over the details of the data set used (and you can tell this is one clip pasted in every lesson) is mind numbing. Cover the data set once and then simply say "We will use our Canadian Immigration Data set, refer back to it if you have question" . Then use this time to go into a bit more detail on the graph mechanics. Secondly, there is no lab environment for the final assignment (as was provided in precious courses of the Data Science module). This overly complicates the assingment beyond the material being tested (I was bangin my head as to why I could not get a graph working until I realized it was the lack of an environmnet variable, not my code, that was causing the issue.

The course itself was good.

Unfortunatly it was not clear at the beginning, that the "Data Analysis Module" is a pre-requisite. After struggling with the lab of week 3, I found out and took the Data Analysis Module. I tried the lab again - meanwhile the first part has changed - the file was not in the same structure. So, the code I wrote before was worthless. Took a while to figure this out.

Then in CC Lab the "conda install" did not run - neither in the lesson, nor in the lab - therefore I spent many hours struggling to find this out - didn't know, if it was my coding.

It would be good, to improve those "organisational problems". That's why I only gave 3 of 5 stars. It did cost me a lot of time.

The content and lessons and exercises and the lab itself is very good and interesting. Also the amount and speed, very good to handle besides a full-time job (if everything works ;-) ).

The course is very informative with step by step explanations. However, there are too little teaching staff to answer all the students questions. As well, throughout the lab quite a few things were unclear (i.e. a certain map is not available for free users, a certain tiles doesn't work with maps, something must be downloaded/imported despite saying it must not). These things could have been mentioned in the lab instead of having to look through many students questions on the same issue up to two years ago. The importing/downloading parts of the code were also very slow on the notebook and it often had to restart often due to this. The final assignment discussion page often crashed and froze too but all the other discussion pages worked very well (no crashing or freezing, fast loading times).

The notebooks don't mention that Mapbox Bright isn't available for free anymore. This results in one of the map exercises in Week 3 to not have a map populate. Only after hammering away at the code and reviewing the forums did I learn that this map type isn't free anymore. There's also a section in the final where we are asked to populate labels on a bar chart. This wasn't covered at all in the material. Only after reading the forums and being linked to some obscure blog post was I able to figure this out.

Also critical items not converted the material: why when load a csv file from a URL do you sometimes need to add a .csv extension to the string and other times not (such as in the final map). Why does a .geojson file need a .json file extension when being fetched from a URL?

This course is generally helpful and provides some good examples and directions towards resources. It is a bit repetitive at times and moves quickly through a lot of details and methods. It really is a question of how much extra effort do you want to put in outside and after you are finished the course though. If you are new to python I imagine this course would involve a bit of challenge - but that's fine - you are never going to know the way to do something all of the time and part of the skill you need is understanding the code, but also, how to search for answers when you are stuck. One thing that really needs to change with this entire certificate program is the screen capping solutions and uploading them into the webform for the final assignment.

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.