This course was really well designed. I've taken the preceding courses and I really connected with the format of this course. I liked how the labs really explored different options and played around with the code in a variety of ways to show a more complete picture of what the code instances can do. I especially liked how sometimes the labs would purposely use incorrect code which users might enter, and then explain why that code didn't apply or work for given scenarios.

I liked how the videos (specifically regarding Canada dataset) would repeat the cleansing of the data and introduction of the data for each type of plot because it really reinforced the concept, however it could have been better if the corresponding code were displayed alongside its effects instead of just showing its effects because then it would drive home the code and the concept instead of just the concept.

I also liked the fact that the final project asked students to make connections beyond the individual class scope, as a way of teaching that mimics real-world projects and learning.

This course started out good but quickly went downhill. The first two weeks focused on matplotlib, and while I did learn something useful, I did not need a dataset recap on every single video. its a waste of time.

Week three is where things quickly go off the rail. Waffle charts were introduced, and we were told that there is no quick way to do a waffle chart in python. That's fine, I'll learn how to do it the old school way. That was time consuming but time well spent right? WRONG. When you take the module quiz you are asked if pyWaffle is the best way to create a waffle chart. You answer no because you actually watched the videos and did the lab training, but you get a wrong answer. You check online and what do you know, you can actually do a waffle chart with 2 lines of code. IBM knew this (hense the question in the quiz) but nobody bothered to update the lab or the videos

The video on word clouds introduces the concept and you were supposed to learn in in the lab, but then you get to the lab and you are told to skip this section. I have no words for this

The part on dashboards takes the cakes. IBM basically provides you with external links and leaves it at that, there isnt even an attempt to teach you these concepts. And you know what the best part is? the course project requires you to build a dashboard. Yep, the thing they didnt even attempt to teach you.

Anyway this review is too long and I doubt anyone reads it, but hey, if you do, just be prepared to read a ton of external resources because you are not getting what you are promised

While I enjoyed the content of this course, I feel that the instruction was disorganized. This course is part of a beginner sequence in data science, but the teacher assumes certain advanced skills are already known and does not teach them. For example, chart annotation is only briefly covered in the second lab, but the final assignment requires a depth of knowledge not taught in this course. If that's the case then chart annotation should be taught as its own section. A lot of the quizzes are written to trick you with ambiguous phrases, rarely do they actually test what is learned in the labs. I think the teaching in the other IBM data science courses is far better than this one, hopefully they improve this one.

In my opinion, it's a terrible course. The labs seem to belong to an advanced course, whereas the videos are elementary and for absolute beginners. The labs take several hours and and even days to complete and understand, whereas it's claimed only an hour is required. None of the links/path at the final assignment worked! zero to none customer support, which is an absolute shame. The final assignments are way beyond the scope of a beginner course. Waste of money!!!

The course was shallow in content. I wish it explored pros/cons of advanced visualization techniques & how to go about implementing them in python with several real-world examples. The videos were extremely short & repetitive explaining the same dataset in each video. Bulk of the learning happens in optional labs & peer reviewed final assignment. Most of the insights are easily available online.

Like other reviews I was really looking forward to this course in the IBM syllabus but was very disappointed. Videos were very high level and repetitive - and there were a lot of them. The detail in the exercises was better but there were significant challenges in getting them to work. And then the final assignment was a bit of a joke. The visualisations you were expected to produce had not been taught in the course and as you'll see from the forums requires a lot of work arounds. I would suggest this course needs a bottom up re-write. Do less but cover it better!

There's a lot of good material, and ultimately enough to integrate into successive work in data science.

On the frustrating side, most of the explanations focused on "what" rather than "why", and there's so many "why" left unanswered when choosing to address a problem with these tools.

Additionally, most of the questions were either insultingly easy or incredibly difficult. Plenty of googling in addition to reviewing presented material.

I found this module of the Data Science Professional Certificate to be quite disappointing.

The videos were too short, repetitive (same data processing in every video) and lacked the depth of explanation required to get a good grasp on the material. Everything felt rushed and superficial.

There was use of more advanced Python code without a gradual preparation towards tackling more difficult concepts and syntax. For example, building a 'waffle chart' from scratch in Python seemed a bit out of the scope of this course. Since there are more than 40 charts available in Matplotlib, what is the point of using an entire Lab session going over the complex code required (at least in my humble opinion) to build a very specific type of graph. I found it would have been more useful to explain the process of selecting the right graph for the right type of data and how to highlight the insight we want to draw on the data.

On top of that, all of a sudden the course shifted into building web applications with Dash. Again little to no explanation or preparation about the code being used, just 'copy and paste' these lines of code and there you have it.

Overall, my understanding is that the libraries addressed in this module are not quite complex and required quite some time to be explained properly. The suggested time required to complete each section seemed quite unrealistic given the breadth and depth of skills/understanding needed to use these Python libraries successfully.

I appreciate that the videos were done in a human's voice and not a robot. It helps me focus to hear the natural pace and emphasis on certain points. Also, the labs were very clear (thank you). There was clear guidance/notes through steps which is very helpful because this is a new thing for me. The final was also fair and comprehensive. I have a long way to go but this class was very well done.

Great course, one of the best course to get hands-on learning for Data Visualization with Python. Particularly the lap exercise, it will make you think on every line of code you write. Excellent!!!

It's a really great course with proper hands on time and the assignments are great too. i got enough opportunity to explore the things which were taught in the course. Really Satisfied. Thanks :)

Bottom Line Up Front: You are going to have to teach yourself using Google for the Final Project

This course was unique in the Data Science Professional Certificate Progression in that the content of the videos was not very helpful, making your rely on the labs to teach yourself.

The labs were not great in walking you through all the parameters that were available for each type of plot, and while you can copy and paste solutions and tinker to learn things, the course didn't leave me with a good understanding of how or why things work and thus, unable to apply solutions creatively and appropriately without much effort.

By far my least favorite course of the 7 IBM courses I have completed so far.

Final assignment was frustrating, its was difficult, It took more than a month to get my assignment reviewed. At the beginning i waited for a week, I did not get any peers to review, then soon after a week when i logged in, my assignment was gone and 4th week videos and new assignment were reset. I completed again all modules and new assignment finally and again waited for a week to get it done. I've spent more time in this course in the entire Data Science Professional certificate, I don't know whether this is a problem from the creator or coursera itself.

The videos were unfortunately pretty useless. At least half of the time the respective dataframe was processed (but always in exactly the same manner... zero information about the actual new concept). In addition the videos were too short and the really important new concepts were only introduced by quickly showing the code snippets. There was often no explanation of key concepts. Unfortunately, overall one of the weaker courses on Coursera. The Data Analysis Python course is much better and explains similar concepts.

This course did not went into the depth and breadth as per expectation after following previous course. In my personal opinion as PhD myself with history of teaching for 4 years, the content is not enough to make a separate course. This course can be included as part of data analysis course itself as separate week worth of lesson.

While the data visualization tools outlined here are valuable, the final assignment for this course does a very poor job of assessing the things that were actually taught.

Early on, you are assured that the course will emphasize the scripting layer of matplotlib, rather than the more complex artist layer. In the final assignment, however, you are instructed to use the artist layer, and configure many parameters that were never covered in any of the labs or videos.

Likewise, utilizing geojson files for choropleth maps was covered in the most cursory manner - you are never requried to open a geojson file, or understand how it is configured. You just load it in and carry on with the assignment. However, without understanding the setup of a geojson file, no student is going to understand how to properly configure the key_on parameter when making their own choropleth map for the final assignment.

The ability to actually create a graph from a set of data is perhaps the smallest part of the final assessment. Everything else is fussing with irritating details, often details that we were never taught about in class.

If I could give this course zero star, I would! Please IBM and Coursera, fix this course! It's the worst course so far in the specialization. The videos are poorly made. The labs are full of bugs. I had no prior experience in data visualization and it was extremely hard for me to follow the labs and assignments. This course it's not for everybody, but for people with an acceptable knowledge of Python and HTML. I wish all the courses in this specialization were like the SQL course. That course was beautiful. You should check that.

This was the worst course in the specialization.. It was hard to follow, there weren't many lessons, and it wasn't worth the cost.

One should take another course on coursera to pass the final assessment since the tasks are not covered in the course.

Total emarrassment for IBM

This was a valuable course to learn visualization with Python. I especially appreciate the section on mapping and the waffle charts. The only suggestion I would offer is to have more opportunities for practice coding, especially for grouping data for analysis, labeling charts, calculating percentages. These could either be optional assignments or links to other resources. But regardless, this is the best course out of the series for me so far.

This course is really good. The instructor did a great job introducing common graphs, charts and map techniques. What they look like. How, where and when to use them.

The lab is time-intensive which give chance to thoroughly practice the technique. One more plus point is the lab uses real data and guide you through the step of retrieving, cleaning, analyzing, visualizing and mapping.

Definitely recommend.

The course with the IBM Lab is a very good way to learn and practice. The tools we've learned in this module can supply a good material to enrich all data work that need to be presented in a nice way.

This course gives very well knowledge about different types of visualization techniques and helps to start with visualization. Coursera provided an amazing course with an amazing instructor.

Great course, extremely thorough! Interesting assignments. It is more than obvious that the author has put time and effort into it. Thanks for that :)