Just Awful. Test is nothing like the practice modules. Very poorly done.

This course provides lectures that enable learners to understand the theory, application and practices that data scientists use to create meaningful visual presentations of complex data relationships. The labs provide adequate opportunities to do hands-on end-to-end work with data and visualization tools. The learner is challenged to go beyond the scope of information presented in the course to also search other resources to gain the knowledge necessary to complete the final project. Searching for additional resources builds a foundation for independent future work.

Informative and concise intro to Data Vis in Python. I enjoyed the Plotly and dash interactive graphics and additional resources.

It took me longer than th recommended time though as I am new to Pandas and data vis in python.

Learnt a lot from this visualization course. The one I found most interesting was making the dashboard. Although sometime the code and indentation are tedious, but this might be useful in the future.

The final project was somewhat more challenging due to some file downloading issues. But I was able to get some help in the forums for that, which helped me accomplish my goals.

I learned and understood how to make graphics based on a previously clean and standardized data source. I liked this section.

Really good course with easy to understand materials and wide varity of visualization techniques and tools.

The assessment was really complex, but the course overall is really usefull!!

Amazing course!!!! I liked your very detailed and well-organized notebooks <3

Good course. I like the way it was delivered.

The course was overall, pretty good. Although it was extremely repetitive with regard to 'cleaning' the data, the information covered was explained and shown pretty well. The lab sessions were detailed. I would have liked to see more of the possible implementations as opposed to manipulation of the aesthetic. I also hoped they would cover seaborn in more detail.

Although most people are against the final assignment, I actually enjoyed it as the previous courses gave us a jupyter notebook with most of the work already done, only letting us write the main part of the code. Coding from scratch with just the dataset helped me understand the topic better and will definitely make it easier the next time I attempt data visualization.

The course is very good. Intuitive and easy to follow. The real challenge is in the peer review exercises, where your patience is tested. You really have to work hard to get all the solutions to the questions. There are so many things that the course just can't teach you in the time constraints.

A great course for you to further understand the mechanics of data visualisation as well as providing a space for you to familiarise and test your understanding on the subject matter.

this course should come before data analysis with python

Although I understand that learning doesn't take place at only one place, this course seemed very weak in terms of providing enough examples necessary to solve the problems in the final assignment.

All videos had a same part that was repeated, and no information was agregated by this repetition.

The contents of the labs are quite good, but a more detailed explanation could exist.

Some updates are needed: one of the labs uses MapBoxBright, which gives us a clean figure with no map because this is not available anymore.

The final assignment required us to look for solutions that were not present in the course, and in my opinion, they should be. The student should go to outside sources when it feels a need to understand something deeply or if the way presented by the instructors was not the best for the student to understand what's going on.

There's a lot of room for improvement: the videos should not be repetitive; the contents should be updated, anything that is required in the assignment should be presented throughout the course, if it's not in the labs it should at least be in the videos; the final assignment could provide a notebook with the requirements as the other courses in the specialization offer (in my case, I took it as part of Data Science Professional Certificate by IBM); if this is not the case, the student should be prompted to create a notebook with the questions and answers, which would estimulate even more the creativity around data visualization.

I hold a degree in computer sciences with majors in Software Engineering so please take this review of the course seriously.

Unfortunately, this is the only course where it seems the teacher never had any outline as to what he needs to teach and how.

1) He has made the video lectures useless as he declared himself that the videos will be short but you have to 'read' lines and lines of lectures to get a grasp of the visualizations he will teach. I think he don't know if it was that easy for a person to get knowledge then he would have just read text books and would have gotten the degree as according to him there wouldn't be any need to educational institutions.

2) Many times, he introduces many 'advanced' functions of Python which was not taught in the previous course which was about Data Analysis by Python. I don't have any problem in learning new things everyday but using multiple advanced functions in a 'beginner' course makes it tough for student to grasp what he was trying to teach.

3) There are far better and easier ways to do many things but it seems he deliberately uses long, tedious and advances methods for plotting various graphs and makes things confusing again and again.

4) Lastly, he himself gives advanced quizzes for the stuff which were not even taught extensively and it makes hard to even pass them.

Die Inhalte des Kurses stellen knapp einzelne relevante Phythonbibliotheken zur Datenvisualisierung vor und führen gelungen in diese ein. Leider ist das Final Asignment eine didaktische Katastrophe. Bei mir war das vorbereitet Dashboard an mehreren Stellen fehlerhaft, so dass die im Asignment angeforderten Screenshots nur dann erstellt werden konnten, wenn man neben den gestellten Aufgaben auch die Fehler behbt.

Da wäre zum Einen ein bereits im Forum diskutiertet Fehler der auf dem Parsing eines strings -> int beruht. Zum anderen wurde die colour : 'Flights' nicht als Property des ugehörigen Feldes erkannt. Das sind Dinge, die bereits bei der Konzeption des Asignments hätte feststellen können, wenn man die Aufgabe einmal selbst durchspielt.

Zum Anderen standen die Anzahl der angeforderten Scrennshots dieses Assignments nur in geringem Zusammenhang mit der des Absolventen erbrachten Leistung innerhalb der Aufgabe. Ich habe mehr Zeit damit verbraht sie Screenshots anzufertigen, als dass ich für das eigentliche Assignment benötigt habe. In anderen Assignments habe ich erlegt, dass man einfach den "Code" der Aufgabe reviewen und dabei diesen mit den Ergebnissen abgleichen sollte. Ein solche Konzept würde hier sicher auch den Aufwand etwas entschlacken

This course is overloaded and doesn't teach well.

After having tons of examples you will end up with tasks where you have solution available in hint or even as part of your code skeleton. I wonder how people fail to pass the task reviews. Or they just can't read?

Questions in quizes and exams are like "who is the author of the library?" Does it really matter in such a course?

Some other questions are checking that you have heard a couple of words properly during this 5-week course. Don't you want to ask about the visuals? What about the code in Python?

Wording in tasks is often a disaster. E. g. a pie chart is based on a total advertisement expenditures but it is called "Total Sales". Who are you trying to cheat, my dear data scientist?

Some tasks are even missed in task statement, but you will be asked for their results later, with no idea what to answer.

What is never missed during the course, though, is that you must assign labels and titles to your plots. No matter how accurate is your plot, what was the task and even if you don't have the task, they will remind you on every page that you must set the labels. Looks like it is the most important thing in the whole course and the lesson of life!

It is a good course, however, it seems that it was designed not to teach very well the Dash module. Very few explanations are given about the whole subject and it is strange that the final assignment is based on this when we had many other things to work with that were better explained and detailed.

There are several errors in the given code in the final assignment and, assuming that everyone here is a junior and/or has their first contact with programming language through this course, it should have been tested better. Not all people have the expertise to turn around the issues when trying to run the application. I'm lucky because everyone in my house is familiar with programming language so I decided to get help from here, instead of sending messages, print screens, and whatsoever to have an answer just one day later.

Furthermore, please, stop with the print screens' way of evaluating. It is time-consuming. It would be better if there was a way to just send a link to showcase our work.

Regards,

Duarte.

Hi,

In the final assignment, I had to explore the internet to get some codes to display the bar graph or the map. These codes were not covered in the class. The course needs to get improved by giving the keys of how to do things . For instance, the method .patches was never covered in the course. I do not know how to use it. It may be a part of panda library, but the method was critical to do the assignment. There are many other examples. I spent almost a night to finish the assignment because I took a long time to self learn these tasks. It is good at one side, but the course should help me.

Thanks

The course content is great but the way it is being taught is not up to the mark..

the labs are good but that's not the way everyone can learn things..

Something can be done like some instructor should be there who will be teaching us about those libraries. In the videos, the instructors are just giving a brief idea about the libraries and asking us to go through the labs for better understanding.. How about giving more ideas where someone will guide us through the labs too. I hope you can understand..

On overall IBM data science professional certificate track: Pros: Content is just good enough, instructors are good. Cons: IBM watson and the platform given to practise on is awful and has terrible performance and reliability issues, most often doesnt work and had an impact on my test deliverables. I personally overcame those issues to some extent with kaggle's and google colab jupyter notebook environments.

the last week is horrible you guys are just here to rip off people with no prior experience in coding and that is really not okay 100% really really bad

week 1-3 were amazing THE BEST WEEKS IN ALL COURSE SO FAR without a doubt

week4-5 are just there to make people pay an extra month for you data visuals

shame IBM

Most of them are straight forward. However, the instructions on Final Assignment Part 1 (Task 1.6 only) & 2 (not matching to the application skeleton coding), result into frustrations and confusing for most learners. These need to be updated to future learners as soon as possible.

Some good material, but some was pretty niche and therefore less useful. The lessons were okay, but as usual the whole thing was riddled with typos and technical issues. Not good enough for a major organisation like IBM.