Could have been better

好多东西根本没说啊，然后就要做作业，一脸懵逼

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Need more hand notes

carelessly prepared

I found it boring

Can be better

course is ok

too basic

Too basic

Pros: The labs were pretty good, particularly the early ones that used the Jupyter notebook, and those were the main way that I learned from this course. Cons: The video lectures were not at all engaging and didn't seem very necessary, and I just skimmed the transcript or skipped them entirely. Often there felt like a lack of alignment between the videos and labs and between the different modules, as if different people had put each of them together and hadn't coordinated well with each other. The later labs, on Dash, were not as good as the earlier ones - it's a cool system, but perhaps it was just too complex for this course - I felt like all I did was fill in a few blanks and didn't really understand it. But the biggest issue is that the assessments were poor. The multiple choice assessments at the end of each module were pretty useless - most questions were trivial, a few obscure, but almost none effectively tested either programming skills or conceptual understanding. The final project for this particular course was also not very good. It involved a large number of poorly defined tasks, supplemented with copious hints that did almost all the work for you, supplying you with almost complete that only required you to fill in a few words. To have actually figured out the tasks by myself would have been very laborious, if not impossible due to the lack of specificity in the instructions. But using the supplied code transformed it into merely an exercise in careful copying and pasting, and I don't feel like I learned that much.

There is a section of the videos about 1.5 minutes long where you have to listen to "Now lets process the data frame so the country name becomes the index of each row. This should make retrieving rows pertaining to specific countries a lot easier. Also lets add an extra column that represents the cumulative sum of annual immigration, from each country, from 1980 to 2013. So for Afghanistan for example it is 58639 total, and for Albania it is 15699 and so on. And lets name our data frame DF_Canada".....

This replays in each of the what 10-12 videos or so... It adds no value whatsoever because its just saying that same thing without actually showing how we accomplished that. So its a loop about half the size of each video with non-pertinent information. Fast forward to week 4 and we suddenly jump into Dash. I found Dash to be very interesting, but the learning curve was steep since we didnt really discuss Dash in any of the videos. You are learning it purely off the workshops which uses an IBE that we do not use in any of the other courses in the Data Analyst Certification series.... I would recomend adding value content to the videos and not relying as heavily on the self directed labs to do the training for this course.

This course needs some significant remodeling in order for users to feel like they learned something from this course. I couldn't finish the final assignment because visually speaking, it was so hard to follow. Furthermore, the final assignment was creating a dashboard, which covered Week 4. There was no graded assignment that covered Weeks 1-3. Luckily, that material was easy to follow. Not sure what the point of having the material for Weeks 1-3 is if we are not going to be tested on this material via a final assignment.

To make this course more worthwhile, I think there should be a graded peer review assignment for Weeks 1-3, so learners have a chance to test their knowledge on this material. Then have an assignment that addresses dashboards. I also think it would be easier the dashboard assignment in IBM Watson Studio.

I spent more time trying to fix "localhost refused to connect" and other errors from the end of course management, while submitting my final assignment, than on doing the actual course or assignment - it's not a very efficient way to test a newbie like me who isn't familiar with these various environments.

Also, when there's no instructor speaking to you and it's just robotic voice and text, it doesn't feel much like an educational course and is off-putting. The lectures were also way too short and flew through concepts too quickly. The only mitigating factor was the labs which were decent and the only thing that actually aided my learning rather than being a hindrance. I hope you don't take this the wrong way, but I'm unsubscribing from this course. Thanks anyway.

This course touches some very interesting topics and tries to cover many aspects of Data Visualization. That said the videos do not explain anything in depth, most of them are just 3 minutes presentations of the same code that will be explained in the labs wasting another 2 minutes just to do THE SAME dataset formatting IN EACH VIDEO! The labs are interesting however you have to read and understand a lot ON YOUR OWN, most parameters are not explained (of course you can just copy paste the examples to pass the labs) and if you really want to learn you have to study most of the material on the websites and tutorials of the various tools that are used in the course e.g. matplotlib, Plotly, Dash etc. So I don't really understand what you need this course for.

El curso esta descripto como nivel intermedio, sin embargo, las primeras dos semanas se tratan plots basicos (ej. boxplot, pie chart, scatter, etc) pero no se profundiza muchos sobre las opciones de parametros a ajustar ni como mejorar la parte estetica, y la practica solo da un pantallasmo muy general. Yo en mi caso buscaba conocimientos algo mas avanzados sobre estos graficos.

La seccion de dash, si bien yo no la conocia y estuvo interesante, fue poco clara la explicacion, una practica completa pero como que me falto mas base y explicacion de los diferentes modulos para poder llevarla a cabo. Al final no me quedo muy claro como se deberia armar el archivo para dashnoard ni que rol cumple cada parte.

I would give 2 stars for the materials taught prior to 5th week's final project. It goes downhill from there. The final project is atrocious. The assignment does not run in the suggested platform (e.g., Jupyter notebook, Watson, or Skills Network - the latter has been malfunctioning for couple of weeks now). I had to run the project in Google Colab to get the output. Most of the students are facing the same problem. Ans yet, neither the instructors nor IBM staff provide any workable solution. Furthermore, the instructions given does not match with the output generated in the lab (e.g., only one upload 'space' for uploading multiple plots). Terrible experience for most participants.

This was a horrible course culminating in the two assignments. The coding experience was mostly debugging as the skeleton code has problems on its own. The assignments for graphs (part 1 and dash) were not clear for some of the graphs. And people must have been confused like hell as the peer review of my code did not bring me 100 percent of the points, though it was quite stable (unlike the skeleton). And worst of all, I spent most of the time debugging code in an unfriendly environment--right, even if this was the intention, the intention was bad. Never again. The positive part is of course the cheatsheets. But, frankly, this was the worst module I've done with IBM.

Overall, the course has been interesting and pretty useful to learn the basics of Data Visualization and how to implement interesting graphs in Python. Unfortunately, the final assignment is a complete mess and a waste of time. The tasks themselves took about half an hour to complete, but the provided code is full of typos and small errors that required days of troubleshooting, only to figure out that my code wasn't working because of a couple of misplaced spaces in a code that was meant to be only copied and pasted. In addition, a basic understanding of HTML is required to complete the final weeks of the course.

The topic was interesting and visual feedback is gratifying.

The instructor videos were almost totally worthless. The first video should explain how the course will be conducted (information presentation, additional reading required, overall approach to homework, etc.) with subsequent videos explaining important concepts.

The included sample code files at least presented a progression to learn some concepts and provide a basis for experimentation.

The final homework required extensive internet searches rather defeating the point of a "lecture course".

By far the worst Coursera course I have taken.

The instructions and rubrics for the final assignment part 2 are unclear. It would be beneficial if the instructors could explicitly specify whether they want students to take screenshots of the code or the graphs. The phrase 'take a screenshot representing' can be interpreted differently by individuals, leading to varying submissions. Consequently, I received different grades for identical submissions and a zero for accurate code. Resubmitting solely due to a lack of standardized grading requirements is both time-consuming and energy-draining.

I think the materials are good, and give a very broad overview of what visualization packages are available.

However, having the assignment based on Plotly Dash is very painful as it is not a straightforward setup, and some individuals might face challenges even executing the codes.

I would suggest sticking with matplotlib, as I presume the main objective is to know how to storytell, and not on how fanciful your storytelling can be. We can probably leave that in other courses like Tableau, D3.js or a focused visualisation course.

One of the main packages used has been deprecated. Final was confusing to many. The decision to use a different tool to run the python code should be changed to use the same software that is typical to the program. Should consider not focusing on the dashboarding elements of python. The final would have been better if it had been in the typical environment either through IBM or coursera and required just submissions of code and visualizations from a jupyter notebook. There were also technical difficulties with the final project.