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Voltar para Data Visualization with Python

Comentários e feedback de alunos de Data Visualization with Python da instituição IBM

4.5
estrelas
9,693 classificações
1,449 avaliações

Sobre o curso

"A picture is worth a thousand words". We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data. One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions. The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate....

Melhores avaliações

LS
27 de Nov de 2018

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.

AM
13 de Ago de 2020

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!!!

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101 — 125 de 1,443 Avaliações para o Data Visualization with Python

por umair

11 de Abr de 2019

this course should come before data analysis with python

por Rodolpho P

29 de Set de 2020

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.

por Baher

23 de Ago de 2020

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

por BISHAL C

19 de Nov de 2021

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..

por Vimal O P

9 de Nov de 2021

On overall IBM data science professional certificate track: P​ros: 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.

por Sarah s

14 de Jan de 2019

This course was nice but there were extra stressors that weren't included in the course.

por Frank A I

21 de Mar de 2021

Unfortunately, this has to be to worst Coursera course that I have taken. I only give it 2 stars for the first few weeks, otherwise this is more of a 0/5 star

While the beginning was a descent course, the final project was very much a left field task. While it did have some of the material from the course, there were several aspects that were not explained in depth or not at all. It also didnt help that there were some errors in the base code that were not explained and it took the students to resolve them.

Many people pinged the instructors for assistance, including myself, but aside from a few comments here and there, all their responses were basically "Review this thread that says to run the code a gain and give it time to load". When I asked for more detailed instruction beyond that thread, I never heard back from the instructors. With so many students opening threads and asking the same question, the instructors should take that as a hint that something is wrong and that they should take a more active roll to resolve the issue (a new lecture or assignment to help explains the errors rather than a thread provided).

The best part was that when I mentioned I had issues with this at work, a coworker of mine who had 20 years experience, at least 10 of which is with Python, offered to look over the code with me. He was confused with what the instructor was attempting to have us learn with the final assignment.

In short, I rate this course low because of the final assignment not being properly explained before or during it and that fact that there is little to no instructor support beyond repeating themselves and telling students to "toggle the dropdowns and wait"/ "rerun the code"

por Thierry C

31 de Jul de 2021

T​his course is the most disorganized I ever followed to date on Coursera. Up to the step where we learn about the Dashboards, the course is pretty well presented and the labs are working with good guidance but then, when reaching the dashboards, the guidances disappear, none of the dashboard lab work in JupyterLab. The final exam is a torture with a peer review submission so messy that you will have to spend more time assembling screenshots together than producing the dashboards themselves, and after so much struggle, you will have to answer sneaky questions with some answer that do not even make any sense in English. Be prepared for a lot of frustration and even though, they say that knowledge of HTML language is not required... well... most of the dashboard labs are actually based on HTML language. I wish the course was better prepared and that all labs were working, I have learned less than I could due to those broken labs.

por Alasdair T

22 de Jun de 2020

Course could benefit from a refresh - for instance, support for Mapbox Bright tiles has been dropped from Folium for 1+ year, but the course still tries to demonstrate their use. There are several posts from confused students wondering why this doesn't work. Surely it'd be better to just remove/update this section of the course rather than have to deal with so many bug reports in the forum?

Also the videos for this course are extremely repetitive and barely of any relevance, e.g. 1-2 mins of several of the videos is just the same footage of the data being imported to Pandas and cleaned. Once you've seen this once, you've more or less got the point. Add to this that the Final Assignment required knowledge of matplotlib which was *not* covered in the course, and had to be researched elsewhere, and it seems obvious that the quality and relevance of the video content could be improved significantly.

por Carina D

20 de Abr de 2021

Although the course was informative, the course components related to Dash applications need to be reworked or removed. For a course that should only require the use of a course-provided cloud-based Jupyter notebook (JupyterLab via IBM Skills Network Labs), the labs and final assignment should work via that service. The final assignment also should be reworked into a new assignment because of its incompatibility with the course-provided Jupyter notebook. A final assignment should not be debugged extensively and require the use of outside applications (one of them being an application that requires computer installation) to be completed. The course should explicitly state what resources are needed for the final assignment if it requires outside resources, and the debugging instructions should be listed in the final assignment instructions, not in a thread in a discussion board.

por Ricardo S

12 de Mar de 2021

I'm disappointed. I believe data visualization is a very important skill, but this course didn't teach the most valuable skills.

The videos feel like someone is merely reading the documentation. There is a difference between showing something and teaching something, and very little was thought in this course.

In the labs, the visualizations (1) Do not tell a message (2) Are not compelling (3) Do not teach you how to generalize the idea behind the chart.

The worst part: the course creators apparently know this. Some of the labs don't even have exercises, because clearly these "classes" are not enough to teach you how to do it on your own. And the final assignment has multiple posts explaining how to fix the many oversights in it.

This has honestly impacted my opinion on IBM (is this what you offer to your clients?) and Coursera (is this the average quality of a course?)

por João R d C

4 de Fev de 2020

Out of all the courses in the IBM Data Science Professional Certificate, this was the one I had the highest expectation for and unfortunately I was a bit disappointed. The course materials are lacking in information and the final assignment asks for customisations that weren't covered in the course materials, which leads to question: are these important things to know and the materials are lacking in information ? Or are these irrelevant and should be a part of the final assignment? Because if they're just there to make sure no one gets a 100% grade, then that's just sad.

por Anoosh G

6 de Fev de 2021

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.

por James H

5 de Mai de 2020

This class could have been one of the best based on my interest, but it wasnt explained very well and I had to use outside sources to figure out what was going on in the labs and sections... Also some of the final project material wasnt covered in the class itself... It was more difficult than it needed to be... Once I used Google to find answers, the stuff I actually learned were useful...

por Mehmood S

29 de Abr de 2020

Much of what was tested, was not taught in the course. Therefore, the course requires individuals to do their own research online to answer the final assignment questions.

The purpose of paying for the course is to quickly learn fundamentals from the course, NOT to spend hours looking online for the right answers and waste time with trial and error experimentation.

por Mark S

19 de Ago de 2020

Very poor. As the course carries IBM's name, I expected a premium product, but I was disappointed. The training videos were brief and didn't go into the material in any great detail, and certainly didn't prepare the student for the lab sessions or the final assignment. I learnt more from Stack Overflow than I did from from the training videos.

por Ana C T M

21 de Jan de 2020

Links did not work for classes hands-on exercises, repetitive video explanations, and final project required content that was not explained in classes. Overall, it was a bad experience on a subject that should have received more thought and caring from instructors on lesson plans and class materials given its importance.

por Sean H

14 de Ago de 2020

Labs and lessons did not adequately prepare me for the peer review lab. A lot of information went through quickly or was hard to reread on account of the sheer volume of charts created. Minor gripe but when learning pie charts the videos mention pie charts being awful without properly explaining why they are awful.

por Jeff S

8 de Jan de 2021

Dislikes:

-Many videos repeated just over 1 minute of the exact same content reviewing the dataset.

-Videos were very brief and then exercises would be beyond concepts taught.

-lab contained thick code to prepare graphs but not explained.

Likes:

-Enjoyed creating the maps & learning about other visualisations.

por Steve K

20 de Out de 2019

Almost all videos included the same bit about getting and reframing the data. This was a significant portion of the videos as well.

There seemed to be more confusion around the final assignment judging by the amount of questions in the forum. The assignment needs to be rewritten or made more clear.

por Juan C C

23 de Mar de 2020

Content was solid, but videos mostly said "go do the labs" vs offer meaningful tips for the final an beyond. Worst of all, the tech is outdated. I spent an entire weekend working on the final assignment due to technology issues. An embarrassment for Coursera and IBM that they let this happen.

por Mo S H

13 de Fev de 2020

Better to read matplotlib and folium documentation and look for answers from stackoverflow.

This course didn't offer enough components of matplotlib and folium.

This offers lab sessions but they are not main contents.

Final assignments are impossible for someone who just takes only this course.

por Abeer S

12 de Mai de 2020

The teaching pedagogy wasn't as good as the other courses I have completed in this certificate (Professional Certificate in Data Science) so far.

Questions in the assignment were related to topics that were not discussed in the course. I had to search online and complete the assignment.

por Gabriel A

18 de Mai de 2020

This course is in need of a healthy overhaul from content to lab to final assignment. I would recommend adding a recap "week" just for data frame manipulation. As with many of the courses, the labs could use some proofreading and updating, this course skips around a lot.

por Aniruddha P

21 de Set de 2019

The course can be made much more better. The final course assignment wasn't much based on the things that were taught during the course. Example could be using the labels above the bar.

On the bright side, the contents were really good. Thanks to the instructors! :)