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Voltar para Data Visualization with Python

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

10,695 classificações

Sobre o curso

One of the most important skills of successful data scientists and data analysts is the ability to tell a compelling story by visualizing data and findings in an approachable and stimulating way. In this course you will learn many ways to effectively visualize both small and large-scale data. You will be able to take data that at first glance has little meaning and present that data in a form that conveys insights. This course will teach you to work with many Data Visualization tools and techniques. You will learn to create various types of basic and advanced graphs and charts like: Waffle Charts, Area Plots, Histograms, Bar Charts, Pie Charts, Scatter Plots, Word Clouds, Choropleth Maps, and many more! You will also create interactive dashboards that allow even those without any Data Science experience to better understand data, and make more effective and informed decisions. You will learn hands-on by completing numerous labs and a final project to practice and apply the many aspects and techniques of Data Visualization using Jupyter Notebooks and a Cloud-based IDE. You will use several data visualization libraries in Python, including Matplotlib, Seaborn, Folium, Plotly & Dash....

Melhores avaliações


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.


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 1,631 Avaliações para o Data Visualization with Python

por Kirti S

22 de abr de 2020

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

por Alejandro A

24 de abr de 2020

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

por Veronika S

21 de abr de 2020

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

por Paolo Q

15 de jan de 2023

Good course. I like the way it was delivered.

por Advaith G

16 de set de 2020

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.

por Renier S

24 de abr de 2020

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.

por Atfy I Z

21 de abr de 2020

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.

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 Farrukh N A

1 de jul de 2020

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.

por Boris B

21 de out de 2022

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

por Renan D

14 de dez de 2022

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.



por Baher

23 de ago de 2020


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.



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

9 de nov de 2021

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.

por Kevin B

19 de out de 2022

Warning for those whose native language is NOT English: These IBM Data Science courses are in DESPERATE need of review by a native English speaker. If English wasn't my first language, I can only imagine how much I would have struggled. It is pretty unbelievable that they expect people to pay money for courses that have so many many grammar, syntax, and audio transcription errors.

por Fabrizio P

5 de jan de 2023

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

por Bob D

16 de dez de 2021

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.

por Hillary P

27 de jul de 2022

The content was good, however the submissions on the final project were nightmarish. Include more uploads for the screen shots. It makes it nearly impossible to see with the way they are currently uploaded.

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 Natasha d T

31 de jan de 2022

Beware: Only do this course if you're ready to be frustrated and confused beyond your wildest expectations. Week 1, 2 and 3 starts out well enough, but in week 4 you get hit by brand new work that never gets explained to you. Firstly, the labs just 'give' you the code layout. No explanation, just: this is how it is, live with it. Secondly, they changed from Jupyter to an IDE based on THEIA. It is horrible, impossible to view your code as a whole, full of bugs and hard to navigate. Just scrolling in it was like a course on its own.

In week 5 you get the biggest shock of all: the final assignment is solely based on the work that wasn’t explained to you, in an IDE that makes you want to pull your hair out. I struggled for a day and a half with the assignment. Most of the time was spent battling with the IDE. I had to reopen it at least a dozen times (frozen, files won’t open), and that is when you learn the hard way that all your work gets wiped out when you do. So, copy and paste your work in Word or GitHub, you will need it. There is also an error in the code provided by them. I’m not sure if that was part of the assignment, but it seems very cruel.

One positive: between being given an impossible task and Coursera taking forever to answer cries for help, you become a master at troubleshooting. You have no other choice. Maybe that was the point of the course. If it was, I passed with flying colors. Unfortunately, I cannot say the same about Data Visualization with Python. Just doing work based on instruction, and never really understanding why you are doing it, is no way to build a solid foundation for future learning.

por Yohann P

4 de abr de 2022

While the lab contents are useful and I will keep the code to come back to it in the future, I find the video lectures rather superficial and the assignments completely useless. The final assignment was basically copy and paste from the instructions to the code skeleton provided by the course. There were typos in the code, and it wasn't using the latest versions of the libraries being demoed. The assignment submission form seriously needs to be reworked with clearer instructions and the correct number of upload fields to match the number of files requested. Also the assignment asked me to prove that parts of the code I got from the instructors worked. I don't see the point in doing that, apart from wasting my time. I feel that the course was designed by multiple people under time pressure and there hasn't been sufficient reviewing across the content to check that things match up. In short, I recommend reading the syllabus before enrolling into the course and picking up with the documentation of the tools included in the course. Let's be honest, no one uses matplotlib. So skip that part and move on to the other libs in the course. Each on has a very detailed documentation with lots of tutorials and huge communities you can ask questions to. Save yourself the 15 or so hours it took me to trudge through the content and get the same amount of learning done in half the time by going directly to the source.

por Gina A

15 de dez de 2021

The first few weeks of content was actually really useful for someone interested in data science, but the last 2 weeks were a bit of a trainwreck. It was clear that the assignments related to dash weren't well conceived--there were many issues, instructors were slow to address these issues in the forums in a concrete way, and honestly it felt more like a programming lesson than something practical in data visualization.

The final assignment grading also wasn't well conceived. The way the guidelines/questions were asked were very unclear, so sometimes you didn't see what you actually needed to upload until you had already taken the screenshot or submitted the assignment. This carried over into the grading--there were times the directions said to remove points if certain things weren't present on a student's answer, but then as a grader you actually didn't have the ability to remove points for that reason.

These types of mistakes are, I feel, pretty sloppy and shouldn't exist in a course that's part of an IBM-backed certification that people pay money for.

por Thierry C

31 de jul de 2021

This 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.