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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

10,265 classificações

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


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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1401 — 1425 de 1,553 Avaliações para o Data Visualization with Python

por LIM K W

2 de jan de 2020

Easily encounter 503 errors for the labs


25 de dez de 2019

Need to improve more please

por Marina H

19 de nov de 2019

Some code did't work in Labs

por Biswa B

18 de mai de 2021

I didn't like this course.

por Anmol P

6 de nov de 2019

More content and examples

por Em

29 de out de 2019

The labs are very buggy.

por Naman S

17 de abr de 2020

Could have been better


7 de out de 2019


por Mark P

18 de set de 2019

JSON links are broken.

por Musfiqul A

18 de fev de 2020

Need more hand notes

por pranav s

15 de mai de 2020

I found it boring

por Adil J

1 de jul de 2019

Can be better

por Shreyas R V

17 de mai de 2020

course is ok

por Gloria S

15 de ago de 2019

too basic

por Fabio B

15 de mar de 2019

Too basic

por Vu C T

22 de set de 2021

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 Matt N

30 de dez de 2021

T​here 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".....

T​his 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.

por Ian R

18 de fev de 2021

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.

por Soubir D

1 de mai de 2021

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.

por Albert K

8 de mai de 2021

The lessons are so easy to finish. Annoying is to have 40% of the time of each video repeating data cleaning. MOST FRUSTRATING is the FINAL PEER GRADED PROJECT, so frustrating to the level that even if your code is correct it will fail to run to show the required charts. Google colab was the final savior for me to avoid the unending errors thrown at me in Jupyter NB, and through skills lab. In terms of recommending, I am not sure of what i can say based on the last part of it. I was grading and came across 2 students who seemed frustrated and submitted wrong files for each question. I am sure the their tactic was first to submit the project and get access to other student's work. Which showed a high order that students were frustrated and tired enough

por Cynthia J

27 de jul de 2021

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.

L​a 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.

por Abu S N

2 de mai de 2021

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.

por Mauro L

31 de ago de 2021

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.