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學生對 IBM 提供的 Data Visualization with Python 的評價和反饋

5,649 個評分
636 條評論


"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....



Nov 21, 2019

It's a really great course with proper hands on time and the assignments are great too. i got enough opportunity to explore the things which were taught in the course. Really Satisfied. Thanks :)


Jan 08, 2020

This course gives very well knowledge about different types of visualization techniques and helps to start with visualization. Coursera provided an amazing course with an amazing instructor.


351 - Data Visualization with Python 的 375 個評論(共 625 個)

創建者 Muthu M H

Feb 15, 2020


創建者 Diego F B M

Dec 19, 2019


創建者 Marouane E O

Sep 15, 2019






創建者 Arnab K C

Aug 19, 2019


創建者 Usman R M

Apr 04, 2019


創建者 Rajib K S

Feb 23, 2019


創建者 Kathleen P

Dec 31, 2018


創建者 Haowen W

Jan 31, 2020


創建者 Yu M C

Dec 09, 2019


創建者 Manea S I

Sep 14, 2019


創建者 Prabhu M

Sep 07, 2019


創建者 Nay L

Jul 13, 2019


創建者 Aditya J

May 22, 2019


創建者 Piotr M

Oct 28, 2018


創建者 Muhammad T A

Sep 16, 2019


創建者 Fan Y

Jul 25, 2019


創建者 Manivannan D

Feb 20, 2019


創建者 banan A

Jan 11, 2019


創建者 Amy P

May 26, 2019

Once again, quality hands-on labs were the highlight of this course (as has been the case throughout the IBM Data Science Certificate courses). The end-of-week quizzes were also a bit more difficult/involved, which was a good challenge. Still, I think there's room to increase the difficulty a bit further - after all, you can re-take the quizzes if at first you don't pass. I appreciated that the final project gave us the opportunity to apply a wide range of the skills that we learned.

That being said, I think there was quite a bit of fluff in the lectures. I would have preferred more content/exposure to other libraries rather than the redundant "data recaps" at the beginning of almost every video. I also would have appreciated more theory/recommendations for selecting the best visualization for a given application.

創建者 Lena N

Sep 26, 2018

The best parts of the course were the labs and the final assignment. I spend a lot of time at the labs, paying extra attention to the details and often following the external links suggested by the instructor. I found the final assignment very interesting with good explanations step by step and I especially liked how the instructor were present at the discussion forums.

The weakest part of the course were the videos, I think I could have skipped them altogether. The information mentioned in them were elaborated much better at the labs. Also, for some reason, 1/3 of each video was exactly the same clip recalling the dataset. That felt a bit useless and loss of time! On the other hand, each video was a couple of minutes long so no big deal in the end.

創建者 Jess M

Feb 28, 2019

The videos are nice and clear, the visualizations are beautiful, and I'm sure that all of the libraries presented are extremely useful. But this course is not well-suited to students who have no prior background in Python before taking the Applied Data Science specialization. I look forward to coming back and maybe having a shot at understanding the code in the labs after I take a Python programming course. The long chunks of code presented here are mostly opaque if all you have are the previous courses in this specialization.

創建者 Azhan A

Nov 21, 2019

The reason I'm giving it 4 stars is because the although the content was good, the labs were challenging but there are something which I found missing, for example, there should have been more information on libraries related to cholorpleth map. !wget was not working on my PC's jupyter notebook and looking it up on the internet was even harder because this extension or whatever it is big on its own. I don't know what to write to get the correct google search.

創建者 Eugene T B

Sep 23, 2019

The lectures make everything seem simple, but you really have to dive into the labs and make a point of studying on your own. You can easily get through most of this course just by running the Jupyter Notebooks that are provided then copy/pasting and editing for the final. If you really want to get something out of the course, you really have to motivate yourself to learn the material.

創建者 Oriana R

Nov 01, 2018

Honestly, out of all the courses I've taken so far, this one was the best, in terms of presentation. The instructor repeated a lot of the formatting for each code block and by the end, one could easily remember what code to use for the specific visualizations.

The only reason I did not give 5 stars was because I thought the final assignment deviated a bit, but otherwise, a good course.

創建者 Benjamin S

Jan 24, 2020

This course has one advantage over the others in the series: practice time. The labs are more thorough and provide more practice problems. However, the overall quality in production of this course is lower than the others. Additionally, there were some points awarded on the final project for things simply not covered in the lectures or labs, which was frustrating to say the least.