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Learner Reviews & Feedback for Data Visualization with Python by IBM

7,969 ratings
1,079 reviews

About the Course

"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....

Top reviews


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.

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26 - 50 of 1,063 Reviews for Data Visualization with Python

By David A

Oct 22, 2019

While I enjoyed the content of this course, I feel that the instruction was disorganized. This course is part of a beginner sequence in data science, but the teacher assumes certain advanced skills are already known and does not teach them. For example, chart annotation is only briefly covered in the second lab, but the final assignment requires a depth of knowledge not taught in this course. If that's the case then chart annotation should be taught as its own section. A lot of the quizzes are written to trick you with ambiguous phrases, rarely do they actually test what is learned in the labs. I think the teaching in the other IBM data science courses is far better than this one, hopefully they improve this one.

By Clarence E Y

Mar 30, 2019

This course provides lectures that enable learners to understand the theory, application and practices that data scientists use to create meaningful visual presentations of complex data relationships. The labs provide adequate opportunities to do hands-on end-to-end work with data and visualization tools. The learner is challenged to go beyond the scope of information presented in the course to also search other resources to gain the knowledge necessary to complete the final project. Searching for additional resources builds a foundation for independent future work.

By Chris A B

Oct 27, 2019

The final project was somewhat more challenging due to some file downloading issues. But I was able to get some help in the forums for that, which helped me accomplish my goals.

By Rubén A G Z

Apr 22, 2020

I learned and understood how to make graphics based on a previously clean and standardized data source. I liked this section.

By Kirti S

Apr 22, 2020

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

By Alejandro A

Apr 24, 2020

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

By Shmagina V

Apr 21, 2020

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

By Renier S

Apr 24, 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.

By Atfy I Z

Apr 21, 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.

By umair

Apr 11, 2019

this course should come before data analysis with python

By Sarah s

Jan 14, 2019

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

By Andrew T

Jul 08, 2020

Compared to other courses in the IBM Professional Certification catalog, this course has some noticeable deficiencies.

First, the overall content of the course rather confusing. The very first lecture focuses around efficient 'less-is-more' figure design, which I certainly agree with. However, much of the course (and most of the tested material) focuses on making extraneous graphics such as waffle charts and chloropleth maps in situations where a simple bar graph would be the most efficient way to present data. Meanwhile, the standard module Seaborn (which is EXTREMELY expansive in data visualization utility) is given only a single 2 minute lecture.

Second, unlike all other courses I have taken in the IBM certification, the assignments and workshop sections of this course are largely unhelpful. In addition to my point above, the workshops focus on manipulating aesthetics of simple graphics (i.e. changing colors in a bar graph) as opposed to showcasing the broad number of figures that Python is capable of generating. This left me disappointed with what I took away from the course in terms of usable knowledge.

Finally, the final assignment is arduous and poorly documented. There is no structured notebook that provides guidance on solving the problems, which is particularly troublesome when rendering uncommon figures such as chloropleth maps. I found that I spent >80% of my time on the assignment chasing down unintelligible error messages, as opposed to developing a real understanding of the logic behind generating graphics in Python.

The majority of other courses in the IBM certification have been very well designed and educational, I just feel that this one in particular has a lot of room for improvement.

By Alasdair T

Jun 22, 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.

By Olin H K

Apr 19, 2020

Bottom Line Up Front: You are going to have to teach yourself using Google for the Final Project

This course was unique in the Data Science Professional Certificate Progression in that the content of the videos was not very helpful, making your rely on the labs to teach yourself.

The labs were not great in walking you through all the parameters that were available for each type of plot, and while you can copy and paste solutions and tinker to learn things, the course didn't leave me with a good understanding of how or why things work and thus, unable to apply solutions creatively and appropriately without much effort.

By far my least favorite course of the 7 IBM courses I have completed so far.

By Joao C

Feb 05, 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.

By Paul B

Feb 29, 2020

Like other reviews I was really looking forward to this course in the IBM syllabus but was very disappointed. Videos were very high level and repetitive - and there were a lot of them. The detail in the exercises was better but there were significant challenges in getting them to work. And then the final assignment was a bit of a joke. The visualisations you were expected to produce had not been taught in the course and as you'll see from the forums requires a lot of work arounds. I would suggest this course needs a bottom up re-write. Do less but cover it better!

By Aylin B E

Mar 16, 2020

The video content was not extensive and it was recommended to study on labs for more detiled information, however, like many people who took the lesson, I had difficulty opening and using the lab When I wrote about the problem to the support team, they said they already escalate this issue to their partner to take care of it and fix it as soon as possible. I completed assigments and labs on a different compiler.

I'm disappointed about this course after all the good courses I took on Coursera. I hope they will fix the issue quickly.

By Chu Y C

Jun 11, 2020

quiz had strangely tricky questions, which were not thoroughly covered in the videos. And as others have said, the final assignment challenged us to do a bunch of things that are out of the scope of this topic. It would have been much better if we had more guidance on setting up the groundwork to perform the visualizations. For instance, I had to figure out how to import folium into my notebooks, which took up some time - some direction might have helped with that. Overall, I did learn some from the material, but the experience could be way better.

By Johannes W

Aug 30, 2019

The videos were unfortunately pretty useless. At least half of the time the respective dataframe was processed (but always in exactly the same manner... zero information about the actual new concept). In addition the videos were too short and the really important new concepts were only introduced by quickly showing the code snippets. There was often no explanation of key concepts. Unfortunately, overall one of the weaker courses on Coursera. The Data Analysis Python course is much better and explains similar concepts.

By Hui W F

Dec 29, 2019

I enjoy the IBM certificate programme in data science so far. But this course is a great disappointment because of the followings. 1) The clips are not organised which waste us time to read the same contents (i.e. how to clean up the data) for more than five times. 2) The instruction is not clear without going into the details of the coding. 3) Some of the images of the assignment are incorrect, misleading many of us to spend extra time to fix something that should not be fixed. Hope Coursera can redo this course.

By Sarah W

Aug 26, 2019

The class is beneficial as it allows you to understand how to create visualizations of your projects. However, the final project for this class was very hard to understand. A lot of the parts that were expected to be completed were no where in the videos or labs. In the forum, you could tell because a lot of people were confused about the same stuff. Others and I ended up using other sources as a way to get the results Coursera was wanting, rather than what Coursera taught us.

By Jennifer B

Mar 12, 2020

This course does very little teaching. The labs demonstrate how to do things, but there is little explanation of why, and no theory about visualisation at all. Instead, students are simply taught to follow recipes. Also, for the past week, the IBM servers have been extremely unreliable. I haven't taken off any stars as it's not the fault of the teaching staff, but it's a bad look when an IBM run course can't actually get the IBM computing services to work properly.

By Gilles W

Apr 09, 2019

Videos are so so, the same introduction for all videos with 2 minutes of data formatting, which is exactly the same in all videos, leaving only few minutes at the end of the vid's for the content of the lesson. The examples in Jupyter were interesting but not very well structured. At the end, I better used Google and Pandas documentation to solve problems and learn about the topic. Not a bad lesson, but there are just more effective way to learn in my opinion.


Jun 22, 2020

There's a lot of good material, and ultimately enough to integrate into successive work in data science.

On the frustrating side, most of the explanations focused on "what" rather than "why", and there's so many "why" left unanswered when choosing to address a problem with these tools.

Additionally, most of the questions were either insultingly easy or incredibly difficult. Plenty of googling in addition to reviewing presented material.

By James H

May 05, 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...