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Learner Reviews & Feedback for Data Visualization with Python by IBM

9,588 ratings
1,434 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

Aug 13, 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!!!

Nov 20, 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 :)

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By Nima G M

Nov 9, 2020

Before visualizing any data, one should gather and import those data to their computer directory, and this could not happen without the Pandas library. Importing the data could be done simply using the Pandas library, whose functions somewhat overlaps with the Matplotlib library.

Although in the last week, the author introduces the Folium library, which is a library to visualize Maps and other related things that could be shown on the Maps, like the population density of different cities in a country, the main focus of the course is on the Pandas library, which is, of course, need that lots of attention and time.

In summary, this course is especially helpful for those who want to become familiar with the Pandas library.

The author also gives a very short amount of time to show how seaborn could be used to plot the regression plots using seaborn.regplot function, which is also showing wise time management by the author since it does not need more amount of time to spend on.

By liam c

May 7, 2021

The course and materials were very useful. However, there are a couple of things that I would like to flag up for possible improvement

There's are over reliance on the Jupyter Notebook and a lot of useful information that should have been in the videos was pushed into them

I know Dash is a large subject to cover but more information about the call back mechanism in Dash would have been useful - Fortunately I've used Dash, Matplotlib and Flask for a few years so it wasn't much of an issue for me.

Every video spent the first minute going over the data layout rather than focusing on information about a particular function (plot)

The biggest issue was the fact that I had to ask to be moved from an inactive session group, to an active one, to get access to the external tools and tests. This has impacted a large number of students and I have left a 'how to raise a support case' note in the discussion board for the group I was originally with

By 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.