This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.
The Truthful Art (Alberto Cairo)
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From the course by University of Michigan
Applied Plotting, Charting & Data Representation in Python
1721 ratings
University of Michigan
1721 ratings
Course 2 of 5 in the Specialization Applied Data Science with Python
From the lesson
Module 1: Principles of Information Visualization
In this module, you will get an introduction to principles of information visualization. We will be introduced to tools for thinking about design and graphical heuristics for thinking about creating effective visualizations. All of the course information on grading, prerequisites, and expectations are on the course syllabus, which is included in this module.
Meet the Instructors
Christopher Brooks
I consider Alberto Cairo's book,
The Truthful Art to be required reading for any data scientist.
In one book, he has managed to pack in a plethora of references to experts in
the field while still distilling down details into memorable lessons.
While we won't have an opportunity to cover everything he talks about.
I want to talk in this lecture about the foundation of the book in particular which
is a five part frame work for considering the qualities of an information graphic.
Importantly, Cairo says that these qualities are not independent of one
another and that they're interrelated.
So while I tend to be prone to reductionism, keep in mind that
the different qualities interact with one another as we go through the lecture.
The first quality of a good visualization is that it's truthful.
Now Cairo admits that truth could be considered subjective and
it's certainly on a continuum with some things being more truthful than others.
And then it might be impossible to establish an absolute truth.
But as data scientists,
he argues, we have two obligations when it comes to protecting the truth.
First, we have to be honest with ourselves when we clean and summarize data.
Are the activities we're engaging in likely to obscure a message because
of the limitations that we've applied?
We should consider explicitly each modification that we're making to
the data.
And make sure we are practicing what he calls self deception.
Skepticism is an important skill for data scientist.
And we often get so focus on looking for patterns that we reduce or
present data in ways which are inauthentic to the phenomena being described.
The second obligation is to our audience.
That is there are techniques and data science and information visualization,
which we can use to shine light on to specific pieces of data.
Indeed this is generally the point of the fields.
But if we omitted the ability of the reader to explore the phenomena more fully
this will generate doubt and cast distrust.
Cairo gives us a great examples of this,
using a chart inspired by one put out by the lobbying association.
The national cable and telecommunications association.
This association lobbies on behalf of US cable companies.
And provided a graphic which suggested that, after regulations were relaxed,
cable companies invested four times as much in the industry.
Now the original chart has been taken down, but
Cairo provides an example of what was visualized.
And we can see here, the title of the chart alone, was intended to draw
the reader to the conclusion, less regulation means more industry investment.
But there's some immediate problems with this graphic.
First, it's unclear whether the monies are adjusted for
inflation, which is a big issue when providing charts of financial data.
Second, the time spans for the two bars are different.
One covers a span of three years, while the other covers a span of four years.
Think of this through the lens of Tufts' Live Factor Analysis.
The bars have the same width, but don't represent the same period of time.
This is a completely unforgivable design choice.
And finally, there seems to be a bunch of missing data from years 1997 and 1998.
And the chart stops in the year 2003, but the information was tweeted in 2014.
What does a more honest view of the data look like?
Well Cairo tracked this down, and provided a line graph.
We can see that after regulation,
there were in fact years of sustained industry investment.
We see a slight drop after deregulation, then a massive spike in spending.
And then a significant fall from the period post 2002,
much of which is excluded from the original chart.
While it's unclear whether the dollar values are in fact corrected for
inflation, seems pretty unreasonable to suggest from this data alone
that regulations stems cable company investment and infrastructure.
The lesson here is that, while there's maybe no absolute truth,
there are ways of displaying data which are more truthful than others.
The second quality of a visualization to consider is whether it is
functional or not.
This again is continuum when we saw an example previously with the slides on
data-ink-ratio provided by Darkhorse Analytics.
If you expect your audience would want to compare two data series.
Direct labeling of them is one way to increase comprehension.
Well there are many heuristics for increasing functionality,
I would like you to remember the user test we did with.
I think now is an exciting time for understating situated human behavior.
The web has dramatically increased trainability to test social and
behavioral hypothesis that scale across diverse populations.
And crowd power and mechanical tuck are just two example of this.
Understanding how functional a given approach to a visualization is doesn't
necessarily require expensive ethnographic research.
It's now much easier to test hypotheses directly with crowd sources.
The third quality of visualization, Cairo shares is whether it is beautiful or not.
I think that this requires knowing a great deal about your audience.
There are different perceptions based on people's lived experiences and
aspects about themselves such as gender and culture.
There's a lot to building a beautiful information visualization experience,
which depends on situational and contextual factors too.
For instance, if you're building a chart for a report,
the colors and styles that you might choose, even down the font choice,
might be more related to beauty than for instance, function.
But the interplay of these elements is something which is difficult to ignore.
I wonder is this broad topic of beauty one of the conflationary factors of the chart
junk memorability issue?
Where as Tufte argued that the duck junk is distracting and misleading,
the study by Bateman et al showed that it can make visualizations more memorable.
Is this disconnect an issue of beauty in information graphics?
Here's one parting thought on beauty in information
that perhaps drives home this quality.
One of the most popular tech magazines, well known for
it's full page information graphic spreads is Wired Magazine.
On Wired's website, they tag these articles as info porn.
A reference to the visceral reaction that they hope readers will view
when viewing the articles.
The fourth quality of visualization from Cairo
is that information graphics should be insightful.
They shouldn't just replicate data in tables, but
they should draw the viewer in such that the viewer has an aha or eureka moment.
I think about this largely in the context of academic
papers where I do most of my work.
A common issue that novice writers have in this area is the inclusion of a figure for
every piece of data they have.
This is really unnecessary and adds bloat and contributes to reader fatigue.
But the right figure drives home the results of a study quickly.
Scientific peer review is common in my field, but
readers don't have the large amounts of time that they need to do full reviews.
When flipping through a submitted paper,
the reviewer sees an image which jumps off the page and gives them immediate insight.
This sets them up to understand the more nuanced story
to be told throughout the rest of the paper.
The final quality of visualization which Cairo
proposes is information graphics should be enlightening.
This is different than insightful.
In fact, Cairo argues that this quality is composed of the previous four,
truthful, functional, beautiful and insightful.
But he separates and then adds a social responsibility dimension.
I don't think there's an obvious answer to this question.
But without a doubt,
Cairo's Five Qualities of Great Visualizations being truthful, functional,
beautiful, insightful, and enlightening are well worth considering.
Cairo has more great work, and
following this lecture I have assigned a reading of his as well as
a discussion question to encourage you to weigh in on the immorality of
information visualization as it relates to data science.
And this ends the week of the course.
We've covered a lot of material very quickly.
But I really do believe that these two authors Alberto Ciro and
Edward Tufty are fundamental in building a strong foundation for
the applied data science skills, we'll focus on in the second week of the course.
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