Description
Complete a 1-2 synthesis and critique of Berinato’s “Why Visualizations That Really Work” and try to create your own from the example data.Below I have attached the following article you are going to review I will also attach a separate file on the specific instructions you must follow for reviewing this article please follow all instructions as they are very specific and critical.THE ARTICLE REVIEW IS 1-2 PAGES LONG, WRITTEN IN CHICAGO STYLE, 12 POINT FONT, DOUBLE SPACED WITH ONE INCH MARGINS ON ALL SIDES.THERE SHOULD ALSO BE PAGE NUMBERS THAT START ON THE FIRST PAGE AND ARE PUT IN THE UPPER RIGHT CORNER. PLEASE BE SURE TO READ ALL INSTRUCTIONS BEFORE WRITING THE ARTICLE REVIEW.
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Article/Critical Review Instructions
ECON 2300 / ECON 4520
1.
Introduction
Article reviews are reading assignments that ask you to link course reading with
current articles, podcasts, and other media that help illustrate the key statistical concepts
with a focus on data literacy (1-2 pages). Please answer one of the following:
● How Does the Article Apply and Illustrate Key Statistical Concepts?
○ Analyze how the author uses statistical methodologies, theories, or
concepts that are relevant to the course material. Identify specific methods,
findings, or interpretations, and link them back to the principles taught in
the course.
● What is the Quality and Relevance of the Data Presented?
○ Evaluate the quality of the data used in the article. Is the data collection
method robust and relevant to the study? How are the data analyzed, and
are the conclusions drawn from the data supported by appropriate
statistical tests? Assess whether the data and methods used are in
alignment with current standards of data literacy.
● How Does the Article Contribute to the Broader Field or Current Discussions
○ Connect the article’s findings or perspectives to current articles, podcasts,
or other media that are shaping the conversation in the field. How does
this particular work add to or challenge existing knowledge or debates?
Assess its potential impact on future research, policy, or practice.
Make sure to be precise in linking these concepts. I am looking for a synthesis of
classroom materials and the article’s information with a focus on linking theory and
practice.
2.
Formatting and Citation
In adhering to the Chicago style guidelines for formatting and citation, the 1-2
page synthesis should be typed with a clear, readable font such that is easily readable,
12-point in size. The text should be double-spaced with margins of at least one inch on
all sides. Page numbers should start on the first page and be placed in the upper right
corner. Citations within the text should be formatted using either footnotes or
endnotes, depending on your professor’s preference. Each footnote or endnote should
correspond to a superscript number in the text, leading the reader to the cited source. In
the footnotes or endnotes, include the author’s full name, the title of the work,
publication place, publisher, date of publication, and the page number(s) referenced. If
you are citing a journal article, include the volume, issue, and page numbers. A separate
bibliography page at the end of the document should list all the references in
alphabetical order by the author’s last name, following the same citation details as in the
footnotes or endnotes. Adherence to these guidelines ensures that the synthesis
maintains a formal, scholarly tone and accurately credits all sources, aligning with the
academic integrity standards as described by the University guidelines.
In Chicago style, citations can be handled through either footnotes or endnotes,
depending on the preference of your professor or the specific requirements of the
assignment. When you cite a source, a superscript number is inserted at the end of the
sentence or clause containing the cited material. This number corresponds to a note at
the bottom of the page (footnote) or at the end of the paper (endnote). Please consider
using a citation manager such as Zotero or Endnote for keeping track of your citations
and this makes it easy to manage your work.
In this class, we use the Chicago Author-Date citation style. The in-text citation
includes the author’s last name, the publication year, and the page number(s) if
applicable. This style is often used in the sciences and social sciences. Here’s how to
apply it:
2.1
In-Text Citation
When you cite a source in the text, you include the author’s last name and the
publication year. If you are citing a specific page or range of pages, you include that as
well. For example:
Example: (Smith 2020, 15-17)
2.2
Reference
At the end of your paper, you will include a reference list with full citations for
every source cited in the text. The entries are arranged alphabetically by the author’s last
name.
Example: Author’s Last name, First name. Year. “Title of the Article.” Title of
the Journal Volume Number (Issue Number): Page Range.
2.3
Examples
For a synthesis of an article and classroom material, you might cite both within the text
as follows:
According to recent research on data literacy (Johnson 2022, 45), the
methodology aligns with what was taught in our statistical concepts course
(Miller 2019, 67-68).
In the reference list, these citations would appear as:
Johnson, Mary A. 2022. Data Literacy in the Modern Age. New York: Academic
Press.
Miller, John B. 2019. “Statistical Concepts in Undergraduate Education.” Journal
of Statistics Education 15(3): 65-80.
Remember, consistency is key in a citation, and it may be beneficial to consult the latest
edition of the Chicago Manual of Style or your institution’s guidelines for any specific
rules or variations that may apply to your assignment.
3.
Writing Style
“The Elements of Style” by William Strunk Jr. and E.B. White is a seminal guide
that emphasizes clarity, conciseness, and precision in writing. Rather than inundating
the reader with complex rules, the authors present fundamental principles that foster a
clean and straightforward writing style. Among the well-known guidelines are the
encouragement to “omit needless words,” the advice to use the active voice rather than
the passive, and the recommendation to choose simple words over complex ones when
possible. Strunk and White’s guidance also extends to grammatical constructions,
advising on proper comma usage, verb tense consistency, and the appropriate use of
parallel structure. This timeless guide serves as a touchstone for many writers, whether
novices or professionals, aiming to produce clear and effective prose.
● Clarity and Brevity: In line with Strunk and White’s principle to “omit needless
words,” your synthesis should be concise and to the point. Eliminate unnecessary
words or phrases and focus on clear, direct statements.
● Use the Active Voice: Strunk and White emphasize the use of the active voice
for a more engaging and direct style. Instead of writing “The experiment was
conducted by the researchers,” write “The researchers conducted the experiment.”
● Adopt a Simple Style: Avoid overly complex or pretentious language. Choose
simple and straightforward words that convey your meaning without confusion,
following Strunk and White’s guidance to “write in a way that comes naturally.”
● Organize Logically: Your synthesis should follow a logical structure. Start with
a clear thesis statement, present your points in a coherent order, and conclude
with a strong summary. This aligns with Strunk and White’s focus on clear and
logical composition.
● Ensure Parallel Construction: Strunk and White advise using parallel structure
when listing items or ideas. For example, “The study was groundbreaking,
innovative, and influential” maintains a parallel structure.
● Be Consistent with Tenses: Maintain consistency in verb tenses, another
principle emphasized in “The Elements of Style.” If you begin in the past tense,
stay in that tense unless there’s a clear reason to switch.
A note on spelling and grammar, as college students you are still learning to write
effectively, and therefore you may not have all the grammar and spelling skills you need
for the working world. However, I am not really interested in evaluating your spelling or
grammar skills, I want to know how you think and you should work hard to make your
writing as clear as possible so that I can understand your ideas and critical thinking. I do
not take points off for spelling or grammar.
4.
Synthesis (ECON 2300)
A synthesis is a combination of various ideas, findings, or aspects from different
sources to form a comprehensive understanding of a subject. For a 1-2 page synthesis,
read and analyze the selected sources, identifying key themes or insights. Summarize
these in a coherent and concise manner, emphasizing how they connect or contrast with
one another. The goal is to create a unified picture that reflects the complexity and
nuances of the subject without presenting a specific argument or thesis. Proper citation
and adherence to stylistic guidelines are essential.
.
Submission
Please submit the 1-2 page Critical/Article Review via Brightspace by the noon
deadline. If you are late you will receive a 0 unless a prior arrangement has been made
with me before the deadline has passed.
Analytics And Data Science
Visualizations
That
Really
Work
by Scott Berinato
From the Magazine (June 2016)
HBR Staff
Summary. Not long ago, the ability to create smart data visualizations (or dataviz)
was a nice-to-have skill for design- and data-minded managers. But now it’s a
must-have skill for all managers, because it’s often the only way to make sense of
the work they do. Decision… more
Not long ago, the ability to create smart data visualizations, or
dataviz, was a nice-to-have skill. For the most part, it benefited
design- and data-minded managers who made a deliberate
decision to invest in acquiring it. That’s changed. Now visual
communication is a must-have skill for all managers, because
more and more often, it’s the only way to make sense of the work
they do.
Data is the primary force behind this shift. Decision making
increasingly relies on data, which comes at us with such
overwhelming velocity, and in such volume, that we can’t
comprehend it without some layer of abstraction, such as a visual
one. A typical example: At Boeing the managers of the Osprey
program need to improve the efficiency of the aircraft’s takeoffs
and landings. But each time the Osprey gets off the ground or
touches back down, its sensors create a terabyte of data. Ten
takeoffs and landings produce as much data as is held in the
Library of Congress. Without visualization, detecting the
inefficiencies hidden in the patterns and anomalies of that data
would be an impossible slog.
But even information that’s not statistical demands visual
expression. Complex systems—business process workflows, for
example, or the way customers move through a store—are hard to
understand, much less fix, if you can’t first see them.
Thanks to the internet and a growing number of affordable tools,
translating information into visuals is now easy (and cheap) for
everyone, regardless of data skills or design skills. This is largely a
positive development. One drawback, though, is that it reinforces
the impulse to “click and viz” without first thinking about your
purpose and goals. Convenient is a tempting replacement for
good, but it will lead to charts that are merely adequate or, worse,
ineffective. Automatically converting spreadsheet cells into a
chart only visualizes pieces of a spreadsheet; it doesn’t capture an
idea. As the presentation expert Nancy Duarte puts it, “Don’t
project the idea that you’re showing a chart. Project the idea that
you’re showing a reflection of human activity, of things people did
to make a line go up and down. It’s not ‘Here are our Q3 financial
results,’ it’s ‘Here’s where we missed our targets.’”
Managers who want to get better at making charts often start by
learning rules. When should I use a bar chart? How many colors
are too many? Where should the key go? Do I have to start my y-
axis at zero? Visual grammar is important and useful—but
knowing it doesn’t guarantee that you’ll make good charts. To
start with chart-making rules is to forgo strategy for execution; it’s
to pack for a trip without knowing where you’re going.
Your visual communication will prove far more successful if you
begin by acknowledging that it is not a lone action but, rather,
several activities, each of which requires distinct types of
planning, resources, and skills. The typology I offer here was
created as a reaction to my making the very mistake I just
described: The book from which this article is adapted started out
as something like a rule book. But after exploring the history of
visualization, the exciting state of visualization research, and
smart ideas from experts and pioneers, I reconsidered the project.
We didn’t need another rule book; we needed a way to think about
the increasingly crucial discipline of visual communication as a
whole.
The typology described in this article is simple. By answering just
two questions, you can set yourself up to succeed.
The Two Questions
To start thinking visually, consider the nature and purpose of
your visualization:
CONCEPTUAL
FOCUS: Ideas
GOALS: Simplify, teach (“Here’s how our organization is
structured.”)
DATA-DRIVEN
FOCUS: Statistics
GOALS: Inform, enlighten (“Here are our revenues for
the past two years.”)
Is the information conceptual or data-driven?
Am I declaring something or exploring something?
If you know the answers to these questions, you can plan what
resources and tools you’ll need and begin to discern what type of
visualization will help you achieve your goals most effectively.
The first question is the simpler of the two, and the answer is
usually obvious. Either you’re visualizing qualitative information
or you’re plotting quantitative information: ideas or statistics. But
notice that the question is about the information itself, not the
forms you might ultimately use to show it. For example, the
classic Gartner Hype Cycle uses a traditionally data-driven form—
a line chart—but no actual data. It’s a concept.
If the first question identifies what you have, the second elicits
what you’re doing: either communicating information
(declarative) or trying to figure something out (exploratory).
DECLARATIVE
FOCUS: Documenting, designing
GOALS: Affirm (“Here is our budget by department.”)
EXPLORATORY
FOCUS: Prototyping, iterating, interacting, automating
GOALS: Confirm (“Let’s see if marketing investments
contributed to rising profits.”) and discover (“What
would we see if we visualized customer purchases by
gender, location, and purchase amount in real time?”)
Managers most often work with declarative visualizations, which
make a statement, usually to an audience in a formal setting. If
you have a spreadsheet workbook full of sales data and you’re
using it to show quarterly sales in a presentation, your purpose is
declarative.
But let’s say your boss wants to understand why the sales team’s
performance has lagged lately. You suspect that seasonal cycles
have caused the dip, but you’re not sure. Now your purpose is
exploratory, and you’ll use the same data to create visuals that will
confirm or refute your hypothesis. The audience is usually
yourself or a small team. If your hypothesis is confirmed, you may
well show your boss a declarative visualization, saying, “Here’s
what’s happening to sales.”
Exploratory visualizations are actually of two kinds. In the
example above, you were testing a hypothesis. But suppose you
don’t have an idea about why performance is lagging—you don’t
know what you’re looking for. You want to mine your workbook to
see what patterns, trends, and anomalies emerge. What will you
see, for example, when you measure sales performance in relation
to the size of the region a salesperson manages? What happens if
you compare seasonal trends in various geographies? How does
weather affect sales? Such data brainstorming can deliver fresh
insights. Big strategic questions—Why are revenues falling?
Where can we find efficiencies? How do customers interact with
us?—can benefit from a discovery-focused exploratory
visualization.
The Four Types
The nature and purpose questions combine in a classic 2×2 to
define four types of visual communication: idea illustration, idea
generation, visual discovery, and everyday dataviz.
Idea
Illustration. We
might call this
quadrant the
“consultants’
corner.” Consultants
can’t resist process
diagrams, cycle
diagrams, and the
like. At their best,
idea illustrations clarify complex ideas by drawing on our ability
to understand metaphors (trees, bridges) and simple design
conventions (circles, hierarchies). Org charts and decision trees
are classic examples of idea illustration. So is the 2×2 that frames
this article.
Idea illustration demands clear and simple design, but its reliance
on metaphor invites unnecessary adornment. Because the
discipline and boundaries of data sets aren’t built in to idea
illustration, they must be imposed. The focus should be on clear
communication, structure, and the logic of the ideas. The most
useful skills here are similar to what a text editor brings to a
manuscript—the ability to pare things down to their essence.
Some design skills will be useful too, whether they’re your own or
hired.
For HBR Subscribers
How to Give a Great Presentation
Tips to improve your talk, from preparation to delivery.
Show Reading List
IDEA ILLUSTRATION
INFO TYPE: Process, framework
TYPICAL SETTING: Presentations, teaching
PRIMARY SKILLS: Design, editing
GOALS: Learning, simplifying, explaining
Suppose a company engages consultants to help its R&D group
find inspiration in other industries. The consultants use a
technique called the pyramid search—a way to get information
from experts in other fields close to your own, who point you to
the top experts in their fields, who point you to experts in still
other fields, who then help you find the experts in those fields,
and so on.
It’s actually tricky to explain, so the consultants may use
visualization to help. How does a pyramid search work? It looks
something like this:
The axes use conventions that we can grasp immediately:
industries plotted near to far and expertise mapped low to high.
The pyramid shape itself shows the relative rarity of top experts
compared with lower-level ones. Words in the title—“climbing”
and “pyramids”—help us grasp the idea quickly. Finally, the
designer didn’t succumb to a temptation to decorate: The
pyramids aren’t literal, three-dimensional, sandstone-colored
objects.
Too often, idea illustration doesn’t go that well, and you end up
with something like this:
Here the color gradient, the drop shadows, and the 3-D pyramids
distract us from the idea. The arrows don’t actually demonstrate
how a pyramid search works. And experts and top experts are
placed on the same plane instead of at different heights to convey
relative status.
Idea Generation. Managers may not think of
visualization as a tool to support idea generation,
but they use it to brainstorm all the time—on
whiteboards, on butcher paper, or, classically, on
the back of a napkin. Like idea illustration, idea generation relies
on conceptual metaphors, but it takes place in more-informal
settings, such as off-sites, strategy sessions, and early-phase
innovation projects. It’s used to find new ways of seeing how the
business works and to answer complex managerial challenges:
restructuring an organization, coming up with a new business
process, codifying a system for making decisions.
IDEA GENERATION
INFO TYPE: Complex, undefined
TYPICAL SETTING: Working session, brainstorming
PRIMARY SKILLS: Team-building, facilitation
GOALS: Problem solving, discovery, innovation
Although idea generation can be done alone, it benefits from
collaboration and borrows from design thinking—gathering as
many diverse points of view and visual approaches as possible
before homing in on one and refining it. Jon Kolko, the founder
and director of the Austin Center for Design and the author of
Well-Designed: How to Use Empathy to Create Products People
Love, fills the whiteboard walls of his office with conceptual,
exploratory visualizations. “It’s our go-to method for thinking
through complexity,” he says. “Sketching is this effort to work
through ambiguity and muddiness and come to crispness.”
Managers who are good at leading teams, facilitating
brainstorming sessions, and encouraging and then capturing
creative thinking will do well in this quadrant. Design skills and
editing are less important here, and sometimes
counterproductive. When you’re seeking breakthroughs, editing is
the opposite of what you need, and you should think in rapid
sketches; refined designs will just slow you down.
Suppose a marketing team is holding an off-site. The team
members need to come up with a way to show executives their
proposed strategy for going upmarket. An hourlong whiteboard
session yields several approaches and ideas (none of which are
erased) for presenting the strategy. Ultimately, one approach
gains purchase with the team, which thinks it best captures the
key point: Get fewer customers to spend much more. The
whiteboard looks something like this:
Of course, visuals that emerge from idea generation often lead to
more formally designed and presented idea illustrations.
Visual Discovery.
This is the most
complicated
quadrant, because
in truth it holds two
categories. Recall
that we originally
separated
exploratory
purposes into two kinds: testing a hypothesis and mining for
patterns, trends, and anomalies. The former is focused, whereas
the latter is more flexible. The bigger and more complex the data,
and the less you know going in, the more open-ended the work.
VISUAL DISCOVERY
INFO TYPE: Big data, complex, dynamic
TYPICAL SETTING: Working sessions, testing, analysis
PRIMARY SKILLS: Business intelligence, programming,
paired analysis
GOALS: Trend spotting, sense making, deep analysis
Visual confirmation. You’re answering one of two questions
with this kind of project: Is what I suspect actually true? or What
are some other ways of depicting this idea?
The scope of the data tends to be manageable, and the chart types
you’re likely to use are common—although when trying to depict
things in new ways, you may venture into some less-common
types. Confirmation usually doesn’t happen in a formal setting;
it’s the work you do to find the charts you want to create for
presentations. That means your time will shift away from design
and toward prototyping that allows you to rapidly iterate on the
dataviz. Some skill at manipulating spreadsheets and knowledge
of programs or sites that enable swift prototyping are useful here.
Suppose a marketing manager believes that at certain times of the
day more customers shop his site on mobile devices than on
desktops, but his marketing programs aren’t designed to take
advantage of that. He loads some data into an online tool (called
Datawrapper) to see if he’s right (1 above).
He can’t yet confirm or refute his hypothesis. He can’t tell much
of anything, but he’s prototyping and using a tool that makes it
easy to try different views into the data. He works fast; design is
not a concern. He tries a line chart instead of a bar chart (2).
Now he’s seeing something, but working with three variables still
doesn’t quite get at the mobile-versus-desktop view he wants, so
he tries again with two variables (3). Each time he iterates, he
evaluates whether he can confirm his original hypothesis: At
certain times of day more customers are shopping on mobile
devices than on desktops.
On the fourth try he zooms in and confirms his hypothesis (4).
New software tools mean this type of visualization is easier than
ever before: They’re making data analysts of us all.
Visual exploration. Open-ended data-driven visualizations tend
to be the province of data scientists and business intelligence
analysts, although new tools have begun to engage general
managers in visual exploration. It’s exciting to try, because it
often produces insights that can’t be gleaned any other way.
Because we don’t know what we’re looking for, these visuals tend
to plot data more inclusively. In extreme cases, this kind of project
may combine multiple data sets or load dynamic, real-time data
into a system that updates automatically. Statistical modeling
benefits from visual exploration.
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Exploration also lends itself to
interactivity: Managers can
adjust parameters, inject new
data sources, and continually
revisualize. Complex data
sometimes also suits specialized
and unusual visualization, such
as force-directed diagrams that
show how networks cluster, or
topographical plots.
Function trumps form here: Analytical, programming, data
management, and business intelligence skills are more crucial
than the ability to create presentable charts. Not surprisingly, this
half of the quadrant is where managers are most likely to call in
experts to help set up systems to wrangle data and create
visualizations that fit their analytic goals.
Anmol Garg, a data scientist at Tesla Motors, has used visual
exploration to tap into the vast amount of sensor data the
company’s cars produce. Garg created an interactive chart that
shows the pressure in a car’s tires over time. In true exploratory
form, he and his team first created the visualizations and then
found a variety of uses for them: to see whether tires are properly
inflated when a car leaves the factory, how often customers
reinflate them, and how long customers take to respond to a lowpressure alert; to find leak rates; and to do some predictive
modeling on when tires are likely to go flat. The pressure of all
four tires is visualized on a scatter plot, which, however
inscrutable to a general audience, is clear to its intended
audience.
Garg was exploring data to find insights that could be gleaned
only through visuals. “We’re dealing with terabytes of data all the
time,” he says. “You can’t find anything looking at spreadsheets
and querying databases. It has to be visual.” For presentations to
the executive team, Garg translates these exploration sessions
into the kinds of simpler charts discussed below. “Management
loves seeing visualizations,” he says.
Everyday Dataviz. Whereas data scientists do
most of the work on visual exploration, managers
do most of the work on everyday visualizations.
This quadrant comprises the basic charts and
graphs you normally paste from a spreadsheet into a
presentation. They are usually simple—line charts, bar charts,
pies, and scatter plots.
EVERYDAY DATAVIZ
INFO TYPE: Simple, low volume
TYPICAL SETTING: Formal, presentations
PRIMARY SKILLS: Design, storytelling
GOALS: Affirming, setting context
“Simple” is the key. Ideally, the visualization will communicate a
single message, charting only a few variables. And the goal is
straightforward: affirming and setting context. Simplicity is
primarily a design challenge, so design skills are important.
Clarity and consistency make these charts most effective in the
setting where they’re typically used: a formal presentation. In a
presentation, time is constrained. A poorly designed chart will
waste that time by provoking questions that require the presenter
to interpret information that’s meant to be obvious. If an everyday
dataviz can’t speak for itself, it has failed—just like a joke whose
punch line has to be explained.
That’s not to say that declarative charts shouldn’t generate
discussion. But the discussion should be about the idea in the
chart, not the chart itself.
Suppose an HR VP will be presenting to the rest of the executive
committee about the company’s health care costs. She wants to
convey that the growth of these costs has slowed significantly,
creating an opportunity to invest in additional health care
services.
The VP has read an online report about this trend that includes a
link to some government data. She downloads the data and clicks
on the line chart option in Excel. She has her viz in a few seconds.
But because this is for a presentation, she asks a designer
colleague to add detail from the data set to give a more
comprehensive view.
This is a well-designed, accurate chart, but it’s probably not the
right one. The executive committee doesn’t need two decades’
worth of historical context to discuss the company’s strategy for
employee benefits investments. The point the VP wants to make
is that cost increases have slowed over the past few years. Is that
clearly communicated here?
In general, when it takes more than a few seconds to digest the
data in a chart, the chart will work better on paper or on a
personal-device screen, for someone who’s not expected to listen
to a presentation while trying to take in so much information. For
example, health care policy makers might benefit from seeing this
chart in advance of a hearing at which they’ll discuss these longterm trends.
Our VP needs something cleaner for her context. She could make
her point as simply as this:
Simplicity like this takes some discipline—and courage—to
achieve. The impulse is to include everything you know. Busy
charts communicate the idea that you’ve been just that—busy.
“Look at all the data I have and the work I’ve done,” they seem to
say. But that’s not the VP’s goal. She wants to persuade her
colleagues to invest in new programs. With this chart, she won’t
have to utter a word for the executive team to understand the
trend. She has clearly established a foundation for her
recommendations.
In some ways, “data visualization” is a terrible term. It seems to
reduce the construction of good charts to a mechanical
procedure. It evokes the tools and methodology required to create
rather than the creation itself. It’s like calling Moby-Dick a “word
sequentialization” or The Starry Night a “pigment distribution.”
It also reflects an ongoing obsession in the dataviz world with
process over outcomes. Visualization is merely a process. What we
actually do when we make a good chart is get at some truth and
move people to feel it—to see what couldn’t be seen before. To
change minds. To cause action.
Some basic common grammar will improve our ability to
communicate visually. But good outcomes require a broader
understanding and a strategic approach—which the typology
described here is meant to help you develop.
ABusiness
version Review.
of this article appeared in the June 2016 issue (pp.92–100) of Harvard
Scott Berinato is a senior editor at Harvard
Business Review and the author of Good Charts
Workbook: Tips Tools, and Exercises for Making
Better Data Visualizations and Good Charts:
The HBR Guide to Making Smarter, More
Persuasive Data Visualizations.
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