Qualitative data definition and examples. Qualitative metaphors. Data visualization & journalism. Common kinds: mind maps, flow diagrams, words cloud, user journey, tube map, maps. Qualitative chart chooser
Linked Data and examples, why they matter. Data driven strategies. Data mining: laws and applications. Data aggregation and fundamentals of data representation (table, bar chart, histogram, pie chart, line graph, scatter plot). Data science definition and job roles (who does what).
Data mining, phases of the data mining process and its laws (according to Thomas Khabaza). Classical data aggregation, summary statistics and fundamental representation (tables, bar charts, histograms, pie charts, line graphs). Introduction to data science: definition, applications, process and roles.
Qualitative data definition and examples. Qualitative metaphors. Data visualization & journalism. Common kinds: mind maps, flow diagrams, words cloud, user journey, tube map, maps. Qualitative chart chooser
Survivorship bias applied to information. Cognition, how we learn, sensation and perception, experience. Human sight and visual perception, visual memory. Gestalt principles. Machine perception.
Visual communication of quantitative data (v. 2020 ITA)Frieda Brioschi
Quantitative and qualitative data recap. Visual systems and preattentive attributes. Quantitative data visualization, chart selector. Some useful tactics.
How to collect and organize data (v. ITA 2020)Frieda Brioschi
Overview on data collection methods and a deep dive on data (primary Vs secondary, qualitative and quantitative). Bias. Data processing and structured, unstructured, semistructured data. Example of personal data tracking.
Linked Data and examples, why they matter. Data driven strategies. Data mining: laws and applications. Data aggregation and fundamentals of data representation (table, bar chart, histogram, pie chart, line graph, scatter plot). Data science definition and job roles (who does what).
Recap on storytelling.
We analyze the current landscape, starting from Cluetrain Manifesto, through some definitions (social networks, networked publics).
How we can create an effective message: personalization, groups, behaviours, communities, immediacy, perfect timing, different techniques and styles.
Then some essential rules, regarding listen and conversation, the blur between public and private, goals.
Linked Data and examples, why they matter. Data driven strategies. Data mining: laws and applications. Data aggregation and fundamentals of data representation (table, bar chart, histogram, pie chart, line graph, scatter plot). Data science definition and job roles (who does what).
Data mining, phases of the data mining process and its laws (according to Thomas Khabaza). Classical data aggregation, summary statistics and fundamental representation (tables, bar charts, histograms, pie charts, line graphs). Introduction to data science: definition, applications, process and roles.
Qualitative data definition and examples. Qualitative metaphors. Data visualization & journalism. Common kinds: mind maps, flow diagrams, words cloud, user journey, tube map, maps. Qualitative chart chooser
Survivorship bias applied to information. Cognition, how we learn, sensation and perception, experience. Human sight and visual perception, visual memory. Gestalt principles. Machine perception.
Visual communication of quantitative data (v. 2020 ITA)Frieda Brioschi
Quantitative and qualitative data recap. Visual systems and preattentive attributes. Quantitative data visualization, chart selector. Some useful tactics.
How to collect and organize data (v. ITA 2020)Frieda Brioschi
Overview on data collection methods and a deep dive on data (primary Vs secondary, qualitative and quantitative). Bias. Data processing and structured, unstructured, semistructured data. Example of personal data tracking.
Linked Data and examples, why they matter. Data driven strategies. Data mining: laws and applications. Data aggregation and fundamentals of data representation (table, bar chart, histogram, pie chart, line graph, scatter plot). Data science definition and job roles (who does what).
Recap on storytelling.
We analyze the current landscape, starting from Cluetrain Manifesto, through some definitions (social networks, networked publics).
How we can create an effective message: personalization, groups, behaviours, communities, immediacy, perfect timing, different techniques and styles.
Then some essential rules, regarding listen and conversation, the blur between public and private, goals.
Linked Data and examples, why they matter. Data driven strategies. Data mining: laws and applications. Data aggregation and fundamentals of data representation (table, bar chart, histogram, pie chart, line graph, scatter plot). Data science definition and job roles (who does what).
Survivorship bias applied to information. Cognition, how we learn, sensation and perception, experience. Human sight and visual perception, visual memory. Gestalt principles. Machine perception.
Recap on storytelling.
We analyze the current landscape, starting from Cluetrain Manifesto, through some definitions (social networks, networked publics).
How we can create an effective message: personalization, groups, behaviours, communities, immediacy, perfect timing, different techniques and styles.
Then some essential rules, regarding listen and conversation, the blur between public and private, goals.
The age of artificial intelligence, deep dives on machine learning and deep learning. Machine perception and applications. How company use AI in their businesses. Case study: Netflix.
The age of artificial intelligence, deep dives on machine learning and deep learning. Machine perception and applications. How company use AI in their businesses. Case study: Netflix. Basic tools for data manipulation and data visualization.
Quantitative and qualitative data recap. Visual systems and preattentive attributes. Quantitative data visualization, chart selector. Some useful tactics.
How to collect and organize data (v. ITA 2021)Frieda Brioschi
Overview on data collection methods and a deep dive on data (primary Vs secondary, qualitative and quantitative). Bias. Data processing and structured, unstructured, semistructured data. Example of personal data tracking.
Survivorship bias applied to information. Cognition, how we learn, sensation and perception, experience. Human sight and visual perception, visual memory. Gestalt principles. Machine perception.
Overview on data collection methods and a deep dive on data (primary Vs secondary, qualitative and quantitative). Bias. Data processing and structured, unstructured, semistructured data. Databases jargon.
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
Data visualization is crucial to understanding the big data being generated by apps and services. Data visualization toolkits such as D3.js and charting toolkits are immensely popular but it remains difficult to create meaningful dashboards or usable analytics tools or clear data visualizations. This talk will discuss data visualization principles, present best practices, showcase excellent visualizations in practice, and share useful tips and mistakes learned.
Visual communication of qualitative and quantitative data (v. 2021 ITA)Frieda Brioschi
Visual systems and preattentive attributes. Quantitative data visualization, chart selector. Some useful tactics. Qualitative data definition and examples. Qualitative metaphors. Data visualization & journalism. Common kinds: mind maps, flow diagrams, words cloud, user journey, tube map, maps. Qualitative chart chooser.
Linked Data and examples, why they matter. Data driven strategies. Data mining: laws and applications. Data aggregation and fundamentals of data representation (table, bar chart, histogram, pie chart, line graph, scatter plot). Data science definition and job roles (who does what).
Survivorship bias applied to information. Cognition, how we learn, sensation and perception, experience. Human sight and visual perception, visual memory. Gestalt principles. Machine perception.
Recap on storytelling.
We analyze the current landscape, starting from Cluetrain Manifesto, through some definitions (social networks, networked publics).
How we can create an effective message: personalization, groups, behaviours, communities, immediacy, perfect timing, different techniques and styles.
Then some essential rules, regarding listen and conversation, the blur between public and private, goals.
The age of artificial intelligence, deep dives on machine learning and deep learning. Machine perception and applications. How company use AI in their businesses. Case study: Netflix.
The age of artificial intelligence, deep dives on machine learning and deep learning. Machine perception and applications. How company use AI in their businesses. Case study: Netflix. Basic tools for data manipulation and data visualization.
Quantitative and qualitative data recap. Visual systems and preattentive attributes. Quantitative data visualization, chart selector. Some useful tactics.
How to collect and organize data (v. ITA 2021)Frieda Brioschi
Overview on data collection methods and a deep dive on data (primary Vs secondary, qualitative and quantitative). Bias. Data processing and structured, unstructured, semistructured data. Example of personal data tracking.
Survivorship bias applied to information. Cognition, how we learn, sensation and perception, experience. Human sight and visual perception, visual memory. Gestalt principles. Machine perception.
Overview on data collection methods and a deep dive on data (primary Vs secondary, qualitative and quantitative). Bias. Data processing and structured, unstructured, semistructured data. Databases jargon.
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
Data visualization is crucial to understanding the big data being generated by apps and services. Data visualization toolkits such as D3.js and charting toolkits are immensely popular but it remains difficult to create meaningful dashboards or usable analytics tools or clear data visualizations. This talk will discuss data visualization principles, present best practices, showcase excellent visualizations in practice, and share useful tips and mistakes learned.
Visual communication of qualitative and quantitative data (v. 2021 ITA)Frieda Brioschi
Visual systems and preattentive attributes. Quantitative data visualization, chart selector. Some useful tactics. Qualitative data definition and examples. Qualitative metaphors. Data visualization & journalism. Common kinds: mind maps, flow diagrams, words cloud, user journey, tube map, maps. Qualitative chart chooser.
Data centric business and knowledge graph trendsAlan Morrison
The deck for my kickoff keynote at the Data-Centric Architecture Forum, February 3, 2020. Includes related data, content, and architecture definitions and fundamental explanations, knowledge graph trends, market outlook, transformation case studies and benefits of large-scale, cross-boundary integration/interoperation.
RECOMMENDED ELEMENTS OF INFOGRAPHICS IN EDUCATION (PROGRAMMING FOCUSED) ijma
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Data visualization is a technique that converts complex data into simple, crisp and strikingly interactive images that present the required information instead of long and boring texts. These visual objects include infographic, dials and gauges, geographic, maps, detailed bar, sparklines, heat maps, pie, fever charts etc.
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This introduction was presented as part of a workshop at the Measurement and Accountability for Results in Health Summit at the World Bank (June 2015). The workshop focused on simple ways anyone working with data can improve their presentations, and included visualization redesign activity to put these principles in practice.
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Visual communication of qualitative data (v. 2020 ITA)
1. data & content design
Frieda Brioschi - frieda.brioschi@gmail.com
Emma Tracanella - emma.tracanella@gmail.com
VISUAL COMMUNICATION OF
QUALITATIVE DATA
LESSON 9 - 2020
7. data & content design
LESSON 9
DEFINITION
7
A data visualization is a visual representation of data created to amplify the
cognitive processing and the social application of the data represented (Borgo/
Cairo 2013).
The main division line on the content side is whether the data that are visualized
are numerical o non-numerical.
Non-numerical data might refer to collections of documents, network relations,
topographical structures, etc.
8. data & content design
LESSON 9
MAIN SOURCES OF NON-NUMERICAL DATA COLLECTION
8
10. data & content design
LESSON 9
GOAL
The most common sources of qualitative data include interviews, observations,
and documents, none of which can be “crunched” easily by statistical software.
The goal of qualitative data analysis is to uncover emerging themes, patterns,
concepts, insights, and understandings. Qualitative studies often use an analytic
framework — a network of linked concepts and classifications — to understand an
underlying process; that is, a sequence of events or constructs and how they
relate.
▸ https://www.sagepub.com/sites/default/files/upm-binaries/43144_12.pdf
10
11. data & content design
LESSON 9
QUALITATIVE DATA ANALYSIS
Data analysis in qualitative research focuses on qualities more than quantities.
The statistical focus on the p value in quantitative research is replaced in
qualitative research with pattern seeking and the extraction of meaning from rich,
complex sources of linguistic (narrative) or visual (image) data.
Much effort is directed toward the creation of categories. Words, symbols,
metaphors, vignettes, and an entire array of creative linguistic tools or visual
displays may be used instead of the “number crunching” employed in qualitative
data analysis.
▸ https://www.sagepub.com/sites/default/files/upm-binaries/43144_12.pdf
11
12. data & content design
LESSON 9
CREATIVE THINKING
The types of thinking and skills needed for qualitative data analysis are different
from those needed for quantitative data analysis. Creativity, divergent thinking,
keen perception of patterns among ambiguity, and strong writing skills are
helpful for qualitative data analysis.
Qualitative analysis is less dependent on computing software. Whereas statistical
analysis often centers on the p value, qualitative data analysis involves more time-
consuming extraction of meaning from multiple sources of complex data.
▸ https://www.sagepub.com/sites/default/files/upm-binaries/43144_12.pdf
12
13. data & content design
LESSON 9
THE QUALITATIVE METAPHORS
Qualitative data analysts face the task of recording data via a variety of methods
(interviews, observation, field notes, etc.), coding and categorizing (using a
variety of clustering and classification schemes), attaching concepts to the
categories, linking and combining (integrating) abstract concepts, creating theory
from emerging themes, and writing an understanding.
Metaphors are useful as interpretive tools in this process, serving a heuristic
(guiding) role or explaining the elements of a theory.
▸ https://www.sagepub.com/sites/default/files/upm-binaries/43144_12.pdf
13
14. data & content design
LESSON 9
KALEIDOSCOPE
One useful metaphor is a kaleidoscope for the
purpose of describing qualitative data analysis.
Grouping similar data bits together, then
comparing bits within a pile. Differentiation
creates subpiles, which eventually become
connected by a pattern they share. This process
requires continual “back and forth” refinement
until a grand concept emerges.
▸ https://www.sagepub.com/sites/default/files/upm-binaries/
43144_12.pdf
14
15. data & content design
LESSON 9
JIGSAW PUZZLE
Assembling data into an explanation is akin to reassembling puzzle
pieces. One strategy is grouping all pieces that look alike, sky for
example, and placing these pieces near the top. Other sketchy-looking
objects may be grouped together using any dimension (e.g., color)
whose properties make conceptual sense.
Puzzle pieces will have to be rearranged many times before the
reassembled pieces emerge into a coherent pattern. If successful, a
whole structure will eventually be built, held tight by the interconnected
pieces.
▸ https://www.sagepub.com/sites/default/files/upm-binaries/43144_12.pdf
15
16. data & content design
LESSON 9
SYMPHONY
Qualitative data analysis is best understand as a symphony based on three elegant
but simple notes: noticing, collecting, and thinking. Clearly not linear, the process is
described as iterative (a repeating cycle), recursive (returning to a previous point),
and “holographic” (each “note” contains a whole) with “swirls and eddies.”
When one notices, one records information and codes it using an organizing
framework. When one collects, one shifts and sorts information. When one thinks,
one finds patterns, makes sense of them, and makes discoveries (including
“wholes” and “holes”).
▸ https://www.sagepub.com/sites/default/files/upm-binaries/43144_12.pdf
16
17. data & content design
LESSON 9
DATA VISUALIZATION AS A TOOL
Data visualization can be a powerful tool in qualitative reporting. While we
certainly can’t completely escape text-centric pages in our qualitative reports,
graphics add visual interest and help break up the monotony of pages (or slides)
of text.
Graphics help support qualitative findings and enable us to communicate in
more interesting ways beyond words on paper (or a screen). Effective data
visualization can also help readers understand concepts more quickly and easily
and make information more memorable.
17
https://www.qrca.org/blogpost/1488356/323845/Data-Visualization-3-Ways-to-Make-Your-Qualitative-Reports-Pop
18. data & content design
LESSON 9
DATA VISUALIZATION IN JOURNALISM
Newspapers and other media outlets have jumped on board the data
visualization.
Publications like The Washington Post, The New York Times and the Los
Angeles Times employ full-time data journalists to augment their reporting.
These folks take an enormous trove of data on a particular topic and expertly
slice, dice and manipulate the information into interactive graphics that
communicate big ideas in an accessible and elegant way.
18
19. data & content design
LESSON 9
THE CONFIRMED U.S. MEASLES CASES BY COUNTY IN 2019
19
https://www.nytimes.com/interactive/2019/health/measles-outbreak.html
20. data & content design
LESSON 9
THE EARLIER START OF SPRING IN SOME PARTS OF THE U.S.
20
https://www.washingtonpost.com/graphics/2018/national/early-spring/
21. data & content design
LESSON 9
THE EARLIER START OF SPRING IN SOME PARTS OF THE U.S.
21
https://www.washingtonpost.com/graphics/2018/national/early-spring/
23. data & content design
LESSON 9
MIND MAP
A mind map is a hierarchical diagram used to visually organize information,
showing relationships among pieces of the whole.
It is often created around a single concept, drawn as an image in the center of a
blank page, to which associated representations of ideas such as images, words
and parts of words are added. Major ideas are connected directly to the central
concept, and other ideas branch out from those major ideas.
▸ https://en.wikipedia.org/wiki/Mind_map
23
24. data & content design
LESSON 9
BUZAN’S GUIDELINES
1. Start in the center with an image of the topic, using at least 3 colors.
2. Use images, symbols, codes, and dimensions throughout your mind map.
3. Select key words and print using upper or lower case letters.
4. Each word/image is best alone and sitting on its own line.
5. The lines should be connected, starting from the central image. The lines
become thinner as they radiate out from the center.
24
25. data & content design
LESSON 9
BUZAN’S GUIDELINES
6. Make the lines the same length as the word/image they support.
7. Use multiple colors throughout the mind map, for visual stimulation and also for
encoding or grouping.
8. Develop your own personal style of mind mapping.
9. Use emphasis and show associations in your mind map.
10.Keep the mind map clear by using radial hierarchy or outlines to embrace your
branches.
▸ https://en.wikipedia.org/wiki/Mind_map
25
26. data & content design
LESSON 8
26https://www.mindmeister.com/blog/why-mind-mapping/
28. data & content design
LESSON 9
FLOW DIAGRAM
A flow diagram is a diagram that visually
displays interrelated information such as
events, steps in a process, functions, etc., in
an organized fashion, such as sequentially or
chronologically.
▸ https://books.google.com/books?
id=qusmDAAAQBAJ&printsec=frontcover#v=onepage&q=%2
2flow%20diagram%22&f=false
28
30. data & content design
LESSON 9
30https://kallwejt.com/filter/Baltimore/Baltimore-Waste-1
31. data & content design
LESSON 9
CUSTOMER JOURNEY MAPS
31
Customer journey maps are
another way to employ data
visualization in qualitative
reports.
Is a way of walking through a
process or service, from the
perspective of someone who is
interacting with it.
32. data & content design
LESSON 7
32
https://neiltamplin.me/an-example-customer-journey-map-for-a-housing-association-22b3719dcc10
33. data & content design
LESSON 9
33
https://www.brightvessel.com/customer-journey-map-2018/
34. data & content design
LESSON 9
WORD CLOUDS
The most obvious strategy for visualizing text-based data: the word
cloud, also known as a tag cloud.
Frequent words or phrases are shown in larger, bolder font.
Less-frequent words or phrases are shown in a smaller font.
Word clouds are okay for visualizing one-word descriptions, but not
for visualizing all your qualitative data.
34
35. data & content design
LESSON 9
ONE WORD FOR TEACHER
35
People described
their favorite teacher
using only one word
and the adjectives
were visualized in a
word cloud shaped
like an apple.
36. data & content design
LESSON 9
BEFORE AFTER COMPARISON
36
Word clouds are also
great for before/after
comparisons, like
these tweets
describing breakups.
https://www.vice.com/en_us/article/ezvaba/what-our-breakups-look-like-on-twitter
37. data & content design
LESSON 9
ONE WORD FOR OBAMA
37
People described
Barack Obama using
only one word and
the adjectives were
visualized in a bubble
cloud (and then color-
coded by the
sentiment or tone of
that adjective).
38. data & content design
LESSON 9
COLOR-CODED PHRASES
38
The New York Times’
election coverage in
2016 compared and
contrasted speeches
from Donald Trump
and Hillary Clinton.
First, the New York
Times team presented
miniature thumbnail
images of each
nominee’s convention
speech.
https://www.nytimes.com/interactive/2016/07/29/us/elections/trump-clinton-pence-kaine-speeches.html
39. data & content design
LESSON 9
COLOR-CODED PHRASES
39
Directly underneath the
thumbnails, the New
York Times team pulled
out a few sample
quotes so that readers
can get a sense of what
was said.
40. data & content design
LESSON 9
MAPS
Maps are artifacts that help us make decisions, in so much as they visually
organize data and information on a space; their aim is to make what they see
comprehensible and usable, to bring it to our knowledge.
The language of maps, in particular, is a circular course that starts from
humanity’s need to explore its surroundings by sharing information, and ending
with the need to plan and shape the reality in which it is immersed. Observation,
abstraction and landing are processes that take place in the ends of those who
perform them.
▸ https://it.moleskine.com/mind-maps-and-infographics/p0198
40
41. data & content design
A MAP IS NOT THE TERRITORY IT REPRESENTS, BUT
IF CORRECT, IT HAS SIMILAR STRUCTURE TO THE
TERRITORY, WHICH ACCOUNTS FOR ITS
USEFULNESS
Alfred Korzybski
LESSON 9
41
42. data & content design
LESSON 9
TUBE MAP
The first diagrammatic map of London's rapid transit network was designed by Harry Beck in
1931. Beck was a London Underground employee who realised that because the railway ran
mostly underground, the physical locations of the stations were largely irrelevant to the
traveller wanting to know how to get from one station to another — only the topology of the
route mattered.
To this end, Beck devised a simplified map, consisting of stations, straight line segments
connecting them, and the River Thames; lines ran only vertically, horizontally, or on 45-degree
diagonals. To make the map clearer and to emphasise connections, Beck differentiated
between ordinary stations (marked with tick marks) and interchange stations (marked with
diamonds).
▸ https://en.wikipedia.org/wiki/Tube_map
42
46. data & content design
LESSON 7
46
Maps, by Mizielinskas Mizielinski
47. data & content design
LESSON 7
47
Maps, by Mizielinskas Mizielinski
48. data & content design
LESSON 9
48
Maps, by Mizielinskas Mizielinski
CC-BY-NC xkcd,
https://xkcd.com/256/
49. data & content design
LESSON 7
49
Maps, by Mizielinskas Mizielinski
CC-BY-NC xkcd,
https://xkcd.com/802/
50. data & content design
LESSON 9
50
Maps, by Mizielinskas Mizielinski
CC-BY-NC xkcd,
https://xkcd.com/802/
51. data & content design
LESSON 9
BUBBLE GRAPH
51
During the 2012 London
Olympics, The New York
Times kept a running medal
count by country and visualized
the data in a simple table
52. data & content design
LESSON 9
BUBBLE GRAPH
52
The Times formatted the same information into a bubble graph. This approach
does a much better job conveying magnitude.
https://www.nytimes.com/interactive/projects/london2012/results