Data Visualization Quotes

Above all else show the data.

If the statistics are boring, then you've got the wrong numbers.

Allowing artist-illustrators to control the design and content of statistical graphics is almost like allowing typographers to control the content, style, and editing of prose.

The purpose of infographics and data visualizations is to enlighten people—not to entertain them, not to sell them products, services, or ideas, but to inform them. It’s as simple—and as complicated—as that.

Perception requires imagination because the data people encounter in their lives are never complete and always equivocal. For example, most people consider that the greatest evidence of an event one can obtain is to see it with their own eyes, and in a court of law little is held in more esteem than eyewitness testimony. Yet if you asked to display for a court a video of the same quality as the unprocessed data catptured on the retina of a human eye, the judge might wonder what you were tryig to put over. For one thing, the view will have a blind spot where the optic nerve attaches to the retina. Moreover, the only part of our field of vision with good resolution is a narrow area of about 1 degree of visual angle around the retina’s center, an area the width of our thumb as it looks when held at arm’s length. Outside that region, resolution drops off sharply. To compensate, we constantly move our eyes to bring the sharper region to bear on different portions of the scene we wish to observe. And so the pattern of raw data sent to the brain is a shaky, badly pixilated picture with a hole in it. Fortunately the brain processes the data, combining input from both eyes, filling in gaps on the assumption that the visual properties of neighboring locations are similar and interpolating. The result - at least until age, injury, disease, or an excess of mai tais takes its toll - is a happy human being suffering from the compelling illusion that his or her vision is sharp and clear. We also use our imagination and take shortcuts to fill gaps in patterns of nonvisual data. As with visual input, we draw conclusions and make judgments based on uncertain and incomplete information, and we conclude, when we are done analyzing the patterns, that out “picture” is clear and accurate. But is it?

Having all the information in the world at our fingertips doesn’t make it easier to communicate: it makes it harder.

between the beginning of time and 2003, humanity generated roughly five exabytes of data, whereas we now produce the same volume of bits every two days.

A data visualization should only be beautiful when beauty can promote understanding in some way without undermining it in another. Is beauty sometimes useful? Certainly. Is beauty always useful? Certainly not.

We do not need to plug a fiber optic cable into our brains in order to access the Internet. Not only can the human retina transmit data at an impressive rate of nearly 10 million bits per second, but it comes pre-packaged with a massive amount of dedicated wetware, the visual cortex, that is highly adapted to extracting meaning from this information torrent and to interfacing with other brain areas for further processing.

Investors look at economic fundamentals; traders look at each other; ‘quants’ look at the data. Dealing on the basis of historic price series was once described as technical analysis, or chartism (and there are chartists still). These savants identify visual patterns in charts of price data, often favouring them with arresting names such as ‘head and shoulders’ or ‘double bottoms’. This is pseudo-scientific bunk, the financial equivalent of astrology. But more sophisticated quantitative methods have since proved profitable for some since the 1970s’ creation of derivative markets and the related mathematics. Profitable

Using a table in a live presentation is rarely a good idea. As your audience reads it, you lose their ears and attention to make your point verbally. When

It is in the combination of words and visuals that the magic of understanding often happens.

When you have just a number or two that you want to communicate: use the numbers directly.

The life of a visual communicator should be one of systematic and exciting intellectual chaos.

You should always want your audience to know or do something. If you can't concisely articulate that, you should revisit whether you need to communicate in the first place.

You know you’ve achieved perfection, not when you have nothing more to add, but when you have nothing to take away

Alberto Cairo has identified four common features of a good data visualization: It contains reliable information. The design has been chosen so that relevant patterns become noticeable. It is presented in an attractive manner, but appearance should not get in the way of honesty, clarity and depth. When appropriate, it is organized in a way that enables some exploration.

In April 2017, a confidential document is leaked that reveals Facebook is offering advertisers the opportunity to target thirteen-to-seventeen-year-olds across its platforms, including Instagram, during moments of psychological vulnerability when they feel “worthless,” “insecure,” “stressed,” “defeated,” “anxious,” “stupid,” “useless,” and “like a failure.” Or to target them when they’re worried about their bodies and thinking of losing weight. Basically, when a teen is in a fragile emotional state. Facebook’s advertising team had made this presentation for an Australian client that explains that Instagram and Facebook monitor teenagers’ posts, photos, interactions, conversations with friends, visual communications, and internet activity on and off Facebook’s platforms and use this data to target young people when they’re vulnerable. In addition to the moments of vulnerability listed, Facebook finds moments when teenagers are concerned with “body confidence” and “working out & losing weight.

PowerPoint presentations, the cesspool of data visualization that Microsoft has visited upon the earth. PowerPoint, indeed, is a cautionary tale in our emerging data literacy. It shows that tools matter: Good ones help us think well and bad ones do the opposite. Ever since it was first released in 1990, PowerPoint has become an omnipresent tool for showing charts and info during corporate presentations.

Will you be encountering each other for the first time through this communication, or do you have an established relationship? Do they already trust you as an expert, or do you need to work to establish credibility? These are important considerations when it comes to determining how to structure your communication and whether and when to use data, and may impact the order and flow of the overall story you aim to tell.

The inadequacy of unidimensional plotting along a continuum (in this case the diagonal of a symmetric matrix) inevitably would make "buffer" elements appear non-conformist when in fact they may be part of an interconnected pattern.

Part Three, that part of formal scientific method called experimentation, is sometimes thought of by romantics as all of science itself because that’s the only part with much visual surface. They see lots of test tubes and bizarre equipment and people running around making discoveries. They do not see the experiment as part of a larger intellectual process and so they often confuse experiments with demonstrations, which look the same. A man conducting a gee-whiz science show with fifty thousand dollars’ worth of Frankenstein equipment is not doing anything scientific if he knows beforehand what the results of his efforts are going to be. A motorcycle mechanic, on the other hand, who honks the horn to see if the battery works is informally conducting a true scientific experiment. He is testing a hypothesis by putting the question to nature. The TV scientist who mutters sadly, “The experiment is a failure; we have failed to achieve what we had hoped for,” is suffering mainly from a bad scriptwriter. An experiment is never a failure solely because it fails to achieve predicted results. An experiment is a failure only when it also fails adequately to test the hypothesis in question, when the data it produces don’t prove anything one way or another.

If you need to visualize the soul, think of it as a cross between a wolf howl, a photon, and a dribble of dark molasses. But what it really is, as near as I can tell, is a packet of information. It’s a program, a piece of hyperspatial software designed explicitly to interface with the Mystery. Not a mystery, mind you, the Mystery. The one that can never be solved. To one degree or another, everybody is connected to the Mystery, and everybody secretly yearns to expand the connection. That requires expanding the soul. These things can enlarge the soul: laughter, danger, imagination, meditation, wild nature, passion, compassion, psychedelics, beauty, iconoclasm, and driving around in the rain with the top down. These things can diminish it: fear, bitterness, blandness, trendiness, egotism, violence, corruption, ignorance, grasping, shining, and eating ketchup on cottage cheese. Data in our psychic program is often nonlinear, nonhierarchical, archaic, alive, and teeming with paradox. Simply booting up is a challenge, if not for no other reason than that most of us find acknowledging the unknowable and monitoring its intrusions upon the familiar and mundane more than a little embarrassing. But say you’ve inflated your soul to the size of a beach ball and it’s soaking into the Mystery like wine into a mattress. What have you accomplished? Well, long term, you may have prepared yourself for a successful metamorphosis, an almost inconceivable transformation to be precipitated by your death or by some great worldwide eschatological whoopjamboreehoo. You may have. No one can say for sure. More immediately, by waxing soulful you will have granted yourself the possibility of ecstatic participation in what the ancients considered a divinely animated universe. And on a day to day basis, folks, it doesn’t get any better than that.

Vision isn’t about photons that can be readily interpreted by the visual cortex. Instead it’s a whole body experience. The signals coming into the brain can only be made sense of by training, which requires cross-referencing the signals with information from our actions and sensory consequences. It’s the only way our brains can come to interpret what the visual data actually means.

Warning: This data storage unit, or "book," has been designed to reprogram the human brain, allowing it to replicate the lost art that was once called reading. It is a simple adjustment and there will be no negative or harmful effects from this process. What you are doing: "Reading" Explained Each sheet is indelibly printed with information and the sheets are visually scanned from left to right, and from top to bottom. This scanned information is passed through the visual cortex directly into the brain, where it can then be accessed just like any other data.

Wild animals enjoying one another and taking pleasure in their world is so immediate and so real, yet this reality is utterly absent from textbooks and academic papers about animals and ecology. There is a truth revealed here, absurd in its simplicity. This insight is not that science is wrong or bad. On the contrary: science, done well, deepens our intimacy with the world. But there is a danger in an exclusively scientific way of thinking. The forest is turned into a diagram; animals become mere mechanisms; nature's workings become clever graphs. Today's conviviality of squirrels seems a refutation of such narrowness. Nature is not a machine. These animals feel. They are alive; they are our cousins, with the shared experience kinship implies. And they appear to enjoy the sun, a phenomenon that occurs nowhere in the curriculum of modern biology. Sadly, modern science is too often unable or unwilling to visualize or feel what others experience. Certainly science's "objective" gambit can be helpful in understanding parts of nature and in freeing us from some cultural preconceptions. Our modern scientific taste for dispassion when analyzing animal behaviour formed in reaction to the Victorian naturalists and their predecessors who saw all nature as an allegory confirming their cultural values. But a gambit is just an opening move, not a coherent vision of the whole game. Science's objectivity sheds some assumptions but takes on others that, dressed up in academic rigor, can produce hubris and callousness about the world. The danger comes when we confuse the limited scope of our scientific methods with the true scope of the world. It may be useful or expedient to describe nature as a flow diagram or an animal as a machine, but such utility should not be confused with a confirmation that our limited assumptions reflect the shape of the world. Not coincidentally, the hubris of narrowly applied science serves the needs of the industrial economy. Machines are bought, sold, and discarded; joyful cousins are not. Two days ago, on Christmas Eve, the U.S. Forest Service opened to commercial logging three hundred thousand acres of old growth in the Tongass National Forest, more than a billion square-meter mandalas. Arrows moved on a flowchart, graphs of quantified timber shifted. Modern forest science integrated seamlessly with global commodity markets—language and values needed no translation. Scientific models and metaphors of machines are helpful but limited. They cannot tell us all that we need to know. What lies beyond the theories we impose on nature? This year I have tried to put down scientific tools and to listen: to come to nature without a hypothesis, without a scheme for data extraction, without a lesson plan to convey answers to students, without machines or probes. I have glimpsed how rich science is but simultaneously how limited in scope and in spirit. It is unfortunate that the practice of listening generally has no place in the formal training of scientists. In this absence science needlessly fails. We are poorer for this, and possibly more hurtful. What Christmas Eve gifts might a listening culture give its forests? What was the insight that brushed past me as the squirrels basked? It was not to turn away from science. My experience of animals is richer for knowing their stories, and science is a powerful way to deepen this understanding. Rather, I realized that all stories are partly wrapped in fiction—the fiction of simplifying assumptions, of cultural myopia and of storytellers' pride. I learned to revel in the stories but not to mistake them for the bright, ineffable nature of the world.

Hildebrand, too, challenged the ideals of scientific naturalism by an appeal to the psychology of perception: if we attempt to analyze our mental images to discover their primary constituents, we will find them composed of sense data derived from vision and from memories of touch and movement. A sphere, for instance, appears to the eye as a flat disk; it is touch which informs us of the properties of space and form. Any attempt on the part of the artist to eliminate this knowledge is futile, for without it he would not perceive the world at all. His task is, on the contrary, to compensate for the absence of movement in his work by clarifying his image and thus conveying not only visual sensations but also those memories of touch which enable us to reconstitute the three-dimensional form in our minds.

Clients recognize excessive self-orientation through such things as: 1. A tendency to relate their stories to ourselves 2. A need to too quickly finish their sentences for them 3. A need to fill empty spaces in conversations 4. A need to appear clever, bright, witty, etc. 5. An inability to provide a direct answer to a direct question 6. An unwillingness to say we don’t know 7. Name-dropping of other clients 8. A recitation of qualifications 9. A tendency to give answers too quickly 10. A tendency to want to have the last word 11. Closed-ended questions early on 12. Putting forth hypotheses or problem statements before fully hearing the client’s hypotheses or problem statements 13. Passive listening; a lack of visual and verbal cues that indicate the client is being heard 14. Watching the client as if he/she were a television set (merely a source of data)

Sound waves, regardless of their frequency or intensity, can only be detected by the Mole Fly’s acute sense of smell—it is a little known fact that the Mole Fly’s auditory receptors do not, in fact, have a corresponding center in the brain designated for the purposes of processing sensory stimuli and so, these stimuli, instead of being siphoned out as noise, bypass the filters to be translated, oddly enough, by the part of the brain that processes smell. Consequently, the Mole Fly’s brain, in its inevitable confusion, understands sound as an aroma, rendering the boundary line between the auditory and olfactory sense indistinguishable. Sounds, thus, come in a variety of scents with an intensity proportional to its frequency. Sounds of shorter wavelength, for example, are particularly pungent. What results is a species of creature that cannot conceptualize the possibility that sound and smell are separate entities, despite its ability to discriminate between the exactitudes of pitch, timbre, tone, scent, and flavor to an alarming degree of precision. Yet, despite this ability to hyper-analyze, they lack the cognitive skill to laterally link successions of either sound or smell into a meaningful context, resulting in the equivalent of a data overflow. And this may be the most defining element of the Mole Fly’s behavior: a blatant disregard for the context of perception, in favor of analyzing those remote and diminutive properties that distinguish one element from another. While sensory continuity seems logical to their visual perception, as things are subject to change from moment-to-moment, such is not the case with their olfactory sense, as delays in sensing new smells are granted a degree of normality by the brain. Thus, the Mole Fly’s olfactory-auditory complex seems to be deprived of the sensory continuity otherwise afforded in the auditory senses of other species. And so, instead of sensing aromas and sounds continuously over a period of time—for example, instead of sensing them 24-30 times per second, as would be the case with their visual perception—they tend to process changes in sound and smell much more slowly, thereby preventing them from effectively plotting the variations thereof into an array or any kind of meaningful framework that would allow the information provided by their olfactory and auditory stimuli to be lasting in their usefulness. The Mole flies, themselves, being the structurally-obsessed and compulsive creatures that they are, in all their habitual collecting, organizing, and re-organizing of found objects into mammoth installations of optimal functional value, are remarkably easy to control, especially as they are given to a rather false and arbitrary sense of hierarchy, ascribing positions—that are otherwise trivial, yet necessarily mundane if only to obscure their true purpose—with an unfathomable amount of honor, to the logical extreme that the few chosen to serve in their most esteemed ranks are imbued with a kind of obligatory arrogance that begins in the pupal stages and extends indefinitely, as they are further nurtured well into adulthood by a society that infuses its heroes of middle management with an immeasurable sense of importance—a kind of celebrity status recognized by the masses as a living embodiment of their ideals. And yet, despite this culture of celebrity worship and vicarious living, all whims and impulses fall subservient, dropping humbly to the knees—yes, Mole Flies do, in fact, have knees!—before the grace of the merciful Queen, who is, in actuality, just a puppet dictator installed by the Melic papacy, using an old recycled Damsel fly-fishing lure. The dummy is crude, but convincing, as the Mole flies treat it as they would their true-born queen.

Galileo's mechanical world was only a partial representation of a finite number of probable worlds, each peculiar to a particular living species; and all these worlds are but a portion of the infinite number of possible worlds that may have once existed or may yet exist. But anything like a single world, common to all species, at all times, under all circumstances, is a purely hypothetical construction, drawn by inference from pathetically insufficient data, prized for the assurance of stability and intelligibility it gives, even though that assurance turns out, under severe examination, to be just another illusion. A butterfly or a beetle, a fish or a fowl, a dog or a dolphin, would have a different report to give even about primary qualities, for each lives in a world conditioned by the needs and environmental opportunities open to his species. In the gray visual world of the dog, smells, near and distant, subtle or violently exciting, probably play the part that colors do in man's world-though in the primal occupation of eating, the dog's world and man's world would approach each other more closely.

Atonement, expiation, laundering, prophylaxis, promotion and rehabilitation -- it is difficult to put a name to all the various nuances of this general commiseration which is the product of a profound indifference and is accompanied by a fierce strategy of blackmail, of the political takeover of all these negative passions. It is the `politically correct' in all its effects -- an enterprise of laundering and mental prophylaxis, beginning with the prophylactic treatment of language. Black people, the handicapped, the blind and prostitutes become `people of colour', `the disabled', `the visually impaired', and `sex workers': they have to be laundered like dirty money. Every negative destiny has to be cleaned up by a doctoring even more obscene than what it is trying to hide. Euphemistic language, the struggle against sexual harassment -- all this protective and protectionist masquerade is of the same order as the use of the condom. Its mental use, of course -- that is, the prophylactic use of ideas and concepts. Soon we shall think only when we are sheathed in latex. And the data suit of Virtual Reality already slips on like a condom.

The motor activities we take for granted—getting out of a chair and walking across a room, picking up a cup and drinking coffee,and so on—require integration of all the muscles and sensory organs working smoothly together to produce coordinated movements that we don't even have to think about. No one has ever explained how the simple code of impulses can do all that. Even more troublesome are the higher processes, such as sight—in which somehow we interpret a constantly changing scene made of innumerable bits of visual data—or the speech patterns, symbol recognition, and grammar of our languages.Heading the list of riddles is the "mind-brain problem" of consciousness, with its recognition, "I am real; I think; I am something special." Then there are abstract thought, memory, personality,creativity, and dreams. The story goes that Otto Loewi had wrestled with the problem of the synapse for a long time without result, when one night he had a dream in which the entire frog-heart experiment was revealed to him. When he awoke, he knew he'd had the dream, but he'd forgotten the details. The next night he had the same dream. This time he remembered the procedure, went to his lab in the morning, did the experiment, and solved the problem. The inspiration that seemed to banish neural electricity forever can't be explained by the theory it supported! How do you convert simple digital messages into these complex phenomena? Latter-day mechanists have simply postulated brain circuitry so intricate that we will probably never figure it out, but some scientists have said there must be other factors.

Moore’s Law, the rule of thumb in the technology industry, tells us that processor chips—the small circuit boards that form the backbone of every computing device—double in speed every eighteen months. That means a computer in 2025 will be sixty-four times faster than it is in 2013. Another predictive law, this one of photonics (regarding the transmission of information), tells us that the amount of data coming out of fiber-optic cables, the fastest form of connectivity, doubles roughly every nine months. Even if these laws have natural limits, the promise of exponential growth unleashes possibilities in graphics and virtual reality that will make the online experience as real as real life, or perhaps even better. Imagine having the holodeck from the world of Star Trek, which was a fully immersive virtual-reality environment for those aboard a ship, but this one is able to both project a beach landscape and re-create a famous Elvis Presley performance in front of your eyes. Indeed, the next moments in our technological evolution promise to turn a host of popular science-fiction concepts into science facts: driverless cars, thought-controlled robotic motion, artificial intelligence (AI) and fully integrated augmented reality, which promises a visual overlay of digital information onto our physical environment. Such developments will join with and enhance elements of our natural world. This is our future, and these remarkable things are already beginning to take shape. That is what makes working in the technology industry so exciting today. It’s not just because we have a chance to invent and build amazing new devices or because of the scale of technological and intellectual challenges we will try to conquer; it’s because of what these developments will mean for the world.

Dr. Hobson (with Dr. Robert McCarley) made history by proposing the first serious challenge to Freud’s theory of dreams, called the “activation synthesis theory.” In 1977, they proposed the idea that dreams originate from random neural firings in the brain stem, which travel up to the cortex, which then tries to make sense of these random signals. The key to dreams lies in nodes found in the brain stem, the oldest part of the brain, which squirts out special chemicals, called adrenergics, that keep us alert. As we go to sleep, the brain stem activates another system, the cholinergic, which emits chemicals that put us in a dream state. As we dream, cholinergic neurons in the brain stem begin to fire, setting off erratic pulses of electrical energy called PGO (pontine-geniculate-occipital) waves. These waves travel up the brain stem into the visual cortex, stimulating it to create dreams. Cells in the visual cortex begin to resonate hundreds of times per second in an irregular fashion, which is perhaps responsible for the sometimes incoherent nature of dreams. This system also emits chemicals that decouple parts of the brain involved with reason and logic. The lack of checks coming from the prefrontal and orbitofrontal cortices, along with the brain becoming extremely sensitive to stray thoughts, may account for the bizarre, erratic nature of dreams. Studies have shown that it is possible to enter the cholinergic state without sleep. Dr. Edgar Garcia-Rill of the University of Arkansas claims that meditation, worrying, or being placed in an isolation tank can induce this cholinergic state. Pilots and drivers facing the monotony of a blank windshield for many hours may also enter this state. In his research, he has found that schizophrenics have an unusually large number of cholinergic neurons in their brain stem, which may explain some of their hallucinations. To make his studies more efficient, Dr. Allan Hobson had his subjects put on a special nightcap that can automatically record data during a dream. One sensor connected to the nightcap registers the movements of a person’s head (because head movements usually occur when dreams end). Another sensor measures movements of the eyelids (because REM sleep causes eyelids to move). When his subjects wake up, they immediately record what they dreamed about, and the information from the nightcap is fed into a computer. In this way, Dr. Hobson has accumulated a vast amount of information about dreams. So what is the meaning of dreams? I asked him. He dismisses what he calls the “mystique of fortune-cookie dream interpretation.” He does not see any hidden message from the cosmos in dreams. Instead, he believes that after the PGO waves surge from the brain stem into the cortical areas, the cortex is trying to make sense of these erratic signals and winds up creating a narrative out of them: a dream.

To give you a sense of the sheer volume of unprocessed information that comes up the spinal cord into the thalamus, let’s consider just one aspect: vision, since many of our memories are encoded this way. There are roughly 130 million cells in the eye’s retina, called cones and rods; they process and record 100 million bits of information from the landscape at any time. This vast amount of data is then collected and sent down the optic nerve, which transports 9 million bits of information per second, and on to the thalamus. From there, the information reaches the occipital lobe, at the very back of the brain. This visual cortex, in turn, begins the arduous process of analyzing this mountain of data. The visual cortex consists of several patches at the back of the brain, each of which is designed for a specific task. They are labeled V1 to V8. Remarkably, the area called V1 is like a screen; it actually creates a pattern on the back of your brain very similar in shape and form to the original image. This image bears a striking resemblance to the original, except that the very center of your eye, the fovea, occupies a much larger area in V1 (since the fovea has the highest concentration of neurons). The image cast on V1 is therefore not a perfect replica of the landscape but is distorted, with the central region of the image taking up most of the space. Besides V1, other areas of the occipital lobe process different aspects of the image, including: •  Stereo vision. These neurons compare the images coming in from each eye. This is done in area V2. •  Distance. These neurons calculate the distance to an object, using shadows and other information from both eyes. This is done in area V3. •  Colors are processed in area V4. •  Motion. Different circuits can pick out different classes of motion, including straight-line, spiral, and expanding motion. This is done in area V5. More than thirty different neural circuits involved with vision have been identified, but there are probably many more. From the occipital lobe, the information is sent to the prefrontal cortex, where you finally “see” the image and form your short-term memory. The information is then sent to the hippocampus, which processes it and stores it for up to twenty-four hours. The memory is then chopped up and scattered among the various cortices. The point here is that vision, which we think happens effortlessly, requires billions of neurons firing in sequence, transmitting millions of bits of information per second. And remember that we have signals from five sense organs, plus emotions associated with each image. All this information is processed by the hippocampus to create a simple memory of an image. At present, no machine can match the sophistication of this process, so replicating it presents an enormous challenge for scientists who want to create an artificial hippocampus for the human brain.

The trends speak to an unavoidable truth. Society's future will be challenged by zoonotic viruses, a quite natural prediction, not least because humanity is a potent agent of change, which is the essential fuel of evolution. Notwithstanding these assertions, I began with the intention of leaving the reader with a broader appreciation of viruses: they are not simply life's pathogens. They are life's obligate partners and a formidable force in nature on our planet. As you contemplate the ocean under a setting sun, consider the multitude of virus particles in each milliliter of seawater: flying over wilderness forestry, consider the collective viromes of its living inhabitants. The stunnig number and diversity of viruses in our environment should engender in us greater awe that we are safe among these multitudes than fear that they will harm us. Personalized medicine will soon become a reality and medical practice will routinely catalogue and weigh a patient's genome sequence. Not long thereafter one might expect this data to be joined by the patient's viral and bacterial metagenomes: the patient's collective genetic identity will be recorded in one printout. We will doubtless discover some of our viral passengers are harmful to our health, while others are protective. But the appreciation of viruses that I hope you have gained from these pages is not about an exercise in accounting. The balancing of benefit versus threat to humanity is a fruitless task. The viral metagenome will contain new and useful gene functionalities for biomedicine: viruses may become essential biomedical tools and phages will continue to optimize may also accelerate the development of antibiotic drug resistance in the post-antibiotic era and emerging viruses may threaten our complacency and challenge our society economically and socially. Simply comparing these pros and cons, however, does not do justice to viruses and acknowledge their rightful place in nature. Life and viruses are inseparable. Viruses are life's complement, sometimes dangerous but always beautiful in design. All autonomous self-sustaining replicating systems that generate their own energy will foster parasites. Viruses are the inescapable by-products of life's success on the planet. We owe our own evolution to them; the fossils of many are recognizable in ERVs and EVEs that were certainly powerful influences in the evolution of our ancestors. Like viruses and prokaryotes, we are also a patchwork of genes, acquired by inheritance and horizontal gene transfer during our evolution from the primitive RNA-based world. It is a common saying that 'beauty is in the eye of the beholder.' It is a natural response to a visual queue: a sunset, the drape of a designer dress, or the pattern of a silk tie, but it can also be found in a line of poetry, a particularly effective kitchen implement, or even the ruthless efficiency of a firearm. The latter are uniquely human acknowledgments of beauty in design. It is humanity that allows us to recognize the beauty in the evolutionary design of viruses. They are unique products of evolution, the inevitable consequence of life, infectious egotistical genetic information that taps into life and the laws of nature to fuel evolutionary invention.

Well before the end of the 20th century however print had lost its former dominance. This resulted in, among other things, a different kind of person getting elected as leader. One who can present himself and his programs in a polished way, as Lee Quan Yu you observed in 2000, adding, “Satellite television has allowed me to follow the American presidential campaign. I am amazed at the way media professionals can give a candidate a new image and transform him, at least superficially, into a different personality. Winning an election becomes, in large measure, a contest in packaging and advertising. Just as the benefits of the printed era were inextricable from its costs, so it is with the visual age. With screens in every home entertainment is omnipresent and boredom a rarity. More substantively, injustice visualized is more visceral than injustice described. Television played a crucial role in the American Civil rights movement, yet the costs of television are substantial, privileging emotional display over self-command, changing the kinds of people and arguments that are taken seriously in public life. The shift from print to visual culture continues with the contemporary entrenchment of the Internet and social media, which bring with them four biases that make it more difficult for leaders to develop their capabilities than in the age of print. These are immediacy, intensity, polarity, and conformity. Although the Internet makes news and data more immediately accessible than ever, this surfeit of information has hardly made us individually more knowledgeable, let alone wiser, as the cost of accessing information becomes negligible, as with the Internet, the incentives to remember it seem to weaken. While forgetting anyone fact may not matter, the systematic failure to internalize information brings about a change in perception, and a weakening of analytical ability. Facts are rarely self-explanatory; their significance and interpretation depend on context and relevance. For information to be transmuted into something approaching wisdom it must be placed within a broader context of history and experience. As a general rule, images speak at a more emotional register of intensity than do words. Television and social media rely on images that inflamed the passions, threatening to overwhelm leadership with the combination of personal and mass emotion. Social media, in particular, have encouraged users to become image conscious spin doctors. All this engenders a more populist politics that celebrates utterances perceived to be authentic over the polished sound bites of the television era, not to mention the more analytical output of print. The architects of the Internet thought of their invention as an ingenious means of connecting the world. In reality, it has also yielded a new way to divide humanity into warring tribes. Polarity and conformity rely upon, and reinforce, each other. One is shunted into a group, and then the group polices once thinking. Small wonder that on many contemporary social media platforms, users are divided into followers and influencers. There are no leaders. What are the consequences for leadership? In our present circumstances, Lee's gloomy assessment of visual media's effects is relevant. From such a process, I doubt if a Churchill or Roosevelt or a de Gaulle can emerge. It is not that changes in communications technology have made inspired leadership and deep thinking about world order impossible, but that in an age dominated by television and the Internet, thoughtful leaders must struggle against the tide.

If Auto Fill is the Japanese throwing star of spreadsheets, then making charts surely is the equivalent of Japanese calligraphy. With just a few clicks of the mouse, it’s possible to turn your raw data into visual presentations that will impress all who come near.

The ability to take data -- to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it -- that’s going to be a hugely important skill in the next decades.

There is some feeling nowadays that reading is not as necessary as it once was. Radio and especially television have taken over many of the functions once served by print, just as photography has taken over functions once served by painting and other graphic arts. Admittedly, television serves some of these functions extremely well; the visual communication of news events, for example, has enormous impact. The ability of radio to give us information while we are engaged in doing other things—for instance, driving a car—is remarkable, and a great saving of time. But it may be seriously questioned whether the advent of modern communications media has much enhanced our understanding of the world in which we live. Perhaps we know more about the world than we used to, and insofar as knowledge is prerequisite to understanding, that is all to the good. But knowledge is not as much a prerequisite to understanding as is commonly supposed. We do not have to know everything about something in order to understand it; too many facts are often as much of an obstacle to understanding as too few. There is a sense in which we moderns are inundated with facts to the detriment of understanding. One of the reasons for this situation is that the very media we have mentioned are so designed as to make thinking seem unnecessary (though this is only an appearance). The packaging of intellectual positions and views is one of the most active enterprises of some of the best minds of our day. The viewer of television, the listener to radio, the reader of magazines, is presented with a whole complex of elements—all the way from ingenious rhetoric to carefully selected data and statistics—to make it easy for him to “make up his own mind” with the minimum of difficulty and effort. But the packaging is often done so effectively that the viewer, listener, or reader does not make up his own mind at all. Instead, he inserts a packaged opinion into his mind, somewhat like inserting a cassette into a cassette player. He then pushes a button and “plays back” the opinion whenever it seems appropriate to do so. He has performed acceptably without having had to think.

This is where Bayes' Theorem comes in and helps us have a clearer picture. By using the theorem, we are forced to look at all data and update our hypothesis with new evidence.

What is Tableau Tableau Software is a highly scalable client-server architecture. It is an application that resides on your computer and used for individuals and publishing data sources as well as workbooks to tableau server. Which is allows for instantaneous insight by transforming data into interactive visualizations? Read from OnlineITGuru at Tableau Online Course

other technologies like data visualization, analytics, high speed communications, and rapid prototyping have augmented the contributions of more abstract and data-driven reasoning, increasing the values of these jobs.” In other words, those with the oracular ability to work with and tease valuable results out of increasingly complex machines will thrive.

The age of the world picture is not concerned with a visual picturing, with mimesis, but rather with a modelling or framing of the world. It is the reduction of the world to data.

I almost always use dark grey for the graph title. This ensures that it stands out, but without the sharp contrast you get from pure black on white (rather, I preserve the use of black for a standout color when I’m not using any other colors). A number of preattentive attributes are employed to draw attention to the “Progress to date” trend: color, thickness of line, presence of data marker and label on the final point, and the size of the corresponding text.

On the horizontal x-axis, we don’t need every single day labeled since we’re more interested in the overall trend, not what happened on a specific day. Because we have data through the 10th day of a 30-day month, I chose to label every 5th day on the x-axis (given that this is days we’re talking about, another potential solution would be to label every 7th day and/or add super-categories of week 1, week 2, etc.). This is one of those cases where there isn’t a single right answer: you should think about the context, the data, and how you want your audience to use the visual and make a deliberate decision in light of those things.

The illusions are fabrications of fragmented data, designed to appease the soul and entice the flesh, even in the real world we are always surrounded by illusions. Mindplant is not that different when you actually think about it. People's misrepresentations, false advertising and false media projections, Visual just created a fake world that's not so different from our own." Spiral explained.

The unique thing you get with a pie chart is the concept of there being a whole and, thus, parts of a whole. But if the visual is difficult to read, is it worth it? In

Many organizations we encounter lament their spreadsheet-driven culture. Every department has its own mechanism for gathering, analyzing, and reporting on its unique data. No consistent “source of truth” exists and data analysts become indispensable because they are the only people in the organization who know how a financial model works, how to access and understand the data sources, and its strengths and weaknesses. People in these organizations wish for a technology solution that could bring all the information together and make it available to all decision makers in interactive, visual dashboards.

Building an airplane was nothing compared to shepherding research through Langley's grueling review process. "Present your case, build, sell it,= so they believe it" --- that was the Langley way. The author of a NACA document --- a technical report was the most comprehensive and exacting, a technical memorandum slightly less formal --- faced a firing squad of four or five people, chosen for their expertise in the topic. After a presentation of findings, the committee, which had read and analyzed the report in advance, let loose a barrage of questions and comments. The committee was brusque, thorough, and relentless in rooting out inaccuracies, inconsistencies, incomprehensible statements, and illogical conclusions obscured by technical gibberish. And that was before subjecting the report to the style, clarity, grammar, and presentation standards that were Pearl Young's legacy, before the addition of the charts and fancy graphics that reduced the data sheet to a coherent, visually persuasive point. A final report might be months, even years, in the making.

Online Course in Big Data Analytics - Training and Placement Our training in big data analytics is a beginner level and advanced level online program so learn concepts of big data technologies with assured placements 361online,com/bigdata/PGP_visualization_asured.php

There are three categories of criteria that an individual must meet in order to be diagnosed with ASD. The categories are listed below along with the typical traits, which may indicate whether the individual needs further assessment: 1.Persistent deficits in social communication and social interaction across contexts, not accounted for by general developmental delays: lack of friends and social life friends often much older or younger mumbling and not completing sentences issues with social rules (such as staring at other people) inability to understand jokes and the benefit of ‘small talk’ introverted (shy) and socially awkward inability to understand other people’s thoughts and feelings uncomfortable in large crowds and noisy places detached and emotionally inexpressive. 2.Restricted, repetitive patterns of behaviour, interests or activities: obsession with ‘special interests’ collecting objects (such as stamps and coins) attachment to routines and rituals ability to focus on a single task for long periods eccentric or unorthodox behaviour non-conformist and distrusting of authority difficulty following illogical conventions attracted to foreign cultures affinity with nature and animals support for victims of injustice, underdogs and scapegoats. 3.Restricted, repetitive patterns of behaviour, interests or activities: inappropriate emotional responses victimised or bullied at school, work and home overthinking and constant logical analysis spending much time alone strange laugh or cackle inability to make direct eye contact when talking highly sensitive to light, sound, taste, smell and touch uncoordinated and clumsy with poor posture difficulty coping with change adept at abstract thinking ability to process data sets logically and notice patterns or trends truthful, naïve and often gullible slow mental processing and vulnerable to mental exhaustion intellectual and ungrounded rather than intuitive and instinctive problems with anxiety and sleeping visual memory.

This turns out to be a huge amount of mental work for a visual thinker with AD/HD. You have to listen, remember, decide, propose, consider, compromise, and plan. All of these require holding a number of thoughts or pieces of data in mind, going back and forth between them, and making decisions based on preferences, cause and effect, and priorities. They take a lot of focus and concentration, they require a very well-functioning active working memory, and they’re tiring. There always will be something to avoid or put off.

Learn Data Science Course at SLA to extract meaningful insights from structured and unstructured data using scientific methods, algorithms, and systematic processes. Get hands-on with popular tools and technologies used to analyze data efficiently. Earn an industry-accredited certificate and placement assistance in our leading Data Science Training Institute in Chennai. Equip yourself with the key concepts of Data Science such as Probability, Statistics, Machine Learning Techniques, Data Analytics Basics, and Data Visualization processes. We are extremely dedicated to serving you better.

There is a story in your data. But your tools don’t know what that story is. That’s where it takes you—the analyst or communicator of the information—to bring that story visually and contextually to life.

This graph shows all the observations together with a line that represents the fitted relationship. As is traditional, the Y-axis displays the dependent variable, which is weight. The X-axis shows the independent variable, which is height. The line is the fitted line. If you enter the full range of height values that are on the X-axis into the regression equation that the chart displays, you will obtain the line shown on the graph. This line produces a smaller SSE than any other line you can draw through these observations. Visually, we see that that the fitted line has a positive slope that corresponds to the positive correlation we obtained earlier. The line follows the data points, which indicates that the model fits the data. The slope of the line equals the coefficient that I circled. This coefficient indicates how much mean weight tends to increase as we increase height. We can also enter a height value into the equation and obtain a prediction for the mean weight. Each point on the fitted line represents the mean weight for a given height. However, like any mean, there is variability around the mean. Notice how there is a spread of data points around the line. You can assess this variability by picking a spot on the line and observing the range of data points above and below that point. Finally, the vertical distance between each data point and the line is the residual for that observation.

See, In India I am going to say as I am Indian citizen, yes there is environmental concerns everywhere in India but they seem to be tiny and can be tackled within 20 years. So either it is exaggerated problem or the real pollution data is not open source i .e - Government is indirectly supporting and/ or hiding monopolies. Because governments focus is only on farming practices where land lords are having too much lands and using mixed system of farming because of unpredictable weather and indeed it does pollute the soil but applying biological remediation will obviously help treat and cleanse them. Why biological remediation is not at all considered? Animal genomics is under ethics, ok understood but microbial genomics, plant genomics? See there is certainly environmental problems from industries that affect farming, But i visualize that it is to eliminate land lords to make complete manu smiriti India. And who polluted farming system, obviously fertilizers and who allowed it? Indian government! before 200 years was there fertilizers in India? Why did they allow it, is it because they wanted pollute it for the money they get from foreign giants! or is it because they wanted to pollute the environment deliberately and then they want to cleanse it so that they get good names and meanwhile while cleansing strategies applied, as a partnership they enter into the system and then they eliminate land holders and make them sudras again manusmiriti concept! Isn't it? Do you know something this manu smiriti concept never much happened in South India, yeah it happened only upto certain level not completely like Uttar Pradesh and Rajasthan. You people have polluted the environment now just pretending to be gods of saving nature and after inturns slowly making manusmiriti India. Yes south has pollution, and we know how to tackle it, we have scientists, we have context specific reasons, we have languages and cultures to protect. Indian law says, every cultures have their own rights to preserve their culture. Yes world is one, I agree, Context specificity always remains same. We have problems yes agreed we resolve it, Indian government as a sovereign country, it your duty to support our work and question only when it is against law, humanism and immorality.

Let us look at the correlation between temperature, humidity and wind speed and all other features. Since the data also contains categorical features, we cannot only use the Pearson correlation coefficient, which only works if both features are numerical. Instead, I train a linear model to predict, for example, temperature based on one of the other features as input. Then I measure how much variance the other feature in the linear model explains and take the square root. If the other feature was numerical, then the result is equal to the absolute value of the standard Pearson correlation coefficient. But this model-based approach of “variance-explained” (also called ANOVA, which stands for ANalysis Of VAriance) works even if the other feature is categorical. The “variance-explained” measure lies always between 0 (no association) and 1 (temperature can be perfectly predicted from the other feature). We calculate the explained variance of temperature, humidity and wind speed with all the other features. The higher the explained variance (correlation), the more (potential) problems with PD plots. The following figure visualizes how strongly the weather features are correlated with other features.

DoppelLab,” a digital platform for combining and visually representing sensor data.15 The idea is to transform any physical space, from the interior of an office building to an entire city, into a “browse-able environment” where you can see and hear everything going on in that space as it flows from thousands or billions or trillions of sensors.

Don’t Make Me Think by Steve Krug is an excellent introduction to web usability. We passionately believe it should be on the school curriculum. Designed for Use by Lukas Mathis is less entertaining than Don’t Make Me Think, but it covers more usability concepts. If this book list seems worryingly short, that’s a testament to how much ground this book covers. The Visual Display of Quantitative Information by Edward R. Tufte contains many examples of complex data shown in beautifully elegant ways. Don’t be put off by its technical-sounding title. It’s fun to read.

1. Plataformas de mercado (marketplace). Listado de startups. 2. Peer to peer (P2P) o economía colaborativa. Listado de startups. 3. Big data (rating, valoraciones/tasaciones, análisis/investigación, geolocalización). Listado de startups. 4. Inversión (Crowdfunding). Listado de startups. 5. Software. Listado de startups. 6. Smart Home o domótica (IoT). Listado de startups. 7. Finanzas (créditos/hipotecas/avales). Listado de startups. 8. Property Management softwares (PMS) y gestión. Listado de startups. 9. Visuales (realidad virtual, realidad aumentada). Listado de startups. 10. Contech (tecnología en la construcción). Listado de startups. 11. Marketing. Listado de startups.

AnyVision is shy about admitting its true role in the West Bank, but digging by NBC News uncovered a project, called Google Ayosh, targeting all Palestinians with the use of big data. AnyVision continues to use the occupation as a vital source to train its systems in the mass surveillance of Palestinians, focusing, it says, on attempts to stop any Palestinian attackers.43 AnyVision is a global company that operates in over forty countries, including Russia, China (Hong Kong), and the US, and in countless locations such as casinos, manufacturing, and even fitness centers. The company changed its name to Oosto in late 2021, and raised US$235 million that year to further develop its AI-enabled surveillance tools. The former head of Mossad, Tamir Pardo, is an advisor and it is staffed by Israel’s intelligence Unit 8200 veterans. It promotes itself as building a world “safer through visual intelligence.” AnyVision so impressed Microsoft that the Seattle software giant briefly invested US$74 million in the company in 2019 before facing a massive backlash. It cut its ties with AnyVision in 2020 due to pressure from the “Palestinian lobby on the Democratic Party,” according to the former head of Israel’s Defense Export Control Agency, though it continues to develop its own facial recognition technology.44 The former Biden administration press secretary Jen Psaki worked for AnyVision as a “crisis communications consultant” and earned at least US$5,000 at some point between leaving the Obama administration in 2017 and starting in the Biden White House.

Agood chart isn’t an illustration but a visual argument,” declares Alberto Cairo near the beginning of his book How Charts Lie.

Behavioral scientists who have conducted studies have discovered that people remember pictures better than they do words most of the time.115 They call this tendency the “picture superiority effect.” Tim points out, “Post-modern society is a world saturated with data. People process approximately one thousand messages a day, digitally and personally. The only hope we have of our message sticking is to insure it contains pictures.”116 The more visual you are, the more memorable you are.

Understanding consumer preferences, market trends, and business opportunities all depend on market research. However, a nuanced approach is required when conducting market research survey in Myanmar. Participation in surveys and the quality of the data can be significantly influenced by cultural norms, beliefs, and practices. The challenges and opportunities of conducting surveys in this one-of-a-kind cultural landscape are brought to light in this article, which examines the intricate connection between culture and market research in Myanmar. Researchers can gain valuable insights for informed decision-making and successful market strategies by comprehending and adapting to Myanmar's cultural nuances. Introduction to market research survey in Myanmar is a country with a lot of culture and tradition that makes it a special place to conduct market research. Understanding the cultural nuances that influence survey participation is essential for businesses trying to comprehend consumer preferences and behaviors in this diverse market. An Overview of Myanmar's Market Research Landscape Market research is rapidly evolving in Myanmar in tandem with the country's economic expansion. In order to gain useful insights from surveys, it is necessary to have a comprehensive comprehension of the cultural dynamics of a population with a wide range of languages and ethnic groups. Understanding How Culture Affects Survey Participation Culture has a big impact on how people respond to market research surveys. Survey response rates can be influenced by interpersonal dynamics, social norms, and traditional beliefs in Myanmar. Cultural Factors That Affect Survey Response Rates People's responses to surveys can be influenced by factors like respect for authority, communal decision-making, and communication styles. The key to maximizing survey participation is recognizing and adapting to these cultural differences. The willingness of respondents to participate in surveys can be influenced by traditional beliefs and practices like face-saving behaviors, hierarchical structures, and superstitions. Researchers can create survey environments that are conducive to honest and valuable feedback by recognizing and respecting these traditional beliefs. Tailoring Survey Designs to Match Cultural Preferences in Myanmar To guarantee the success of market research surveys in Myanmar, survey designs must be adapted to match cultural norms and preferences. In addition to increasing respondent engagement, this strategy encourages inclusivity and a respect for local customs. Adjusting Poll Arrangement for Social Awareness From the language utilized in study inquiries to the visual plan of overview materials, social responsiveness ought to be a core value in forming review surveys. Researchers can increase respondent trust and openness by avoiding potential taboos and including references that are culturally relevant. Respecting local customs, such as greeting rituals, gift-giving practices, and preferred modes of communication, can increase respondents' willingness to participate in surveys by incorporating them into the design of the survey. Researchers can create a more engaging and culturally appropriate research experience by incorporating these elements into survey design. Overcoming Language Barriers in Market Research Surveys Myanmar's language diversity makes conducting market research surveys a significant challenge. Language barriers must be overcome and multilingual survey administration must be promoted in order to ensure effective communication and data collection. Challenges of Myanmar's Language Diversity With over 100 languages spoken there, language barriers can make it hard to take surveys and understand them. Utilizing survey materials that are suitable for a particular language and, if necessary, the services of an interpreter, researchers must overcome these obstacles. The use of bilingual survey

whereas confirmation bias is an automatic tendency to notice data that fit with our beliefs, motivated reasoning is the complementary tendency to scrutinize ideas more carefully if we don’t like them than if we do.”10

Well, that was a huge step. We had an instruction set. We could move data around. We could do conditionals. We could do most of the things a simple chip can do. We had the visual cortex for our display. The auditory cortex for our speakers. The motor cortex for our input. On top of that, we could write any damn software we wanted.

More generally, a data scientist is someone who knows how to extract meaning from and interpret data, which requires both tools and methods from statistics and machine learning, as well as being human. She spends a lot of time in the process of collecting, cleaning, and munging data, because data is never clean. This process requires persistence, statistics, and software engineering skills — skills that are also necessary for understanding biases in the data, and for debugging logging output from code. Once she gets the data into shape, a crucial part is exploratory data analysis, which combines visualization and data sense. She’ll find patterns, build models, and algorithms — some with the intention of understanding product usage and the overall health of the product, and others to serve as prototypes that ultimately get baked back into the product. She may design experiments, and she is a critical part of data-driven decision making. She’ll communicate with team members, engineers, and leadership in clear language and with data visualizations so that even if her colleagues are not immersed in the data themselves, they will understand the implications.

The complexity of a subject, if crucial for understanding the story, needs to be shown in the visualisation. Thus, in many cases, clarifying a subject requires increasing the amount of information, not reducing it.

The null hypothesis of normality is that the variable is normally distributed: thus, we do not want to reject the null hypothesis. A problem with statistical tests of normality is that they are very sensitive to small samples and minor deviations from normality. The extreme sensitivity of these tests implies the following: whereas failure to reject the null hypo thesis indicates normal distribution of a variable, rejecting the null hypothesis does not indicate that the variable is not normally distributed. It is acceptable to consider variables as being normally distributed when they visually appear to be so, even when the null hypothesis of normality is rejected by normality tests. Of course, variables are preferred that are supported by both visual inspection and normality tests. In Greater Depth … Box 12.1 Why Normality? The reasons for the normality assumption are twofold: First, the features of the normal distribution are well-established and are used in many parametric tests for making inferences and hypothesis testing. Second, probability theory suggests that random samples will often be normally distributed, and that the means of these samples can be used as estimates of population means. The latter reason is informed by the central limit theorem, which states that an infinite number of relatively large samples will be normally distributed, regardless of the distribution of the population. An infinite number of samples is also called a sampling distribution. The central limit theorem is usually illustrated as follows. Assume that we know the population distribution, which has only six data elements with the following values: 1, 2, 3, 4, 5, or 6. Next, we write each of these six numbers on a separate sheet of paper, and draw repeated samples of three numbers each (that is, n = 3). We

Power BI Desktop unifies the former Excel Power Tools (Power Pivot, Power Query and Power View) into one vastly improved, stand-alone, data discovery desktop application built on a modernized HTML5 visualization framework.

The Scheffe test is the most conservative, the Tukey test is best when many comparisons are made (when there are many groups), and the Bonferroni test is preferred when few comparisons are made. However, these post-hoc tests often support the same conclusions.3 To illustrate, let’s say the independent variable has three categories. Then, a post-hoc test will examine hypotheses for whether . In addition, these tests will also examine which categories have means that are not significantly different from each other, hence, providing homogeneous subsets. An example of this approach is given later in this chapter. Knowing such subsets can be useful when the independent variable has many categories (for example, classes of employees). Figure 13.1 ANOVA: Significant and Insignificant Differences Eta-squared (η2) is a measure of association for mixed nominal-interval variables and is appropriate for ANOVA. Its values range from zero to one, and it is interpreted as the percentage of variation explained. It is a directional measure, and computer programs produce two statistics, alternating specification of the dependent variable. Finally, ANOVA can be used for testing interval-ordinal relationships. We can ask whether the change in means follows a linear pattern that is either increasing or decreasing. For example, assume we want to know whether incomes increase according to the political orientation of respondents, when measured on a seven-point Likert scale that ranges from very liberal to very conservative. If a linear pattern of increase exists, then a linear relationship is said to exist between these variables. Most statistical software packages can test for a variety of progressive relationships. ANOVA Assumptions ANOVA assumptions are essentially the same as those of the t-test: (1) the dependent variable is continuous, and the independent variable is ordinal or nominal, (2) the groups have equal variances, (3) observations are independent, and (4) the variable is normally distributed in each of the groups. The assumptions are tested in a similar manner. Relative to the t-test, ANOVA requires a little more concern regarding the assumptions of normality and homogeneity. First, like the t-test, ANOVA is not robust for the presence of outliers, and analysts examine the presence of outliers for each group. Also, ANOVA appears to be less robust than the t-test for deviations from normality. Second, regarding groups having equal variances, our main concern with homogeneity is that there are no substantial differences in the amount of variance across the groups; the test of homogeneity is a strict test, testing for any departure from equal variances, and in practice, groups may have neither equal variances nor substantial differences in the amount of variances. In these instances, a visual finding of no substantial differences suffices. Other strategies for dealing with heterogeneity are variable transformations and the removal of outliers, which increase variance, especially in small groups. Such outliers are detected by examining boxplots for each group separately. Also, some statistical software packages (such as SPSS), now offer post-hoc tests when equal variances are not assumed.4 A Working Example The U.S. Environmental Protection Agency (EPA) measured the percentage of wetland loss in watersheds between 1982 and 1992, the most recent period for which data are available (government statistics are sometimes a little old).5 An analyst wants to know whether watersheds with large surrounding populations have

suffered greater wetland loss than watersheds with smaller surrounding populations. Most watersheds have suffered no or only very modest losses (less than 3 percent during the decade in question), and few watersheds have suffered more than a 4 percent loss. The distribution is thus heavily skewed toward watersheds with little wetland losses (that is, to the left) and is clearly not normally distributed.6 To increase normality, the variable is transformed by twice taking the square root, x.25. The transformed variable is then normally distributed: the Kolmogorov-Smirnov statistic is 0.82 (p = .51 > .05). The variable also appears visually normal for each of the population subgroups. There are four population groups, designed to ensure an adequate number of observations in each. Boxplot analysis of the transformed variable indicates four large and three small outliers (not shown). Examination suggests that these are plausible and representative values, which are therefore retained. Later, however, we will examine the effect of these seven observations on the robustness of statistical results. Descriptive analysis of the variables is shown in Table 13.1. Generally, large populations tend to have larger average wetland losses, but the standard deviations are large relative to (the difference between) these means, raising considerable question as to whether these differences are indeed statistically significant. Also, the untransformed variable shows that the mean wetland loss is less among watersheds with “Medium I” populations than in those with “Small” populations (1.77 versus 2.52). The transformed variable shows the opposite order (1.06 versus 0.97). Further investigation shows this to be the effect of the three small outliers and two large outliers on the calculation of the mean of the untransformed variable in the “Small” group. Variable transformation minimizes this effect. These outliers also increase the standard deviation of the “Small” group. Using ANOVA, we find that the transformed variable has unequal variances across the four groups (Levene’s statistic = 2.83, p = .41 < .05). Visual inspection, shown in Figure 13.2, indicates that differences are not substantial for observations within the group interquartile ranges, the areas indicated by the boxes. The differences seem mostly caused by observations located in the whiskers of the “Small” group, which include the five outliers mentioned earlier. (The other two outliers remain outliers and are shown.) For now, we conclude that no substantial differences in variances exist, but we later test the robustness of this conclusion with consideration of these observations (see Figure 13.2). Table 13.1 Variable Transformation We now proceed with the ANOVA analysis. First, Table 13.2 shows that the global F-test statistic is 2.91, p = .038 < .05. Thus, at least one pair of means is significantly different. (The term sum of squares is explained in note 1.) Getting Started Try ANOVA on some data of your choice. Second, which pairs are significantly different? We use the Bonferroni post-hoc test because relatively few comparisons are made (there are only four groups). The computer-generated results (not shown in Table 13.2) indicate that the only significant difference concerns the means of the “Small” and “Large” groups. This difference (1.26 - 0.97 = 0.29 [of transformed values]) is significant at the 5 percent level (p = .028). The Tukey and Scheffe tests lead to the same conclusion (respectively, p = .024 and .044). (It should be noted that post-hoc tests also exist for when equal variances are not assumed. In our example, these tests lead to the same result.7) This result is consistent with a visual reexamination of Figure 13.2, which shows that differences between group means are indeed small. The Tukey and

usually does not present much of a problem. Some analysts use t-tests with ordinal rather than continuous data for the testing variable. This approach is theoretically controversial because the distances among ordinal categories are undefined. This situation is avoided easily by using nonparametric alternatives (discussed later in this chapter). Also, when the grouping variable is not dichotomous, analysts need to make it so in order to perform a t-test. Many statistical software packages allow dichotomous variables to be created from other types of variables, such as by grouping or recoding ordinal or continuous variables. The second assumption is that the variances of the two distributions are equal. This is called homogeneity of variances. The use of pooled variances in the earlier formula is justified only when the variances of the two groups are equal. When variances are unequal (called heterogeneity of variances), revised formulas are used to calculate t-test test statistics and degrees of freedom.7 The difference between homogeneity and heterogeneity is shown graphically in Figure 12.2. Although we needn’t be concerned with the precise differences in these calculation methods, all t-tests first test whether variances are equal in order to know which t-test test statistic is to be used for subsequent hypothesis testing. Thus, every t-test involves a (somewhat tricky) two-step procedure. A common test for the equality of variances is the Levene’s test. The null hypothesis of this test is that variances are equal. Many statistical software programs provide the Levene’s test along with the t-test, so that users know which t-test to use—the t-test for equal variances or that for unequal variances. The Levene’s test is performed first, so that the correct t-test can be chosen. Figure 12.2 Equal and Unequal Variances The term robust is used, generally, to describe the extent to which test conclusions are unaffected by departures from test assumptions. T-tests are relatively robust for (hence, unaffected by) departures from assumptions of homogeneity and normality (see below) when groups are of approximately equal size. When groups are of about equal size, test conclusions about any difference between their means will be unaffected by heterogeneity. The third assumption is that observations are independent. (Quasi-) experimental research designs violate this assumption, as discussed in Chapter 11. The formula for the t-test test statistic, then, is modified to test whether the difference between before and after measurements is zero. This is called a paired t-test, which is discussed later in this chapter. The fourth assumption is that the distributions are normally distributed. Although normality is an important test assumption, a key reason for the popularity of the t-test is that t-test conclusions often are robust against considerable violations of normality assumptions that are not caused by highly skewed distributions. We provide some detail about tests for normality and how to address departures thereof. Remember, when nonnormality cannot be resolved adequately, analysts consider nonparametric alternatives to the t-test, discussed at the end of this chapter. Box 12.1 provides a bit more discussion about the reason for this assumption. A combination of visual inspection and statistical tests is always used to determine the normality of variables. Two tests of normality are the Kolmogorov-Smirnov test (also known as the K-S test) for samples with more than 50 observations and the Shapiro-Wilk test for samples with up to 50 observations. The null hypothesis of

Let me try to clear the ground by reiterating three definitions proposed twenty years ago in a book cowritten with Walt Crawford,8 and adding a fourth to define the content of the various types of resource that constitute the human record as it is encountered and experienced in libraries: Data: Facts and other raw material that can be processed into useful information. Information: Data processed and rendered useful. Knowledge: Information transformed into meaning and made manifest in texts, cartographic, and other visual or audiovisual materials. Imaginative/aesthetic creations: Literary texts, graphic/visual/audiovisual creations, and the like, in which the aesthetic transcends the utilitarian.

Heightened capacity for visual imagery and fantasy “Was able to move imaginary parts in relation to each other.” “It was the non-specific fantasy that triggered the idea.” “The next insight came as an image of an oyster shell, with the mother-of-pearl shining in different colors. I translated that in the idea of an interferometer—two layers separated by a gap equal to the wavelength it is desired to reflect.” “As soon as I began to visualize the problem, one possibility immediately occurred. A few problems with that concept occurred, which seemed to solve themselves rather quickly…. Visualizing the required cross section was instantaneous.” “Somewhere along in here, I began to see an image of the circuit. The gates themselves were little silver cones linked together by lines. I watched the circuit flipping through its paces….” “I began visualizing all the properties known to me that a photon possesses and attempted to make a model for a photon…. The photon was comprised of an electron and a positron cloud moving together in an intermeshed synchronized helical orbit…. This model was reduced for visualizing purposes to a black-and-white ball propagating in a screwlike fashion through space. I kept putting the model through all sorts of known tests.” 5. Increased ability to concentrate “Was able to shut out virtually all distracting influences.” “I was easily able to follow a train of thought to a conclusion where normally I would have been distracted many times.” “I was impressed with the intensity of concentration, the forcefulness and exuberance with which I could proceed toward the solution.” “I considered the process of photoconductivity…. I kept asking myself, ‘What is light? and subsequently, ‘What is a photon?’ The latter question I repeated to myself several hundred times till it was being said automatically in synchronism with each breath. I probably never in my life pressured myself as intently with a question as I did this one.” “It is hard to estimate how long this problem might have taken without the psychedelic agent, but it was the type of problem that might never have been solved. It would have taken a great deal of effort and racking of the brains to arrive at what seemed to come more easily during the session.” 6. Heightened empathy with external processes and objects “…the sense of the problem as a living thing that is growing toward its inherent solution.” “First I somehow considered being the needle and being bounced around in the groove.” “I spent a productive period …climbing down on my retina, walking around and thinking about certain problems relating to the mechanism of vision.” “Ability to grasp the problem in its entirety, to ‘dive’ into it without reservations, almost like becoming the problem.” “Awareness of the problem itself rather than the ‘I’ that is trying to solve it.” 7. Heightened empathy with people “It was also felt that group performance was affected in …subtle ways. This may be evidence that some sort of group action was going on all the time.” “Only at intervals did I become aware of the music. Sometimes, when I felt the other guys listening to it, it was a physical feeling of them listening to it.” “Sometimes we even had the feeling of having the same thoughts or ideas.” 8. Subconscious data more accessible “…brought about almost total recall of a course that I had had in thermodynamics; something that I had never given any thought about in years.” “I was in my early teens and wandering through the gardens where I actually grew up. I felt all my prior emotions in relation to my surroundings.

safety at the beginning of the program was 4.40 (standard deviation, SD = 1.00), and one year later, 4.80 (SD = 0.94). The mean safety score increased among 10th graders, but is the increase statistically significant? Among other concerns is that the standard deviations are considerable for both samples. As part of the analysis, we conduct a t-test to answer the question of whether the means of these two distributions are significantly different. First, we examine whether test assumptions are met. The samples are independent, and the variables meet the requirement that one is continuous (the index variable) and the other dichotomous. The assumption of equality of variances is answered as part of conducting the t-test, and so the remaining question is whether the variables are normally distributed. The distributions are shown in the histograms in Figure 12.3.12 Are these normal distributions? Visually, they are not the textbook ideal—real-life data seldom are. The Kolmogorov-Smirnov tests for both distributions are insignificant (both p > .05). Hence, we conclude that the two distributions can be considered normal. Having satisfied these t-test assumptions, we next conduct the t-test for two independent samples. Table 12.1 shows the t-test results. The top part of Table 12.1 shows the descriptive statistics, and the bottom part reports the test statistics. Recall that the t-test is a two-step test. We first test whether variances are equal. This is shown as the “Levene’s test for equality of variances.” The null hypothesis of the Levene’s test is that variances are equal; this is rejected when the p-value of this Levene’s test statistic is less than .05. The Levene’s test uses an F-test statistic (discussed in Chapters 13 and 15), which, other than its p-value, need not concern us here. In Table 12.1, the level of significance is .675, which exceeds .05. Hence, we accept the null hypothesis—the variances of the two distributions shown in Figure 12.3 are equal. Figure 12.3 Perception of High School Safety among 10th Graders Table 12.1 Independent-Samples T-Test: Output Note: SD = standard deviation. Now we go to the second step, the main purpose. Are the two means (4.40 and 4.80)

Having all the information in the world at our fingertips doesn’t make it easier to communicate: it makes it harder. The more information you’re dealing with, the more difficult it is to filter

Suggestions were given on how to promote mental flexibility. Included were the following: Try to identify with the central person, object, or process in the center of the problem. See how the problem looks from this vantage point. Try to “see” the solution—to visualize how various parts might work together, how a certain situation will work out. Scan rapidly through a large number of possible solutions, ideas, and data. The “right” solution will often appear with a sort of intuitive “knowing” that it is the answer. You will also find that you can be simultaneously aware of an uncommonly large number of ideas or pieces of data processes simultaneously. You will be able to step back from the problem and see it in a new perspective, in more basic terms. Since there is much less of yourself invested than in your prior trials, you will be able to abandon previously tried approaches and start afresh. Above all, don’t be timid about asking for answers. If you want to see the solution in a three-dimensional image, or to project yourself forward in time, or to view some microscopic physical process or something not visible to the physical eye, or to reexperience some event out of the past, by all means do so. Ask. Don’t let your questions be limited by your notion of what can or cannot be done.

The process of creating .jpgs is synonymous with the process of throwing away information. 12-bits of data per channel from the sensor gets squeezed into 8 bits of data per channel (giving up some tonality and fine shades of color). A little bit of dynamic range gets lost too.  Then Lots of visual information that the human brain cannot perceive gets thrown away, which is what’s responsible for JPG’s famously small size.  If there is a lot of high-frequency detail in the image, then that gets replaced by what’s called a .jpg compression artifact (which I describe in a couple of sections).  Then the compressed .jpg image file is written to the memory card, and then the raw information from which the .jpg was produced is discarded (unless you were wise enough to shoot in RAW + JPG mode). 

declarer.declareStream("car", new Fields("first")); declarer.declareStream("cdr", new Fields(

If 2 minutes are required to process 1 minute of data, the system will not be real time for very long.

Most recently, I worked for this advertising agency that specializes in perceptual marketing. They ensure that whatever ads you see in your everyday life are geared to your specific taste, style, demographic, purchasing history, and countless other interwoven criteria. If you walk by a billboard, it shows you something you actually want or an upgrade to something you already have. They use real-time rolling data feeds, so you might see a different ad depending on your mood before versus after lunch, if you were running late or had time to linger, whether you had sex that night or argued with your spouse that morning. Following a negative experience with some company’s wares, they’d give a competitor a shot at shifting your brand loyalty. My big idea was that clients could pay a monthly fee to see no ads at all. Instead of individualized niche marketing, you could experience a world blissfully emptied of promotional clutter. It was a total failure. Because it turns out people like ads. Especially when they’re targeted to warp the visual environment around you to emphasize your needs above all others, as if you’re the indispensable center of the global economy. Nobody wanted to pay for the privilege of being irrelevant to commercial interests. Except me. I essentially got my employer to launch an expensive new product solely for my use. An industry of one.

into large and complex datasets is a prevalent theme in current visualization research for which different approaches are pursued. Topology-based methods are built on the idea of abstracting characteristic structures such as the topological skeleton from the data and to construct the visualization accordingly. Even

How do you create common ground for more effective data communication? You can start by teaching the fundamental grammar of data visualization: metrics, dimensions, distributions, relationships, outliers, and variance. You

Discovery is no longer limited by the collection and processing of data, but rather management, analysis, and visualization.

Exhaustive analysis of the findings indicated principles to improve in-store visibility. Based on these, Guinness created a prototype fixture and installed it in test stores, as shown in Figure 2.1. The extruding fins were highly visible, ensuring that the offer would reach shoppers at the end of the aisle. The fins also broke the linear nature of the aisle, helping to stop shoppers by the display. Product layout was clear and authoritative. All these elements were within the cone of vision. Strong brand block and the use of signpost products reduced visual “noise,” strengthened impact, and acted as guides around the fixture. Figure 2.1 This Guinness display, using fins to break the aisle, helped stop shoppers and increase sales dramatically. Guinness monitored checkout scanner data in the test stores. It then modified the design in response to these findings and installed the display in various retail sites. Guinness then installed the new display in ten sites and identified another ten control sites for a formal test. The new fixture increased sales dramatically. Why? The new display was able to pull customers through the three moments of truth: reaching, stopping, and closing the sale. The fixture made stout easier to find in this busy category, so the display reached out to shoppers. The time until the first customer interaction decreased from an average of 38 seconds to 11 seconds. The majority of stout purchasers went straight to the fixture, so it did a better job stopping them in front of the display. The total average visit time reduced from 2.08 minutes to 1.53 minutes, indicating that it is easier to shop from the new fixture. U-turning in the middle of the aisle halved, to only 24 percent. More customers were now shopping the whole aisle. And, finally, these customers bought Guinness in much higher numbers. In the test stores, Guinness draught sales increased by 25 percent in value and 24 percent in volume. Total stout sales grew by 10 percent and total beer sales by 4 percent

Dimensional designers listen carefully to the emphasis on product, market, and time. Most people find it intuitive to think of such a business as a cube of data, with the edges labeled product, market, and time. Imagine slicing and dicing along each of these dimensions. Points inside the cube are where the measurements, such as sales volume or profit, for that combination of product, market, and time are stored. The ability to visualize something as abstract as a set of data in a concrete and tangible way is the secret of understandability. If

The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decades.

As much as we like to think of ourselves as rational creatures, we are not. If we were, no one would throw away their perfectly working iPhone 5s to go pick up the latest Apple toy. We largely make decisions based on emotions, then rationalize them later with logical arguments. So how can you incorporate emotion into your facts and data? Use a visual story.

When I first joined Facebook, I was working with a team to answer the critical question of how best to grow our business. The conversations were getting heated, with many people arguing their own positions strongly. We ended the week without consensus. Dan Rose, leader of our deal team, spent the weekend gathering market data that allowed us to reframe the conversation in analytics. His effort broke the logjam. I then expanded Dan’s responsibilities to include product marketing. Taking initiative pays off. It is hard to visualize someone as a leader if she is always waiting to be told what to do.

To guess is no good, but to anticipate is GREAT.

Illusions are fabrications of fragmented data designed to appease the soul and entice the flesh. Even in the real world we're surrounded by illusions. Like false advertising and misrepresentations by people. Visual just created a world not so different from our own. Where such illusions are fulfilled and enjoyed vicariously.” Spiral explained.

Beyond annoying our audience by trying to sound smart, we run the risk of making our audience feel dumb. In either case, this is not a good user experience for our audience. Avoid

Use manual sanity checks in data pipelines. When optimizing data processing systems, it’s easy to stay in the “binary mindset” mode, using tight pipelines, efficient binary data formats, and compressed I/O. As the data passes through the system unseen, unchecked (except for perhaps its type), it remains invisible until something outright blows up. Then debugging commences. I advocate sprinkling a few simple log messages throughout the code, showing what the data looks like at various internal points of processing, as good practice — nothing fancy, just an analogy to the Unix head command, picking and visualizing a few data points. Not only does this help during the aforementioned debugging, but seeing the data in a human-readable format leads to “aha!” moments surprisingly often, even when all seems to be going well. Strange tokenization! They promised input would always be encoded in latin1! How did a document in this language get in there? Image files leaked into a pipeline that expects and parses text files! These are often insights that go way beyond those offered by automatic type checking or a fixed unit test, hinting at issues beyond component boundaries. Real-world data is messy. Catch early even things that wouldn’t necessarily lead to exceptions or glaring errors. Err on the side of too much verbosity.

Layering and Separation 53C O N F U S I ON and clutter are failures of design, not attributes of information. And so the point is to find design strategies that reveal detail and complexity — rather than to fault the data for an excess of complication.Or, worse, to fault viewers for a lack of understanding. Among the most powerful devices for reducing noise and enriching the content of displays is the technique of layering and separation, visually stratifying various aspects of the data.

Never again will you simply show data. Rather, you will create visualizations that are thoughtfully designed to impart information and incite action.

Sheila, you’ve got to hang on. All this is just an increased power to think—to visualize, to handle data and the dreams you yourself have created. Nothing more.” “But it is changing me!” The horror of death was in her now. She fought it with something like wistfulness: “—and where has our world gone? Where are our hopes and plans and togetherness?” “We can’t bring them back,” he replied. Emptiness, irrevocability: “We have to make out with what we have now.

Exploratory analysis is what you do to understand the data and figure out what might be noteworthy or interesting to highlight to others.

When we’re at the point of communicating our analysis to our audience, we really want to be in the explanatory space, meaning you have a specific thing you want to explain, a specific story you want to tell—probably about those two pearls.