Top 200+ Best Data Science Quotes That Will Convince You To Make Sense Of Data

Written by Vishal for Factober

FACTOBER KNOWLEDGE & INSPIRATION

October 9, 2020

Data Science Quotes

Making sense of data is one of the first things I would like to do every next day. Today is the edge of information, data, and all. If you want to learn why Data Science is the next big thing? Read these 200+ Data Science and Data Analysis Quotes.

Share these quotes using hashtags – #Top200+, #BestQuotes, #DataScienceQuotes, #DataSense, #DataScience, #DataAnalysis, #Quotes, #DataAnalysis, #DataAnalyticsQuotes, #DataAnalytics, #BigData, #BigDataQuotes, #MachineLearning, #InformationScience, #DataQuotes, #Python #DataQuotes, #DataScienceBooks

  1. A forecaster should almost never ignore data, especially when she is studying rare events like recessions or presidential elections, about which there isn’t very much data, to begin with. Ignoring data is often a tip-off that the forecaster is overconfident, or is overfitting her model—that she is interested in showing off rather than trying to be accurate. Nate Silver
  2. Above all else show the data. Edward R. Tufte, The Visual Display of Quantitative Information
  3. Absolutely nothing useful is realized when one person who holds that there is a 0 percent probability of something argues against another person who holds that the probability is 100 percent. Nate Silver
  4. Act without doing; work without effort. Think of the small as large and the few as many. Confront the difficult while it is still easy; accomplish the great task by a series of small acts. ~Laozi – Wes McKinney, Python for Data Analysis
  5. After adjusting for inflation, a $10,000 investment made in a home in 1896 would be worth just $10,600 in 1996. Nate Silver
  6. All models are wrong, but some models are useful.”90 What he meant by that is that all models are simplifications of the universe, as they must necessarily be. Nate Silver
  7. 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. Edward R. Tufte, The Visual Display of Quantitative Information
  8. Amazon engineer Greg Linden originally introduced doppelganger searches to predict readers’ book preferences, the improvement in recommendations was so good that Amazon founder Jeff Bezos got to his knees and shouted, “I’m not worthy!” to Linden. But what is really interesting about doppelganger searches, considering their power, is not how they’re commonly being used now. It is how frequently they are not used. There are major areas of life that could be vastly improved by the kind of personalization these searches allow. Seth Stephens-Davidowitz
  9. An economist is an expert who will know tomorrow why the things he predicted yesterday didn’t happen. Earl Wilson
  10. As data piles up, we have ourselves a genuine gold rush. But data isn’t the gold. I repeat, data in its raw form is boring crud. The gold is what’s discovered therein. Eric Siegel, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
  11. As for the data employed and the insights gained, the tactic in play is: “Whatever works.” And yet even hard-nosed scientists fight the urge to overexplain. Eric Siegel
  12. As John Maynard Keynes said, “The market can stay irrational longer than you can stay solvent. Nate Silver
  13. As with Google, so with everyone else trying to use data to understand the world. The Big Data revolution is less about collecting more and more data. It is about collecting the right data. Seth Stephens-Davidowitz
  14. Backtesting against historical data, all indications whispered confident promises for what this thing could do once set in motion. As John puts it, “A slight pattern emerged from the overwhelming noise; we had stumbled across a persistent pricing inefficiency in a corner of the market, a small edge over the average investor, which appeared repeatable.” Inefficiencies are what traders live for. A perfectly efficient market can’t be played, but if you can identify the right imperfection, it’s payday. Eric Siegel
  15. 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. Cole Nussbaumer Knaflic
  16. But all predictive models share the same objective: They consider the various factors of an individual in order to derive a single predictive score for that individual. This score is then used to drive an organizational decision, guiding which action to take. Before using a model, we’ve got to build it. Machine learning builds the predictive model Eric Siegel, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
  17. But forecasters often resist considering these out-of-sample problems. When we expand our sample to include events further apart from us in time and space, it often means that we will encounter cases in which the relationships we are studying did not hold up as well as we are accustomed to. Nate Silver
  18. But probability triumphs in the end. An important theorem known as the law of large numbers tells us that as the number of independent trials increases, the average of the outcomes will get closer and closer to its expected value. Charles Wheelan
  19. But realize this: we are living through writing’s Cambrian explosion, not its mass extinction. Language is more varied than ever before, even if some of it is directly copied from the clipboard—variety is the preservation of an art, not a threat to it. Christian Rudder, Dataclysm: Who We Are
  20. By combining the visual and verbal, we set ourselves up for success when it comes to triggering the formation of long-term memories in our audience. Cole Nussbaumer Knaflic
  21. Computers, however, have nothing better to do; keeping track is their only job. They don’t lose the scrapbook, or travel, or get drunk, or grow senile, or even blink. They just sit there and remember. Christian Rudder, Dataclysm: Who We Are
  22. Concentrate on the pearls, the information your audience needs to know. Cole Nussbaumer Knaflic
  23. Data are to statistics what a good offensive is to a star quarterback. Charles Wheelan
  24. Data is useless without context. Nate Silver
  25. Data mining is an exploratory undertaking closer to research and development than it is to engineering. Foster Provost, Data Science for Business: What you need to know about data mining and data-analytic thinking
  26. Data mining is used for general customer relationship management to analyze customer behavior in order to manage attrition and maximize expected customer value. Foster Provost
  27. Data-driven predictions can succeed—and they can fail. It is when we deny our role in the process that the odds of failure rise. Before we demand more of our data, we need to demand more of ourselves. Nate Silver
  28. Descriptive statistics can be like online dating profiles: technically accurate and yet pretty darn misleading. Charles Wheelan
  29. Descriptive statistics exist to simplify, which always implies some loss of nuance or detail. Charles Wheelan, Naked Statistics: Stripping the Dread from the Data
  30. Distinguishing the signal from the noise requires both scientific knowledge and self-knowledge: the serenity to accept the things we cannot predict, the courage to predict the things we can, and the wisdom to know the difference. Nate Silver, The Signal and the Noise: Why So Many Predictions Fail—But Some Don’t
  31. Economy is not baseball, where the game is always played by the same rules. Nate Silver
  32. Exploratory analysis is what you do to understand the data and figure out what might be noteworthy or interesting to highlight to others. Cole Nussbaumer Knaflic
  33. Federal researchers cannot rule out mere chance as the cause of any variation in the performance of students who use these software products and students who do not. Charles Wheelan, Naked Statistics: Stripping the Dread from the Data
  34. Finding patterns is easy in any kind of data-rich environment; that’s what mediocre gamblers do. The key is in determining whether the patterns represent noise or signal. Nate Silver
  35. Fire, knives, automobiles, hair removal cream. Each of these things serves an important purpose. Each one makes our lives better. And each one can cause some serious problems when abused. Now you can add statistics to that list. Charles Wheelan, Naked Statistics: Stripping the Dread from the Data
  36. For distributions without serious outliers, the median and the mean will be similar. Charles Wheelan
  37. For Popper, a hypothesis was not scientific unless it was falsifiable—meaning that it could be tested in the real world by means of a prediction. Nate Silver
  38. Framing a business problem in terms of expected value can allow us to systematically decompose it into data mining tasks. Foster Provost
  39. Good innovators typically think very big and they think very small. New ideas are sometimes found in the most granular details of a problem where few others bother to look. And they are sometimes found when you are doing your most abstract and philosophical thinking, considering why the world is the way that it is and whether there might be an alternative to the dominant paradigm. Rarely can they be found in the temperate latitudes between they two spaces, where we spend 99 percent of our lives. Nate Silver, The Signal and the Noise: Why So Many Predictions Fail—But Some Don’t
  40. Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space. Edward R. Tufte, The Visual Display of Quantitative Information
  41. Having all the information in the world at our fingertips doesn’t make it easier to communicate: it makes it harder. Cole Nussbaumer Knaflic, Storytelling with Data: A Data Visualization Guide for Business Professionals
  42. 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. Cole Nussbaumer Knaflic
  43. He does not depend on insider tips, crooked referees, or other sorts of hustles to make his bets. Nor does he have a “system” of any kind. He uses computer simulations, but does not rely upon them exclusively. Nate Silver
  44. Hedgehogs who have lots of information construct stories; stories that are neater and tidier than the real world, with protagonists and villains, winners and losers, climaxes and dénouements—and, usually, a happy ending for the home team. The candidate who is down ten points in the polls is going to win, goddamnit, because I know the candidate and I know the voters in her state, and maybe I heard something from her press secretary about how the polls are tightening—and have you seen her latest commercial? Nate Silver
  45. Here is one of the most important things to remember when doing research that involves regression analysis: Try not to kill anyone. You can even put a little Post-it note on your computer monitor: “Do not kill people with your research. Charles Wheelan, Naked Statistics: Stripping the Dread from the Data
  46. Here is one of the most important things to remember when doing research that involves regression analysis: Try not to kill anyone. You can even put a little Post-it note on your computer monitor: “Do not kill people with your research.” Because some very smart people have inadvertently violated that rule. Charles Wheelan, Naked Statistics: Stripping the Dread from the Data
  47. High-level knowledge of the fundamentals helps creative business analysts see novel formulations. Foster Provost
  48. Horizontal bar chart If I had to pick a single go-to graph for categorical data, it would be the horizontal bar chart, which flips the vertical version on its side. Why? Because it is extremely easy to read. The horizontal bar chart is especially useful if your category names are long, as the text is written from left to right, as most audiences read, making your graph legible for your audience. Cole Nussbaumer Knaflic
  49. How can we apply our judgment to the data—without succumbing to our biases? Nate Silver
  50. Human beings have an extraordinary capacity to ignore risks that threaten their livelihood, as though this will make them go away. Nate Silver
  51. I am now convinced that Google searches are the most important dataset ever collected on the human psyche. Seth Stephens-Davidowitz
  52. I don’t like the dinosaur in this graphic. It looks too fake. Use a real photo of a dinosaur instead. Christian Rudder, Dataclysm: Who We Are
  53. I sometimes suspect that inside every data scientist is a kid trying to figure out why his childhood dreams didn’t come true. Seth Stephens-Davidowitz
  54. If political scientists couldn’t predict the downfall of the Soviet Union—perhaps the most important event in the latter half of the twentieth century then what exactly were they good for? Nate Silver
  55. If there is a mutual distrust between the weather forecaster and the public, the public may not listen when they need to most. Nate Silver
  56. If you can’t understand a study, the problem is with the study, not with you. Seth Stephens-Davidowitz
  57. If you simply present data, it’s easy for your audience to say, “Oh, that’s interesting,” and move on to the next thing. But if you ask for action, your audience has to make a decision whether to comply or not. This elicits a more productive reaction from your audience, which can lead to a more productive conversation—one that might never have been started if you hadn’t recommended the action in the first place. Cole Nussbaumer Knaflic
  58. In analytics, it’s more important for individuals to be able to formulate problems well, to prototype solutions quickly, to make reasonable assumptions in the face of ill-structured problems, to design experiments that represent good investments, and to analyze results. Foster Provost
  59. In Facebook world, the average adult seems to be happily married, vacationing in the Caribbean, and perusing the Atlantic. In the real world, a lot of people are angry, on supermarket checkout lines, peeking at the National Enquirer, ignoring the phone calls from their spouse, whom they haven’t slept with in years. Seth Stephens-Davidowitz
  60. In the case of a randomized, controlled experiment, the control group is the counterfactual. Charles Wheelan
  61. In the past, firms could employ teams of statisticians, modelers, and analysts to explore datasets manually, but the volume and variety of data have far outstripped the capacity of manual analysis. Foster Provost
  62. Information is a quantity that reduces uncertainty about something. So, if an old pirate gives me information about where his treasure is hidden that does not mean that I know for certain where it is, it only means that my uncertainty about where the treasure is hidden is reduced. Foster Provost
  63. It is much easier after the event to sort the relevant from the irrelevant signals. After the event, of course, a signal is always crystal clear; we can now see what disaster it was signaling, since the disaster has occurred. But before the event it is obscure and pregnant with conflicting meanings. It comes to the observer embedded in an atmosphere of “noise,” i.e., in the company of all sorts of information that is useless and irrelevant for predicting the particular disaster. Nate Silver
  64. It’s easy to lie with statistics, but it’s hard to tell the truth without them. Charles Wheelan, Naked Statistics: Stripping the Dread from the Data
  65. It’s that having haters somehow induces everyone else to want you more. People not liking you somehow brings you more attention entirely on its own. Christian Rudder, Dataclysm: Who We Are
  66. Just run: pip install ipython and then search the Internet for solutions to whatever cryptic error messages that causes. Joel Grus, Data Science from Scratch: First Principles with Python
  67. Learning from data is virtually universally useful. Master it and you’ll be welcomed nearly everywhere! John Elder
  68. Longitudinal data sets are the research equivalent of a Ferrari. Not surprisingly, we can’t always have the Ferrari. The research equivalent of a Toyota is a cross-sectional data set. Charles Wheelan
  69. Machine learning tends to be more focused on developing efficient algorithms that scale to large data in order to optimize the predictive model. Statistics generally pays more attention to the probabilistic theory and underlying structure of the model. Peter Bruce, Practical Statistics for Data Scientists: 50 Essential Concepts
  70. Making predictions based on our beliefs is the best (and perhaps even the only) way to test ourselves. If objectivity is the concern for a greater truth beyond our personal circumstances, and prediction is the best way to examine how closely aligned our personal perceptions are with that greater truth, the most objective among us are those who make the most accurate predictions. Nate Silver
  71. Many people underreport embarrassing behaviors and thoughts on surveys. They want to look good, even though most surveys are anonymous. This is called social desirability bias. Seth Stephens-Davidowitz
  72. Meanwhile, exposure to so many new ideas was producing mass confusion. The amount of information was increasing much more rapidly than our understanding of what to do with it, or our ability to differentiate the useful information from the mistruths. Paradoxically, the result of having so much more shared knowledge was increasing isolation along national and religious lines. The instinctual shortcut that we take when we have “too much information” is to engage with it selectively, picking out the parts we like and ignoring the remainder, making allies with those who have made the same choices and enemies of the rest. Nate Silver
  73. Meanwhile, if the quantity of information is increasing by 2.5 quintillion bytes per day, the amount of useful information almost certainly isn’t. Most of it is just noise, and the noise is increasing faster than the signal. There are so many hypotheses to test, so many data sets to mine—but a relatively constant amount of objective truth. Nate Silver
  74. Men may construe things, after their fashion / Clean from the purpose of the things themselves. Nate Silver
  75. Moreover, data science (and business in general) is not so worried about statistical significance, but more concerned with optimizing overall effort and results. Peter Bruce, Practical Statistics for Data Scientists: 50 Essential Concepts
  76. Most of it is just noise, and the noise is increasing faster than the signal. There are so many hypotheses to test, so many data sets to mine—but a relatively constant amount of objective truth. Nate Silver
  77. Most of you will have heard the maxim “correlation does not imply causation.” Just because two variables have a statistical relationship with each other does not mean that one is responsible for the other. For instance, ice cream sales and forest fires are correlated because both occur more often in the summer heat. But there is no causation; you don’t light a patch of the Montana brush on fire when you buy a pint of Haagan-Dazs. Nate Silver, The Signal and the Noise: Why So Many Predictions Fail—But Some Don’t
  78. Netflix learned a similar lesson early on in its life cycle: don’t trust what people tell you; trust what they do. Seth Stephens-Davidowitz
  79. Never compare your Google searches to everyone else’s social media posts. Seth Stephens-Davidowitz
  80. Not only does political coverage often lose the signal—it frequently accentuates the noise. If there are a number of polls in a state that show the Republican ahead, it won’t make news when another one says the same thing. But if a new poll comes out showing the Democrat with the lead, it will grab headlines—even though the poll is probably an outlier and won’t predict the outcome accurately. Nate Silver
  81. Note how, within each, the preattentive attribute grabs your attention, and how some attributes draw your eyes with greater or weaker force than others (for example, color and size are attention grabbing, whereas italics achieve a milder emphasis). Cole Nussbaumer Knaflic
  82. Nowhere is the nexus between statistics and data science stronger than in the realm of prediction — specifically the prediction of an outcome (target) variable based on the values of other “predictor” variables. Peter Bruce
  83. Once you develop a model, don’t pat yourself on the back just yet. Predictions don’t help unless you do something about them. They’re just thoughts, just ideas. They may be astute, brilliant gems that glimmer like the most polished of crystal balls, but hanging. Eric Siegel
  84. One of the pervasive risks that we face in the information age, as I wrote in the introduction, is that even if the amount of knowledge in the world is increasing, the gap between what we know and what we think we know may be widening. Nate Silver, The Signal and the Noise: Why So Many Predictions Fail-but Some Don’t
  85. One thing to keep in mind with a table is that you want the design to fade into the background, letting the data take center stage. Don’t let heavy borders or shading compete for attention. Instead, think of using light borders or simply white space to set apart elements of the table. Cole Nussbaumer Knaflic
  86. Optimization, you see, is the practice of mathematically formulating a business problem and then solving that mathematical representation for the best solution. John W. Foreman, Data Smart: Using Data Science to Transform Information into Insight
  87. Our ability to analyze data has grown far more sophisticated than our thinking about what we ought to do with the results. Charles Wheelan
  88. Overfit: it acquires details of the training set that are not characteristic of the population in general, as represented by the holdout set. Foster Provost
  89. Partisans who expect every idea to fit on a bumper sticker will proceed through the various stages of grief before accepting that they have oversimplified reality. Nate Silver
  90. People . . . operate with beliefs and biases. To the extent you can eliminate both and replace them with data, you gain a clear advantage. Michael Lewis
  91. People Get Sick and Die I’m not afraid of death; I just don’t want to be there when it happens. Eric Siegel
  92. poker is a hard way to make an easy living. Nate Silver
  93. Political experts had difficulty anticipating the USSR’s collapse, Tetlock found, because a prediction that not only forecast the regime’s demise but also understood the reasons for it required different strands of argument to be woven together. There was nothing inherently contradictory about these ideas, but they tended to emanate from people on different sides of the political spectrum,11 and scholars firmly entrenched in one ideological camp were unlikely to have embraced them both. Nate Silver
  94. Predicting better than pure guesswork, even if not accurately, delivers real value. A hazy view of what’s to come outperforms complete darkness by a landslide. The Prediction Effect: A little prediction goes a long way. Eric Siegel
  95. Predictive analytics (PA)—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions. Eric Siegel
  96. Predictive model—A mechanism that predicts a behavior of an individual, such as click, buy, lie, or die. It takes characteristics of the individual as input, and provides a predictive score as output. The higher the score, the more likely it is that the individual will exhibit the predicted behavior. Eric Siegel
  97. Predictive modeling generates the entire model from scratch. All the model’s math or weights or rules are created automatically by the computer. Eric Siegel
  98. Predictive modeling generates the entire model from scratch. All the model’s math or weights or rules are created automatically by the computer. The machine learning process is designed to accomplish this task, to mechanically develop new capabilities from data. This automation is the means by which PA builds its predictive power. Eric Siegel
  99. Probability doesn’t make mistakes; people using probability make mistakes. Charles Wheelan, Naked Statistics: Stripping the Dread from the Data
  100. Probability tells us that any outlier—an observation that is particularly far from the mean in one direction or the other—is likely to be followed by outcomes that are more consistent with the long-term average. Charles Wheelan, Naked Statistics: Stripping the Dread from the Data
  101. Regression analysis enables us to go one step further and “fit a line” that best describes a linear relationship between the two variables. Charles Wheelan
  102. Regression analysis is the hydrogen bomb of the statistics arsenal. Charles Wheelan, Naked Statistics: Stripping the Dread from the Data
  103. Researchers may have some conscious or unconscious bias, either because of a strongly held prior belief or because a positive finding would be better for their career. (No one ever gets rich or famous by proving what doesn’t cause cancer.) Charles Wheelan
  104. Risk, as first articulated by the economist Frank H. Knight in 1921, is somethimg that you can put a price on…….Uncertainty, on the other hand, is risk that is hard to measure. Nate Silver
  105. Short-term memory Short-term memory has limitations. Specifically, people can keep about four chunks of visual information in their short-term memory at a given time. This means that if we create a graph with ten different data series that are ten different colors with ten different shapes of data markers and a legend off to the side, we’re making our audience work very hard going back and forth between the legend and the data to decipher what they are looking at. Cole Nussbaumer Knaflic
  106. Sitewide, the copy-and-paste strategy underperforms from-scratch messaging by about 25 percent, but in terms of effort-in to results-out it always wins: measuring by replies received per unit effort, it’s many times more efficient to just send everyone roughly the same thing than to compose a new message each time. Christian Rudder, Dataclysm: Who We Are
  107. So it is with statistics; no amount of fancy analysis can make up for fundamentally flawed data. Hence the expression “garbage in, garbage out. Charles Wheelan, Naked Statistics: Stripping the Dread from the Data
  108. So we simplify. We perform calculations that reduce a complex array of data into a handful of numbers that describe those data. Charles Wheelan, Naked Statistics: Stripping the Dread from the Data
  109. Statistical inference is really just the marriage of two concepts that we’ve already discussed: data and probability (with a little help from the central limit theorem). Charles Wheelan, Naked Statistics: Stripping the Dread from the Data
  110. Statistical malfeasance has very little to do with bad math. Judgement an integrity turn out to be surprisingly important. A detailed knowledge of statistics does not deter wrongdoing any more than a detailed knowledge of the law averts criminal behavior. Charles Wheelan
  111. Statistics cannot be any smarter than the people who use them. And in some cases, they can make smart people do dumb things. Charles Wheelan
  112. Statistics is like a high-caliber weapon: helpful when used correctly and potentially disastrous in the wrong hands. Charles Wheelan, Naked Statistics: Stripping the Dread from the Data
  113. Stories of prediction are often those of long-term progress but short-term regress. Nate Silver
  114. Students who attended more selective colleges earned roughly the same as students of seemingly similar ability who attended less selective schools. Charles Wheelan
  115. Successful gamblers, instead, think of the future as speckles of probability, flickering upward and downward like a stock market ticker to every new jolt of information. Nate Silver
  116. The alchemy that the ratings agencies performed was to spin uncertainty into what looked and felt like risk. They took highly novel securities, subject to an enormous amount of systemic uncertainty, and claimed the ability to quantify just how risky they were. Not only that, but of all possible conclusions, they came to the astounding one that these investments were almost risk-free. Nate Silver
  117. The answer as to why bubbles form,” Blodget told me, “is that it’s in everybody’s interest to keep markets going up. Nate Silver
  118. The biggest shift was from a bar graph to a line graph. As we’ve discussed, line graphs typically make it easier to see trends over time. This shift also has the effect of visually reducing discrete elements, because the data that was previously five bars has been reduced to a single line with the end points highlighted. Cole Nussbaumer Knaflic
  119. The broad, mostly unexplored terrain opened by the data explosion has been left to a small number of forward-thinking professors, rebellious grad students, and hobbyists. That will change. Seth Stephens-Davidowitz
  120. The central limit theorem tells us that in repeated samples, the difference between the two means will be distributed roughly as a normal distribution. Charles Wheelan
  121. The challenge with any “before and after” kind of analysis is that just because one thing follows another does not mean that there is a causal relationship between the two. Charles Wheelan
  122. The change Twitter has wrought on language itself is nothing compared with the change it is bringing to the study of language. Christian Rudder, Dataclysm: Who We Are
  123. The emotions aren’t always immediately subject to reason, but they are always immediately subject to action. William James
  124. The good news is that these descriptive statistics give us a manageable and meaningful summary of the underlying phenomenon. That’s what this chapter is about. The bad news is that any simplification invites abuse. Descriptive statistics can be like online dating profiles: technically accurate and yet pretty darn misleading. Charles Wheelan, Naked Statistics: Stripping the Dread from the Data
  125. The greatest risks are never the ones you can see and measure, but the ones you can’t see and therefore can never measure. The ones that seem so far outside the boundary of normal probability that you can’t imagine they could happen in your lifetime—even though, of course, they do happen, more often than you care to realize. Charles Wheelan, Naked Statistics: Stripping the Dread from the Data
  126. The Internet of free platforms, free services, and free content is wholly subsidized by targeted advertising, the efficacy (and thus profitability) of which relies on collecting and mining user data. Alexander Furnas
  127. The irony is that by being less focused on your results, you may achieve better ones. Nate Silver, The Signal and the Noise: Why So Many Predictions Fail-but Some Don’t
  128. The key is in remembering that a model is a tool to help us understand the complexities of the universe, and never a substitute for the universe itself. Nate Silver
  129. The law of large numbers explains why casinos always make money in the long run. Charles Wheelan
  130. The litmus test for whether you are a competent forecaster is if more information makes your predictions better. Nate Silver
  131. The mean, or average, turns out to have some problems in that regard, namely, that it is prone to distortion by “outliers,” which are observations that lie farther from the center. Charles Wheelan, Naked Statistics: Stripping the Dread from the Data
  132. The more interviews that an expert had done with the press, Tetlock found, the worse his predictions tended to be. Nate Silver
  133. The most basic tenet of chaos theory is that a small change in initial conditions—a butterfly flapping its wings in Brazil—can produce a large and unexpected divergence in outcomes—a tornado in Texas. Nate Silver
  134. The most calamitous failures of prediction usually have a lot in common. We focus on those signals that tell a story about the world as we would like it to be, not how it really is. We ignore the risks that are hardest to measure, even when they pose the greatest threats to our well-being. Nate Silver
  135. The need for managers with data-analytic skills The consulting firm McKinsey and Company estimates that “there will be a shortage of talent necessary for organizations to take advantage of big data. Foster Provost, Data Science for Business: What you need to know about data mining and data-analytic thinking
  136. The next Freud will be a data scientist. The next Marx will be a data scientist. The next Salk might very well be a data scientist. Seth Stephens-Davidowitz, Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are
  137. The purpose of any program evaluation is to provide some kind of counterfactual against which a treatment or intervention can be measured. Charles Wheelan
  138. The real moral here is: be yourself and be brave about it. Certainly trying to fit in, just for its own sake, is counterproductive. Christian Rudder, Dataclysm: Who We Are
  139. The signal is the truth. The noise is what distracts us from the truth. Nate Silver, The Signal and the Noise: Why So Many Predictions Fail—But Some Don’t
  140. The standard deviation is the descriptive statistic that allows us to assign a single number to this dispersion around the mean. Charles Wheelan
  141. The standard error is what tells us how much dispersion we can expect in our results from sample to sample, which in this case means poll to poll. Charles Wheelan
  142. The story the data tells us is often the one we’d like to hear, and we usually make sure that it has a happy ending. Nate Silver
  143. The success of college towns and big cities is striking when you just look at the data. But I also delved more deeply to undertake a more sophisticated empirical analysis. Doing so showed that there was another variable that was a strong predictor of a person’s securing an entry in Wikipedia: the proportion of immigrants in your county of birth. The greater the percentage of foreign-born residents in an area, the higher the proportion of children born there who go on to notable success. Seth Stephens-Davidowitz
  144. The trouble with the world is that the stupid are cocksure and the intelligent are full of doubt. Bertrand Russell
  145. 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? Cole Nussbaumer Knaflic
  146. The world’s most famous linguists analyze individual texts; they largely ignore the patterns revealed in billions of books. Seth Stephens-Davidowitz
  147. There is a tendency in our planning to confuse the unfamiliar with the improbable. The contingency we have not considered seriously looks strange; what looks strange is thought improbable; what is improbable need not be considered seriously. Nate Silver
  148. There will be more words written on Twitter in the next two years than contained in all books ever printed. Christian Rudder, Dataclysm: Who We Are
  149. 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. Cole Nussbaumer Knaflic
  150. This distinction between correlation and causation is crucial to the proper interpretation of statistical results. Charles Wheelan, Naked Statistics: Stripping the Dread from the Data
  151. This is the second power of Big Data: certain online sources get people to admit things they would not admit anywhere else. They serve as a digital truth serum. Seth Stephens-Davidowitz
  152. This means that, where appropriate, we will dive into mathematical equations, mathematical intuition, mathematical axioms, and cartoon versions of big mathematical ideas. Joel Grus, Data Science from Scratch: First Principles with Python
  153. Tonight, some thirty thousand couples will have their first date because of OkCupid. Roughly three thousand of them will end up together long-term. Two hundred of those will get married, and many of them, of course, will have kids. There are children alive and pouting today, grouchy little humans refusing to put their shoes on right now, who would never have existed but for the whims of our HTML. Christian Rudder, Dataclysm: Who We Are
  154. Twitter actually may be improving its users’ writing, as it forces them to wring meaning from fewer letters—it embodies William Strunk’s famous dictum, Omit needless words, at the keystroke level. Christian Rudder, Dataclysm: Who We Are
  155. Twitter, Reddit, Tumblr, Instagram, all these companies are businesses first, but, as a close second, they’re demographers of unprecedented reach, thoroughness, and importance. Practically as an accident, digital data can now show us how we fight, how we love, how we age, who we are, and how we’re changing. All we have to do is look. Christian Rudder, Dataclysm: Who We Are
  156. Unfortunately, creating an objective function that matches the true goal of the data mining is usually impossible, so data scientists often choose based on faith and experience. Foster Provost, Data Science for Business: What you need to know about data mining and data-analytic thinking
  157. 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. Cole Nussbaumer Knaflic
  158. We face danger whenever information growth outpaces our understanding of how to process it. Nate Silver
  159. we have been told that those of us who drink a moderate amount of alcohol tend to be in better health. That is a correlation. Does this mean drinking a moderate amount will improve one’s health—a causation? Perhaps not. Seth Stephens-Davidowitz
  160. We make approximations and assumptions about the world that are much cruder than we realize. We abhor uncertainty, even when it is an irreducible part of the problem we are trying to solve. Nate Silver
  161. We need to stop, and admit it: we have a prediction problem. We love to predict things—and we aren’t very good at it. Nate Silver, The Signal and the Noise: Why So Many Predictions Fail-but Some Don’t
  162. We will be forced to acknowledge that we know less about the world than we thought we did. Our personal and professional incentives almost always discourage us from doing this. Nate Silver
  163. What a well-designed forecasting system can do is sort out which statistics are relatively more susceptible to luck; batting average, for instance, is more erratic than home runs. Nate Silver
  164. What do you need your audience to know or do? This is the point where you think through how to make what you communicate relevant for your audience and form a clear understanding of why they should care about what you say. Cole Nussbaumer Knaflic
  165. What makes him successful is the way that he analyzes information. He is not just hunting for patterns. Nate Silver
  166. What would a successful outcome look like? If you only had a limited amount of time or a single sentence to tell your audience what they need to know, what would you say? In particular, I find that these last two questions can lead to insightful conversation. Cole Nussbaumer Knaflic
  167. When catastrophe strikes, we look for a signal in the noise – anything that might explain the chaos that we see all around us and bring order to the world again. Nate Silver
  168. When we advance more confident claims and they fail to come to fruition, this constitutes much more powerful evidence against our hypothesis. We can’t really blame anyone for losing faith when this occurs. Nate Silver
  169. When we can’t fit a square peg into a round hole, we’ll usually blame the peg — when. Nate Silver
  170. When we expand our sample to include events further apart from us in time and space, it often means that we will encounter cases in which the relationships we are studying did not hold up as well as we are accustomed to. The model will seem to be less powerful. It will look less impressive in a PowerPoint presentation (or a journal article or a blog post). We will be forced to acknowledge that we know less about the world than we thought we did. Our personal and professional incentives almost always discourage us from doing this. We forget—or we willfully ignore—that our models are simplifications of the world. We figure that if we make a mistake, it will be at the margin. Nate Silver
  171. 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. Cole Nussbaumer Knaflic
  172. When you have just a number or two that you want to communicate: use the numbers directly. Cole Nussbaumer Knaflic
  173. When you see people in middle management dickering with their Fitbits in the elevator, you know the Quantified Self movement is here to stay. The Christian Rudder
  174. When you want to learn about how people write, their unpolished, unguarded words are the best place to start, and we have reams of them. Christian Rudder, Dataclysm: Who We Are
  175. Wherever there is human judgment there is the potential for bias. Nate Silver
  176. Who is your audience? What do you need them to know or do? This chapter describes the importance of understanding the situational context, including the audience, communication mechanism, and desired tone. Cole Nussbaumer Knaflic
  177. Who needs theory when you have so much information? But this is categorically the wrong attitude to take toward forecasting, especially in a field like economics where the data is so noisy. Nate Silver
  178. Why 10 times as many managers and analysts than those with deep analytical skills? Surely data scientists aren’t so difficult to manage that they need 10 managers! The reason is that a business can get leverage from a data science team for making better decisions in multiple areas of the business. Foster Provost, Data Science for Business: What you need to know about data mining and data-analytic thinking
  179. Yes, the probability that five people in the same school or church or workplace will contract the same rare form of leukemia may be one in a million, but there are millions of schools and churches and workplaces. Charles Wheelan, Naked Statistics: Stripping the Dread from the Data
  180. 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. Cole Nussbaumer Knaflic
  181. You write how you write, wherever you write. Christian Rudder, Dataclysm: Who We Are
    Amazon monitors our shopping preferences and Google our browsing habits, while Twitter knows what’s on our minds. Viktor Mayer-Schönberger
  182. Amazon understands the value of digitizing content, while Google understands the value of datafying it. Viktor Mayer-Schönberger
  183. Big data is not about trying to “teach” a computer to “think” like humans. Instead, it’s about applying math to huge quantities of data in order to infer probabilities. Viktor Mayer-Schönberger
  184. Big data refers to things one can do at a large scale that cannot be done at a smaller one, to extract new insights or create new forms of value, in ways that change markets, organizations, the relationship between citizens and governments, and more. Viktor Mayer-Schönberger
  185. Consider the case of Walmart. It is the largest retailer in the world, with more than two million employees and annual sales of around $450 billion—a sum greater than the GDP of four-fifths of the world’s countries. Before the Web brought forth so much data, the company held perhaps the biggest set of data in corporate America. In the 1990s it revolutionized retailing by recording. Viktor Mayer-Schönberger
  186. Data became a raw material of business, a vital economic input, used to create a new form of economic value. Viktor Mayer-Schönberger
  187. Era of big data challenges the way we live and interact with the world. Most strikingly, society will need to shed some of its obsession for causality in exchange for simple correlations: not knowing why but only what. This overturns centuries of established practices and challenges our most basic understanding of how to make decisions and comprehend reality. Viktor Mayer-Schönberger
  188. Facebook seems to catch all that information too, along with our social relationships. Viktor Mayer-Schönberger
  189. If You Have Too Much Data, Then ‘Good Enough’ Is Good Enough. Viktor Mayer-Schönberger, Big Data: A Revolution That Will Transform How We Live, Work and Think
  190. In a small-data world, because so little data tended to be available, both causal investigations and correlation analysis began with a hypothesis, which was then tested to be either falsified or verified. But because both methods required a hypothesis to start with, both were equally susceptible to prejudice and erroneous intuition. And the necessary data often was not available. Today, with so much data around and more to come, such hypotheses are no longer crucial for correlational analysis. Viktor Mayer-Schönberger
  191. In God we trust—all others bring data. Viktor Mayer-Schönberger
  192. In some ways, we haven’t yet fully appreciated our new freedom to collect and use larger pools of data. Most of our experience and the design of our institutions have presumed that the availability of information is limited. Viktor Mayer-Schönberger
  193. One aim of statistics, after all, is to confirm the richest finding using the smallest amount of data. In effect, we codified our practice of stunting the quantity of information we used in our norms, processes, and incentive structures. Viktor Mayer-Schönberger
  194. One aim of statistics, after all, is to confirm the richest finding using the smallest amount of data. Viktor Mayer-Schönberger
  195. Predictions based on correlations lie at the heart of big data. Viktor Mayer-Schönberger
  196. Recently the idea has gained prominence that the best way to extract the value of government data is to give the private sector and society in general access to try. There is a principle behind this as well. When the state gathers data, it does so on behalf of its citizens, and thus it ought to provide access to society (except in a limited number of cases, such as when doing so might harm national security or the privacy rights of others). Viktor Mayer-Schönberger
  197. Sometimes the constraints that we live with, and presume are the same for everything, are really only functions of the scale in which we operate. Viktor Mayer-Schönberger, Big Data: A Revolution That Will Transform How We Live, Work, and Think
  198. The ability to record information is one of the lines of demarcation between primitive and advanced societies. Viktor Mayer-Schönberger
  199. The lessons of big data apply as much to the public sector as to commercial entities: government data’s value is latent and requires innovative analysis to unleash. Viktor Mayer-Schönberger
  200. The regulars around the bar of the National Press Club never thought to reuse online data about media consumption. Nor might the analytics specialists in Armonk, New York, or Bangalore, India, have harnessed the information in this way. It took Cross, a louche outsider with disheveled hair and a slacker’s drawl, to presume. Viktor Mayer-Schönberger
  201. The technical tools for handling data have already changed dramatically, but our methods and mindsets have been slower to adapt. Viktor Mayer-Schönberger
  202. Today a third of all of Amazon’s sales are said to result from its recommendation and personalization systems. Viktor Mayer-Schönberger
  203. We reckoned we could only collect a little information, and so that’s usually what we did. It became self-fulfilling. We even developed elaborate techniques to use as little data as possible. Viktor Mayer-Schönberger

References:

Data Science for Business

What you need to know about data mining and data-analytic thinking.

By Foster Provost, Tom Fawcett. Data Science for Business introduces the fundamental principles of data science and walks you through the “data-analytic thinking” necessary for extracting useful knowledge and business value from the data you collect.

Python for Data Analysis

By Wes McKinney
Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems.

Data Science from Scratch: First Principles with Python by Joel Grus

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.

The Visual Display of Quantitative Information

by Edward R. Tufte
The classic book on statistical graphics, charts, tables. Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Design of the high-resolution displays, small multiples.

Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are

By Seth Stephens-Davidowitz, Steven Pinker
Blending the informed analysis of The Signal and the Noise with the instructive iconoclasm of Think Like a Freak, a fascinating, illuminating, and witty look at what the vast amounts of information now instantly available to us reveals about ourselves and our world—provided we ask the right questions.

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die

By Eric Siegel
“The Freakonomics of big data.”, Stein Kretsinger, founding executive of Advertising.com; a former lead analyst at Capital One

Practical Statistics for Data Scientists: 50 Essential Concepts

By Peter Bruce, Andrew Bruce
Statistical methods are a key part of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.

Dataclysm: Who We Are (When We Think No One’s Looking)

By Christian Rudder
An audacious, irreverent investigation of human behavior—and the first look at a revolution in the making. Our personal data has been used to spy on us, hire and fire us, and sell us stuff we don’t need. In Dataclysm, Christian Rudder uses it to show us who we truly are.

Big Data: A Revolution That Will Transform How We Live, Work, and Think

By Viktor Mayer-Schönberger, Kenneth Cukier
A revelatory exploration of the hottest trend in technology and the dramatic impact it will have on the economy, science, and society at large. Which paint color is most likely to tell you that a used car is in good shape? How can officials identify the most dangerous New York City manholes before they explode? And how did Google searches predict the spread of the H1N1 flu outbreak?

You May Also Like…

0 Comments

Submit a Comment

Your email address will not be published. Required fields are marked *

Did You Know Saccharin Was An Accidental Invention?

Did you know? The artificial sweetener Saccharin was got invented in 1879. The saccharin is a sugar substitute that was found during investigating the oxidation of o-toluene sulfonamide. It was discovered by the chemists' Ira Remsen and Constantin Fahlberg. This way...

Top 500+ Loneliness Quotes You Should Read Right Now

There are millions of lonely people in the world. Some are trying to keep positive themselves, some might be feeling hard to cope with the situation. You should read these 500+ quotes collection about Loneliness to know more about. Share these quotes using hashtags -...

Top 50+ Interesting Fun Facts About Horses You Should Know

50+ interesting fun facts about horses you should know about. Horses are the most loved animals around the world. They've been roaming the human societies for 5000 years. #FunFacts, #Top50+, #Horses, #InterestingFacts, #FactsAboutHorses, #HorseFunFacts, #AmazingFacts,...

Did You Know These Top 36+ Interesting Facts About Snooker?

What is Snooker anyway? Snooker is played on a baize-covered table, 12 feet by 6 feet, using a cue, one white cue ball, 15 red balls, and 6 of various colors. Each color is assigned a point value. A red ball is worth 1 point; a yellow, 2; green, 3; Brown, 4; blue, 5;...

Top 20 Strange, Weird Facts You Should Know About

The world is one of the weirdest places in the universe. And, being human you are in the first place. Literally, weird facts can refer to suggesting something supernatural; unearthly. Here are 20 one of the weirdest facts. Ants communicate with each other using the...

Pin It on Pinterest

Share This