Genres

Data Science

Lists

How to Lie with Statistics by Darrell Huff

The Politics of Large Numbers by Alain Desrosières

Philosophy, Ethics, and History of Statistics and Data Science

53 books — 4 voters

The Elements of Statistical Learning by Trevor Hastie

The Visual Display of Quantitative Information by Edward R. Tufte

An Introduction to Statistical Learning by Gareth James

Data Science - Learning About Data

136 books — 126 voters

Deep Learning for Coders with Fastai and Pytorch by Jeremy Howard

Learning Deeper

3 books — 1 voter

Designing Data-Intensive Applications by Martin Kleppmann

Making Sense of Stream Processing by Martin Kleppmann

Data Engineering Group

24 books — 5 voters

Math Adventures with Python by Peter Farrell

Humble Book Bundle: Data Science by No Starch Press

15 books — 2 voters

Linear Algebra and Its Applications by Gilbert Strang

Introduction to Probability Models by Sheldon M. Ross

A First Course in Probability by Sheldon M. Ross

14 books — 1 voter

Featured News & Interviews

Great New Books to Read for Hispanic Heritage Month

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“ Successful AI initiatives start with the right problems, but the right problems don’t necessarily come from your data scientists. They can come from leaders, domain experts, and innovators who sit close to the daily business challenges in your organization. Still, it takes practice to develop the vision for spotting AI opportunities... ”
― Kavita Ganesan, The Business Case for AI: A Leader's Guide to AI Strategies, Best Practices & Real-World Applications

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“ In the midst of World War II, Quincy Wright, a leader in the quantitative study of war, noted that people view war from contrasting perspectives: “To some it is a plague to be eliminated; to others, a crime which ought to be punished; to still others, it is an anachronism which no longer serves any purpose. On the other hand, there are some who take a more receptive attitude toward war, and regard it as an adventure which may be interesting, an instrument which may be legitimate and appropriat In the midst of World War II, Quincy Wright, a leader in the quantitative study of war, noted that people view war from contrasting perspectives: “To some it is a plague to be eliminated; to others, a crime which ought to be punished; to still others, it is an anachronism which no longer serves any purpose. On the other hand, there are some who take a more receptive attitude toward war, and regard it as an adventure which may be interesting, an instrument which may be legitimate and appropriate, or a condition of existence for which one must be prepared” Despite the millions of people who died in that most deadly war, and despite widespread avowals for peace, war remains as a mechanism of conflict resolution. Given the prevalence of war, the importance of war, and the enormous costs it entails, one would assume that substantial efforts would have been made to comprehensively study war. However, the systematic study of war is a relatively recent phenomenon. Generally, wars have been studied as historically unique events, which are generally utilized only as analogies or examples of failed or successful policies. There has been resistance to conceptualizing wars as events that can be studied in the aggregate in ways that might reveal patterns in war or its causes. For instance, in the United States there is no governmental department of peace with funding to scientifically study ways to prevent war, unlike the millions of dollars that the government allocates to the scientific study of disease prevention. This reluctance has even been common within the peace community, where it is more common to deplore war than to systematically figure out what to do to prevent it. Consequently, many government officials and citizens have supported decisions to go to war without having done their due diligence in studying war, without fully understanding its causes and consequences. The COW Project has produced a number of interesting observations about wars. For instance, an important early finding concerned the process of starting wars. A country’s goal in going to war is usually to win. Conventional wisdom was that the probability of success could be increased by striking first. However, a study found that the rate of victory for initiators of inter-state wars (or wars between two countries) was declining: “Until 1910 about 80 percent of all interstate wars were won by the states that had initiated them. . . . In the wars from 1911 through 1965, however, only about 40 percent of the war initiators won.” A recent update of this analysis found that “pre-1900, war initiators won 73% of wars. Since 1945 the win rate is 33%.”. In civil war the probability of success for the initiators is even lower. Most rebel groups, which are generally the initiators in these wars, lose. The government wins 57 percent of the civil wars that last less than a year and 78 percent of the civil wars lasting one to five years. So, it would seem that those initiating civil and inter-state wars were not able to consistently anticipate victory. Instead, the decision to go to war frequently appears less than rational. Leaders have brought on great carnage with no guarantee of success, frequently with no clear goals, and often with no real appreciation of the war’s ultimate costs. This conclusion is not new. Studying the outbreak of the first carefully documented war, which occurred some 2,500 years ago in Greece, historian Donald Kagan concluded: “The Peloponnesian War was not caused by impersonal forces, unless anger, fear, undue optimism, stubbornness, jealousy, bad judgment and lack of foresight are impersonal forces. It was caused by men who made bad decisions in difficult circumstances.” Of course, wars may also serve leaders’ individual goals, such as gaining or retaining power. Nonetheless, the very government officials who start a war are sometimes not even sure how or why a war started. ...more ”
― Frank Wayman, Resort to War: 1816 - 2007

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