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Data Science Books
Showing 1-50 of 5,350
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking (Paperback)
by (shelved 250 times as data-science)
avg rating 4.12 — 2,688 ratings — published 2013
Storytelling with Data: A Data Visualization Guide for Business Professionals (Paperback)
by (shelved 227 times as data-science)
avg rating 4.38 — 8,603 ratings — published 2015
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
by (shelved 200 times as data-science)
avg rating 4.59 — 2,368 ratings — published 2013
Python for Data Analysis (Paperback)
by (shelved 197 times as data-science)
avg rating 4.17 — 2,490 ratings — published 2011
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (Hardcover)
by (shelved 185 times as data-science)
avg rating 3.87 — 31,146 ratings — published 2016
The Signal and the Noise: Why So Many Predictions Fail—But Some Don't (Hardcover)
by (shelved 184 times as data-science)
avg rating 3.97 — 53,217 ratings — published 2012
The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Hardcover)
by (shelved 181 times as data-science)
avg rating 4.43 — 1,916 ratings — published 2001
Data Science from Scratch: First Principles with Python (ebook)
by (shelved 180 times as data-science)
avg rating 3.90 — 1,166 ratings — published 2015
Naked Statistics: Stripping the Dread from the Data (Paperback)
by (shelved 173 times as data-science)
avg rating 3.95 — 15,566 ratings — published 2012
Hands-On Machine Learning with Scikit-Learn and TensorFlow (ebook)
by (shelved 172 times as data-science)
avg rating 4.55 — 2,880 ratings — published 2017
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (Kindle Edition)
by (shelved 136 times as data-science)
avg rating 4.52 — 1,255 ratings — published 2016
Python Data Science Handbook: Essential Tools for Working with Data (Paperback)
by (shelved 136 times as data-science)
avg rating 4.30 — 679 ratings — published 2016
Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are (Hardcover)
by (shelved 135 times as data-science)
avg rating 3.90 — 43,316 ratings — published 2017
The Art of Statistics: How to Learn from Data (Hardcover)
by (shelved 131 times as data-science)
avg rating 4.15 — 6,023 ratings — published 2019
Practical Statistics for Data Scientists: 50 Essential Concepts (Paperback)
by (shelved 128 times as data-science)
avg rating 4.02 — 553 ratings — published
Data Smart: Using Data Science to Transform Information into Insight (Paperback)
by (shelved 125 times as data-science)
avg rating 4.12 — 1,022 ratings — published 2013
Doing Data Science: Straight Talk from the Frontline (Paperback)
by (shelved 124 times as data-science)
avg rating 3.73 — 569 ratings — published 2013
The Visual Display of Quantitative Information (Hardcover)
by (shelved 112 times as data-science)
avg rating 4.39 — 8,806 ratings — published 1983
Pattern Recognition and Machine Learning (Information Science and Statistics)
by (shelved 101 times as data-science)
avg rating 4.32 — 1,924 ratings — published
Invisible Women: Data Bias in a World Designed for Men (Hardcover)
by (shelved 98 times as data-science)
avg rating 4.33 — 185,111 ratings — published 2019
Deep Learning (ebook)
by (shelved 97 times as data-science)
avg rating 4.43 — 2,180 ratings — published 2016
How to Lie with Statistics (Paperback)
by (shelved 82 times as data-science)
avg rating 3.83 — 18,725 ratings — published 1954
Algorithms to Live By: The Computer Science of Human Decisions (Hardcover)
by (shelved 76 times as data-science)
avg rating 4.12 — 36,223 ratings — published 2016
Designing Data-Intensive Applications (ebook)
by (shelved 76 times as data-science)
avg rating 4.69 — 11,403 ratings — published 2015
Introduction to Machine Learning with Python: A Guide for Data Scientists (Paperback)
by (shelved 74 times as data-science)
avg rating 4.33 — 605 ratings — published 2015
Deep Learning with Python (Paperback)
by (shelved 73 times as data-science)
avg rating 4.57 — 1,444 ratings — published 2017
The Hundred-Page Machine Learning Book (Paperback)
by (shelved 71 times as data-science)
avg rating 4.25 — 1,499 ratings — published
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python (Paperback)
by (shelved 69 times as data-science)
avg rating 4.22 — 273 ratings — published
Superforecasting: The Art and Science of Prediction (Hardcover)
by (shelved 69 times as data-science)
avg rating 4.07 — 23,234 ratings — published 2015
The Book of Why: The New Science of Cause and Effect (Hardcover)
by (shelved 66 times as data-science)
avg rating 3.92 — 6,866 ratings — published 2018
Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (Paperback)
by (shelved 65 times as data-science)
avg rating 3.67 — 2,127 ratings — published 2013
Big Data: A Revolution That Will Transform How We Live, Work, and Think (Hardcover)
by (shelved 64 times as data-science)
avg rating 3.69 — 8,705 ratings — published 2013
How to Make the World Add Up: Ten Rules for Thinking Differently About Numbers (Paperback)
by (shelved 60 times as data-science)
avg rating 4.11 — 8,630 ratings — published 2020
The Art of Data Science: A Guide for Anyone Who Works with Data (ebook)
by (shelved 60 times as data-science)
avg rating 3.70 — 302 ratings — published 2015
Applied Predictive Modeling (Hardcover)
by (shelved 59 times as data-science)
avg rating 4.40 — 344 ratings — published 2013
Lean Analytics: Use Data to Build a Better Startup Faster (Hardcover)
by (shelved 57 times as data-science)
avg rating 4.10 — 8,262 ratings — published 2013
Dataclysm: Who We Are (When We Think No One's Looking)
by (shelved 55 times as data-science)
avg rating 3.73 — 12,543 ratings — published 2014
Think Stats (Paperback)
by (shelved 55 times as data-science)
avg rating 3.64 — 471 ratings — published 2011
Python Machine Learning (Paperback)
by (shelved 54 times as data-science)
avg rating 4.24 — 761 ratings — published 2015
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (Hardcover)
by (shelved 49 times as data-science)
avg rating 3.73 — 6,564 ratings — published 2015
Machine Learning: A Probabilistic Perspective (Hardcover)
by (shelved 48 times as data-science)
avg rating 4.35 — 522 ratings — published
Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists (Paperback)
by (shelved 45 times as data-science)
avg rating 4.07 — 312 ratings — published 2010
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications (Paperback)
by (shelved 43 times as data-science)
avg rating 4.43 — 1,203 ratings — published 2022
Forecasting: principles and practice (Paperback)
by (shelved 43 times as data-science)
avg rating 4.40 — 321 ratings — published 2013
Mathematics for Machine Learning (Paperback)
by (shelved 41 times as data-science)
avg rating 4.31 — 277 ratings — published
Factfulness: Ten Reasons We're Wrong About the World – and Why Things Are Better Than You Think (Hardcover)
by (shelved 41 times as data-science)
avg rating 4.35 — 208,176 ratings — published 2018
Statistics Done Wrong: The Woefully Complete Guide (Paperback)
by (shelved 41 times as data-science)
avg rating 4.19 — 1,110 ratings — published 2013
Bayesian Data Analysis (Hardcover)
by (shelved 41 times as data-science)
avg rating 4.21 — 544 ratings — published 1995
Artificial Intelligence: A Modern Approach (Hardcover)
by (shelved 39 times as data-science)
avg rating 4.21 — 4,505 ratings — published 1994
Numsense! Data Science for the Layman: No Math Added (Kindle Edition)
by (shelved 39 times as data-science)
avg rating 4.14 — 627 ratings — published
“Data privacy is an illusion because you are not in control. Any information you don’t want out there shouldn’t be shared anywhere on and off the internet.”
―
―
“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.”
― Resort to War: 1816 - 2007
“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.”
― Resort to War: 1816 - 2007












