<div><p>Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that&#8217;s so clouded in hype? This insightful book, based on Columbia University&#8217;s Introduction to Data Science class, tells you what you need to know.</p><p>In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you&#8217;re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.</p><p>Topics include:</p><ul><li>Statistical inference, exploratory data analysis, and the data science process</li><li>Algorithms</li><li>Spam filters, Naive Bayes, and data wrangling</li><li>Logistic regression</li><li>Financial modeling</li><li>Recommendation engines and causality</li><li>Data visualization</li><li>Social networks and data journalism</li><li>Data engineering, MapReduce, Pregel, and Hadoop</li></ul><p><i>Doing Data Science</i> is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O&#8217;Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.</p></div>