Description By Rachel Schutt

Not sure why this book receives such high ratings. I think the authors are quite open about the fact that this book was derived from the class they taught at university. It reads like a bunch of class notes really not well written or that well organized. I read it. Description Covers many topics and gives examples. Authors tie subjects to their own work experiences. Description

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 thats so clouded in hype? This insightful book, based on Columbia Universitys Introduction to Data Science class, tells you what you need to know.

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 youre familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.

Topics include:

Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and Hadoop

Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy ONeil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

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