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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’s so clouded in hype? This insightful book, based on Columbia University’s 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, Microsoftand eBay share new algorithms, methodsand models by presenting case studies and the code they use. If you’re familiar with linear algebra, probabilityand statisticsand have programming experience, this book is an ideal introduction to data science.
Topics include:
Statistical inference, exploratory data analysisand the data science process
Algorithms
Spam filters, Naive Bayesand data wrangling
Logistic regression
Financial modeling
Recommendation engines and causality
Data visualization
Social networks and data journalism
Data engineering, MapReduce, Pregeland Hadoop
Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corpand data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.
