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, Microsoft, and eBay share new algorithms, methods, and
models by presenting case studies and the code they use. If you're
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 process Algorithms Spam filters,
Naive Bayes, and data wrangling Logistic regression Financial
modeling Recommendation engines and causality Data visualization
Social networks and data journalism Data 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 O'Neil, a senior data
scientist at Johnson Research Labs, who attended and blogged about
the course.
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