Now that people are acutely aware that data might make the distinction in an election or a enterprise model, data science as an occupation is gaining flooring. Nevertheless how are you going to get started working in a big-ranging, interdisciplinary topic that’s so clouded in hype? This insightful book, based mostly totally on Columbia School’s Introduction to Data Science class, tells you what you might know.
In a lot of of these chapter-long lectures, data scientists from firms comparable to Google, Microsoft, and eBay share new algorithms, methods, and fashions by presenting case analysis and the code they use. In case you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.
Topics embrace: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 Info Corp, and data science advisor Cathy O’Neil, a senior data scientist at Johnson Evaluation Labs, who attended and blogged about the course.