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Doing data science straight talk from the frontline

A guide to the usefulness of data science covers such topics as algorithms, logistic regression, financial modeling, data visualization, and data engineering.

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  • "Doing data science"
  • "Straight talk from the frontline"@en
  • "Doing data science : straight talk from the frontline"

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  • "A guide to the usefulness of data science covers such topics as algorithms, logistic regression, financial modeling, data visualization, and data engineering."@en
  • "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 is so clouded in hype? This book 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 are 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."
  • "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 processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing 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."@en

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  • "Livres électroniques"
  • "Ressource Internet (Descripteur de forme)"
  • "Livre électronique (Descripteur de forme)"
  • "Electronic books"@en
  • "Electronic books"

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  • "Doing data science : [straight talk from the frontline]"
  • "Doing data science straight talk from the frontline"@en
  • "Doing Data Science"
  • "Doing data science"
  • "Doing data science"@en
  • "Doing data science [straight talk form the frontline]"
  • "Doing data science : straight talk from the frontline"@en
  • "Doing data science : straight talk from the frontline"
  • "Badanie danych. Raport z pierwszej linii działań"
  • "Doing Data Science : [straight talk from the frontline]"
  • "Badanie danych"@en
  • "Badanie danych Raport z pierwszej linii działań"

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