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Data Science Overview

Data Science

  • Descriptive Analyses - Describe a set of data. E.g., census data, Google NGram viewer
  • Exploratory Analyses - Finding relationships you didn’t know about. Should not be used for generalizing or prediction.
  • Inferential Analyses - Use a relatively small sample of data to infer for bigger population.
  • Predictive Analyses - To use the data on some objects to predict values for another object.
  • Causal Analyses -



  1. R in a Nutshell - A quick and practical reference to learn what is becoming the standard for developing statistical software. * Statistics in a Nutshell - An introduction and reference for anyone with no previous background in statistics. * Data Analysis with Open Source Tools - This book shows you how to think about data and the results you want to achieve with it. * Programming Collective Intelligence - Learn how to build web applications that mine the data created by people on the Internet. * Beautiful Data - Learn from the best data practitioners in the field about how wide-ranging — and beautiful — working with data can be. * Beautiful Visualization - This book demonstrates why visualizations are beautiful not only for their aesthetic design, but also for elegant layers of detail. * Head First Statistics - This book teaches statistics through puzzles, stories, visual aids, and real-world examples. * Head First Data Analysis - Learn how to collect your data, sort the distractions from the truth, and find meaningful patterns.