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Intro to Data Science

October 26, 2013

Introduction to Data Science (version 2) is a free ebook on basic data science concepts and examples implemented using the R programming language. The book was written by Professor Jeffrey Stanton from Syracuse University.

The book consists of 17 chapters with the first 5 as a very gentle introduction to organizing and looking at data and also getting started with the R programming language and its APIs and the R-Studio IDE. Chapter 6 opens with a description of 4 noteworthy mathematicians and their contributions that made data science possible: Francis Galton – who coined ‘eugenics’ and half-cousin of Charles Darwin, Karl Pearson – Galton’s junior mathematics partner, William Sealy Gosset – statistician and chemist that worked for Guinness Brewing to bring sampling to beer brewing batches, and Ronald Fisher.

Chapter 7 focus on various ways of conducting sampling from a data set, and replicating that sampling repeatedly so the average results would converge. Another valid approach would be as items were selected for sampling, they’d immediately be replaced (put back in for possible repeated selection). The following chapter on Big Data focused on the 3 V’s: Velocity, Volume, and Variety of the data. Alternative tools like SAS, SPSS, and Processing were briefly mentioned for their pros and cons. Also Student’s t-test was described. Examples of how to use data provided by data.gov were also provided. Chapter 10 used twitter data with an R package “twitteR” and used Poisson distribution to measure popularity of tweets.

Later chapters focused on various packages to work on corpus of text using packages like: stringR, tm, wordcloud, gdata, RMySQL, ROracle, RPostgreSQL, RHadoop, PBSMapping, RCurl. Chapter 16 demonstrated how to perform linear regressions using abline() and lm() for multiple regressions.

Each chapter in the book ended with a list of R APIs used with a brief description plus a little challenge based on the subject covered in the chapter.

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