Introduction to Data Science
Introduction to Data Science
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Introduction to Data Science

Last updated on 2019-03-17

About the Book

The demand for skilled data science practitioners in industry, academia, and government is rapidly growing. This book introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression and machine learning. It also helps you develop skills such as R programming, data wrangling with dplyr, data visualization with ggplot2, algorithm building with caret, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation with knitr and R markdown. The book is divided into six parts: R, Data Visualization, Data Wrangling, Probability, Inference and Regression with R, Machine Learning, and Productivity Tools. Each part has several chapters meant to be presented as one lecture. The book includes dozens of exercises distributed across most chapters. 

About the Author

Rafael A Irizarry
Rafael A Irizarry

Rafael Irizarry is a Professor of Biostatistics and Computational Biology at the Dana Farber Cancer Institute and Biostatistics at the Harvard T.H. Chan School of Public Health . For the past 17 years, Dr. Irizarry’s research has focused on the analysis of genomics data. 

Table of Contents

Part I R


1 Installing R and RStudio 

2 Getting Started with R and RStudio 

3 R Basics 

4 Programming basics 

5 The tidyverse

6 Importing data 


Part II Data Visualization 

7 Introduction to data visualization

8 ggplot2 

9 Visualizing data distributions 

10 Data visualization in practice 

11 Data visualization principles 

12 Robust summaries 


Part III Statistics with R 


13 Introduction to Statistics with R 

14 Probability 

15 Random variables 

16 Statistical Inference 

17 Statistical models 

18 Regression

19 Linear Models

20 Association is not causation


Part IV Data Wrangling


21 Introduction to Data Wrangling

22 Reshaping data

23 Joining tables

24 Web Scraping

25 String Processing

26 Parsing Dates and Times

27 Text mining


Part V Machine Learning


28 Introduction to Machine Learning

29 Smoothing

30 Cross validation

31 The caret package

32 Examples of algorithms

33 Machine learning in practice

34 Large datasets

35 Clustering


Part VI Productivity tools 


36 Introduction to productivity tools

37 Accessing the terminal and installing Git

38 Organizing with Unix

39 Git and GitHub

40 Reproducible projects with RStudio and R markdown


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