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

Last updated on 2019-11-01

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 Getting Started with R and RStudio 

2 R Basics 

3 Programming basics 

4 The tidyverse

5 Importing data 

Part II Data Visualization 

6 Introduction to data visualization

7 ggplot2 

8 Visualizing data distributions 

9 Data visualization in practice 

10 Data visualization principles 

11 Robust summaries 

Part III Statistics with R 

12 Introduction to Statistics with R 

13 Probability 

14 Random variables 

15 Statistical Inference 

16 Statistical models 

17 Regression

18 Linear Models

19 Association is not causation

Part IV Data Wrangling

20 Introduction to Data Wrangling

21 Reshaping data

22 Joining tables

23 Web Scraping

24 String Processing

25 Parsing Dates and Times

26 Text mining

Part V Machine Learning

27 Introduction to Machine Learning

28 Smoothing

29 Cross validation

30 The caret package

31 Examples of algorithms

32 Machine learning in practice

33 Large datasets

34 Clustering

Part VI Productivity tools 

35 Introduction to productivity tools

36 Organizing with Unix

37 Git and GitHub

38 Reproducible projects with RStudio and R markdown

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