Introduction to Data Science
Introduction to Data Science
Data Analysis and Prediction Algorithms with R
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.
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
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
14 Random variables
15 Statistical Inference
16 Statistical models
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
29 Cross validation
30 The caret package
31 Examples of algorithms
32 Machine learning in practice
33 Large datasets
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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