Lectures Pdf Graduate Mathematical Data Science
Introduction to Data Science
Data Analysis and Prediction Algorithms with R
by Rafael A Irizarry
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Book Description
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.
This open book is licensed under a Creative Commons License (CC BY-NC-SA). You can download Introduction to Data Science ebook for free in PDF format (55.8 MB).
Table of Contents
Part I
R
Chapter 1
Getting Started with R and RStudio
Chapter 2
R Basics
Chapter 3
Programming basics
Chapter 4
The tidyverse
Chapter 5
Importing data
Part II
Data Visualization
Chapter 6
Introduction to data visualization
Chapter 7
ggplot2
Chapter 8
Visualizing data distributions
Chapter 9
Data visualization in practice
Chapter 10
Data visualization principles
Chapter 11
Robust summaries
Part III
Statistics with R
Chapter 12
Introduction to Statistics with R
Chapter 13
Probability
Chapter 14
Random variables
Chapter 15
Statistical Inference
Chapter 16
Statistical models
Chapter 17
Regression
Chapter 18
Linear Models
Chapter 19
Association is not causation
Part IV
Data Wrangling
Chapter 20
Introduction to Data Wrangling
Chapter 21
Reshaping data
Chapter 22
Joining tables
Chapter 23
Web Scraping
Chapter 24
String Processing
Chapter 25
Parsing Dates and Times
Chapter 26
Text mining
Part V
Machine Learning
Chapter 27
Introduction to Machine Learning
Chapter 28
Smoothing
Chapter 29
Cross validation
Chapter 30
The caret package
Chapter 31
Examples of algorithms
Chapter 32
Machine learning in practice
Chapter 33
Large datasets
Chapter 34
Clustering
Part VI
Productivity tools
Chapter 35
Introduction to productivity tools
Chapter 36
Organizing with Unix
Chapter 37
Git and GitHub
Chapter 38
Reproducible projects with RStudio and R markdown
Book Details
Subject
Computer Science
Publisher
Leanpub
Published
2019
Pages
722
Edition
1
Language
English
PDF Size
55.8 MB
License
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Lectures Pdf Graduate Mathematical Data Science
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