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R Data Science Bootcamp
Introduction
Introduction to Course
Programming and Data Science Training
Course Curriculum Overview
Course FAQs
Course Notes
Windows Set-up
Intro to Windows Set-up
Windows Installation Procedure (6:19)
Mac OS Set-up
Intro to Mac OS Set-up
Mac OS Installation Procedure
Development Environment Overview
Development Environment Overview (0:21)
Guide to RStudio (12:33)
How to Approach this Course
What is Data Science?
R Basics
Intro to R Basics
Arithmetic in R (4:30)
Variables (5:25)
R Basic Data Types (5:31)
Vector Basics (7:35)
Print Formatting
Getting Help with R (2:12)
Vector Operations (4:23)
Comparison Operators (6:30)
Vector Indexing and Slicing (9:36)
R Basics Exercises
R Basics Exercises - Solutions Walkthrough
R Matrices
Intro to R Matrices
Introduction to R Matrices (0:48)
Creating a Matrix (10:23)
Matrix Operations (5:22)
Matrix Selection and Indexing (6:34)
Matrix Arithmetic (4:15)
Factor and Categorical Matrices (8:14)
R Matrix Training Exercise (1:00)
R Matrix Exercises
R Matrix Exercises - Solutions Walkthrough
R Data Frames
Introduction to R Data Frames (0:44)
Data Frame Basics (8:43)
Data Frames Indexing and Selection (9:15)
Overview of Data Frame Operations - Part 1 (15:58)
Overview of Data Frame Operations - Part 2 (18:40)
R Data Frames Exercises
R Data Frames Training Exercise (1:06)
R Data Frames Exercises - Solutions Walkthrough
R Lists
Intro to R Lists
List Basics (9:00)
Data Input and Output with R
Intro to Data Input and Output with R
Data Input and Output with R (0:24)
CSV Input and Output (6:09)
Excel Files with R (11:43)
SQL with R (9:56)
Web Scraping with R (6:52)
R Programming Basics
Intro to R Programming Basics
Intro to Programming Basics (0:58)
Logical Operators (8:05)
If, else, and else if Statements (14:59)
Conditional Statements Exercises
Intro to Conditional Statements Exercise (1:27)
Conditional Statements Exercises - Solutions Walkthrough
While Loop (6:53)
For Loops (12:28)
R Loops Exercises
R Loops Exercises - Solutions Walkthrough
Functions (19:15)
R Functions Exercises
Functions Training Exercise Overview (2:14)
R Functions Exercises - Solutions Walkthrough
Advanced Programming Features with R
Intro to Advanced R Programming
Intro to Advanced R Programming (0:53)
Built-in R Features (9:49)
Apply (15:16)
Math Functions with R (3:22)
Regular Expressions (5:16)
Dates and Timestamps (12:07)
Data Manipulation in R
Data Manipulation Overview (0:40)
Guide to Using Dplyr (21:33)
Dplyr Exercises
Dplyr Training Exercise (1:09)
Dplyr Exercises - Solutions Walkthrough
Guide to Using Tidyr (20:30)
Data Visualization with ggplot2
Overview of ggplot2 (6:42)
Histograms (18:37)
Scatterplots (16:59)
Barplots (7:57)
Boxplots (7:01)
2 Variable Plotting with ggplot2 (7:48)
Coordinates and Faceting (10:47)
Themes (5:23)
ggplot2 Training Exercise (2:29)
Intro to Data Visualization Project (2:46)
Interactive Visualizations with Plotly (8:49)
Data Visualization Project
Data Visualization Project - Solutions Walkthrough
Programming Project
Intro to Capstone Project (7:55)
Programming Project
Programming Project - Solution Walkthrough
Congratulations!
Introduction to Machine Learning
Introduction to Machine Learning Section (16:48)
Linear Regression
Linear Regression Lecture (5:26)
Linear Regression with R Part 1 (19:40)
Linear Regression with R Part 2 (20:11)
Linear Regression with R Part 3 (11:53)
Introduction to Linear Regression Project (8:27)
Introduction to Linear Regression Project (8:27)
Linear Regression Project Part 2 (10:55)
Linear Regression Project Part 1 (21:23)
Linear Regression with R Part 3 (11:53)
Logistic Regression
Logistic Regression Lecture (11:37)
Logistic Regression with R Part 1 (20:00)
Logistic Regression Lecture (11:37)
Intro to Logistic Regression Project (1:40)
Logistic Regression Project Solutions part 1 (20:01)
Logistic Regression Project Part 2 (15:04)
Logistic Regression with R Part 2 (18:41)
Logistic Regression Project - Part 3 (13:09)
K Nearest Neighbors
KNN Lecture (5:00)
KNN Lecture (5:00)
KNN with R (19:05)
Introduction to KNN Project (3:17)
KNN Project Solutions (11:22)
Decision Trees and Random Forests
Introduction to Tree Methods Lecture (6:30)
Introduction to Tree Methods Lecture (6:30)
Random Forest with R (12:01)
Intro to Tree Methods Project (1:41)
Tree Methods Project Solution Part 1 (16:42)
Tree Methods Project - Part 2 (4:46)
Support Vector Machines
Support Vector Machine Lecture (4:13)
SVM with R (14:50)
Support Vector Machine Lecture (4:13)
Intro to SVM Project (2:13)
SVM Project Solutions Part 1 (11:04)
SVM Project Part 2 (10:17)
K-means Clustering
K Means Algorithm Lecture (4:50)
K Means Algorithm Lecture (4:50)
Intro to K Means Clustering Project (1:56)
K Means Clustering Project Solutions (17:12)
K Means Clustering with R (9:33)
Natural Language Processing
NLP Lecture (4:25)
NLP Lecture (4:25)
NLP with R Part 1 (4:50)
NLP with R Part 2 (15:56)
Neural Nets
Neural Network Lecture (6:13)
Neural Network Lecture (6:13)
Neural Networks in R Part 1 (13:56)
Neural Networks in R Part 2 (18:15)
Web Scraping with R
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