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Introduction to Data Science with R

Video Description

Learn practical skills for visualizing, transforming, and modeling data in R. This comprehensive video course shows you how to explore and understand data, as well as how to build linear and non-linear models in the R language and environment. It’s ideal whether you’re a non-programmer with no data science experience, or a data scientist switching to R from other software such as SAS or Excel.

RStudio Master Instructor Garrett Grolemund covers the three skill sets of data science: computer programming (with R), manipulating data sets (including loading, cleaning, and visualizing data), and modeling data with statistical methods. You’ll learn R’s syntax and grammar as well as how to load, save, and transform data, generate beautiful graphs, and fit statistical models to the data.

All of the techniques introduced in this video are motivated by real problems that involved real datasets. You’ll get plenty of hands-on experience with R (and not just hear about it!), and lots of help if you get stuck.

Garrett Grolemund is a statistician, teacher, and R developer who works as a data scientist and Master Instructor at RStudio. He’s conducted corporate training in R at Google, eBay, Axciom, and many other companies, and is currently developing a training curriculum for RStudio. Garrett co-authored the lubridate R package and wrote the ggsubplot package. He received his Ph.D at Rice University.

Table of Contents

  1. Introduction to Data Science with R
    1. Introduction to the Course 00:15:30
  2. The R Language 1
    1. Orientation to R 00:16:40
    2. Data Structures and Types 00:16:06
    3. Lists and Data Frames 00:18:25
  3. The R Language 2
    1. Subsetting 1 00:24:15
    2. Subsetting 2 00:08:02
    3. R Packages 00:05:48
    4. Logical Tests 00:31:20
    5. Missing Values 00:10:55
  4. Visualizing Data
    1. Introduction to ggplot2 00:07:45
    2. Aesthetics 00:13:46
    3. Facetting 00:07:18
    4. Geoms 00:16:24
    5. Position Adjustments 00:13:07
    6. Visualizing Distributions 00:16:43
    7. Visualizing Big Data 00:09:05
    8. Saving Graphs 00:05:47
  5. Adjusting Graphs
    1. Visualizing Map Data 00:10:14
    2. Titles and Coordinate Systems 00:11:40
    3. Scales and Color Schemes 00:12:13
    4. Themes 00:07:07
    5. Axis Labels and Legends 00:09:45
    6. Further Learning 00:03:13
  6. Tidy Data
    1. Reading in Data 00:09:19
    2. Melt 00:12:55
    3. dcast 00:08:27
    4. rbind and cbind 00:02:14
    5. Saving Data 00:05:00
  7. Transforming Data
    1. Line Plots 00:07:18
    2. Filter and Select 00:04:58
    3. Arrange, Mutate, and Summarize 00:07:29
    4. Joining Data Sets 00:10:53
    5. Grouping Data 00:08:14
    6. The tbl Format 00:03:06
    7. Advanced Manipulations 00:11:28
  8. Modeling Basics
    1. Introduction to Modeling 00:06:22
    2. Linear Models and Model Syntax 00:16:21
    3. Model Inference 00:15:41
    4. Categorical Variables 00:07:45
    5. Multivariate Models 00:18:07
  9. Advanced Modeling
    1. Introduction to Variable Selection 00:11:18
    2. Best Subsets Selection 00:07:21
    3. Stepwise Selection 00:11:31
    4. Penalized Regression 00:04:16
    5. Non-linear Models 00:19:10
    6. Logistic Regression 00:10:24
    7. Modeling Resources 00:02:39
  10. Further Learning
    1. Resources for R 00:03:39