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Learning Path: Introduction to Data Science with R

Video Description

The R programming language has arguably become the single most important tool for computational statistics, visualization, and data science. With this Learning Path, master the basics that you'll need as a data scientist. You'll work your data like never before.

Table of Contents

  1. Introduction
    1. Introduction And Course Overview 00:01:40
    2. About The Author 00:00:36
    3. Installing R And R Studio 00:04:07
    4. Navigating R Studio 00:05:11
    5. Packages 00:03:40
    6. Assigning Variables 00:03:31
    7. Numbers, Strings, And Booleans 00:05:26
    8. Workspace Operations 00:07:59
    9. How To Access Your Working Files 00:01:15
  2. Basic Operations And Manipulations
    1. Basic Operators 00:04:36
    2. Vectors 00:07:26
    3. Sequences 00:04:45
    4. Basic Statistical Functions 00:06:27
    5. Matrices 00:06:51
    6. Matrix Operations 00:05:29
    7. Basic Matrix Statistics 00:04:12
    8. Generating Random Numbers 00:08:01
    9. String Functions 00:07:46
    10. Dates And Times 00:07:56
  3. Plotting
    1. Line Plots 00:08:20
    2. Plotting Arguments 00:04:31
    3. Bar Graphs And Histograms 00:06:49
    4. Scatter Plots 00:06:29
    5. Probability Plots 00:06:34
    6. Combining And Saving Plots 00:04:51
  4. Working With Data
    1. Arrays 00:04:11
    2. Lists 00:03:58
    3. Data Frames 00:05:35
    4. Data Import 00:05:34
    5. Missing Data - Part 1 00:03:56
    6. Missing Data - Part 2 00:04:10
    7. Ordering And Sorting 00:04:17
    8. Subsetting And Indexing 00:04:41
    9. Merging Data 00:04:21
    10. Examining Files And Objects 00:03:49
  5. Data Analysis
    1. Descriptive Statistics 00:05:50
    2. Apply Functions 00:03:38
    3. Linear Models 00:06:08
    4. Extracting Model Information 00:05:06
    5. Principal Componant Analysis 00:02:49
  6. Time Series Data
    1. XTS Objects 00:03:59
    2. ACF Plots 00:02:53
    3. Decomposition 00:02:43
    4. Exponential Smoothing 00:04:05
    5. Rolling Functions 00:04:26
    6. ARIMA Models 00:04:36
  7. Conditional Statements And Loops
    1. If Statements 00:05:59
    2. For Loops 00:04:24
    3. While Loops 00:03:02
    4. Appending Loops 00:04:01
  8. User-Defined Functions
    1. Writing Functions 00:04:37
    2. Debugging Functions 00:05:10
    3. Recursive Functions 00:02:07
  9. Saving Data
    1. Saving Different Types Of Data 00:02:02
    2. Additional Resources 00:02:00
  10. Introduction to Data Science with R
    1. Introduction to the Course 00:15:30
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. Further Learning
    1. Resources for R 00:03:39
    2. Writing Great R Code 00:59:13
  20. Introduction
    1. Introduction 00:01:24
    2. About The Author 00:01:02
    3. How To Access Your Working Files 00:01:15
  21. Data Visualization Principles
    1. What Is A Data Visualization And Why Do We Visualize Data? 00:02:40
    2. Types Of Variables And Encodings 00:03:35
    3. Choosing Effective Visual Encodings 00:03:30
    4. Color 00:03:08
  22. Introduction To Visualization In ggplot2
    1. Getting Set Up With ggplot2 00:03:34
    2. ggplot2's Grammar Of Graphics 00:03:30
    3. Tidy Data And Data Manipulation 00:04:37
    4. Basic Plots 00:05:51
    5. Viewing Data Distributions 00:03:52
  23. Adding Complexity To Visualizations
    1. Additional Visual Encodings: Color, Shape, And Size 00:02:24
    2. Small Multiples 00:02:59
    3. Smoothing Functions 00:03:11
  24. Customizing Your Plots
    1. Axis Scales 00:03:06
    2. Alternate Color Scales 00:03:11
    3. Themes 00:03:14
    4. Customizing Titles, Axis Labels, And Legends 00:03:50
  25. Conclusion
    1. Conclusion 00:01:37
  26. Introduction
    1. What is R Markdown? 00:07:08
    2. flights.R 00:06:48
    3. Orientation 00:05:21
  27. Reproducible Research
    1. Markdown 00:07:55
    2. Knitr Code Chunks 00:12:37
    3. Advanced knitr 00:10:13
    4. Tables 00:09:04
    5. Parameters 00:15:20
  28. Dynamic Reports
    1. Output Formats 00:08:51
    2. Slides Syntax 00:04:32
    3. Customizing Output 00:07:54
    4. Citations and Bibliographies 00:07:59
    5. Interactive Documents 00:06:04
    6. Templates 00:15:49
  29. Where to now?
    1. Resources 00:03:53
  30. Introduction
    1. Welcome 00:06:00
  31. How to build an App
    1. Your First App 00:06:38
    2. Components of an App 00:05:04
    3. Build Your App Around Inputs... 00:03:58
    4. …and Outputs 00:02:43
    5. Tell the Server How to Assemble Outputs From Inputs 00:03:54
    6. Create Reactivity 00:04:02
    7. Your First App Recap 00:02:41
    8. File Structure 00:05:40
    9. Share Your App 00:05:25
  32. Reactive Programming
    1. Vocabulary 00:07:18
    2. Reactive Programming 00:04:19
    3. Display Output with render*() Functions 00:04:54
    4. Build Reusable Objects with reactive() 00:09:24
    5. Prevent Reactions with isolate() 00:05:23
    6. Delay Reactions with eventReactive() 00:07:05
    7. Trigger Side Effects with observeEvent() 00:04:57
    8. Maintain State with reactiveValues() 00:05:10
    9. Observers versus Reactive Expressions, Part 1: Side Effects 00:07:24
    10. Observers versus Reactive Expressions, Part 2: The Key to Shiny 00:05:14
    11. Schedule Reactions with invalidateLater() 00:07:48
    12. Track Data with reactivePoll() and reactiveFileReader() 00:06:34
    13. Interactive Visualizations 00:12:26
    14. Avoid Repetition 00:04:28
  33. Understanding UI
    1. Shiny UI 00:04:26
    2. htmlTemplate() 00:09:02
    3. R Functions to Write HTML 00:07:58
    4. Panels 00:06:20
    5. Layers 00:07:20
    6. Formatting 00:07:26
    7. Raw Input 00:07:45
  34. Where now?
    1. Resources 00:04:50