Total Duration : 27:15 Hours
Total DVDs : 2 DVDs
Course 01. Learning to Program with R
Duration: 4.5 hours
56 tutorial videos
01. Introduction
Introduction And Course Overview
Installing R And R Studio
Navigating R Studio
Packages
Assigning Variables
Numbers, Strings, And Booleans
Workspace Operations
0109 How To Access Your Working Files
02. Basic Operations And Manipulations
Basic Operators
Vectors
Sequences
Basic Statistical Functions
Matrices
Matrix Operations
Basic Matrix Statistics
Generating Random Numbers
String Functions
Dates And Times
03. Plotting
0301 Line Plots
0302 Plotting Arguments
0303 Bar Graphs And Histograms
0304 Scatter Plots
0305 Probability Plots
0306 Combining And Saving Plots
04. Working With Data
0401 Arrays
0402 Lists
0403 Data Frames
0404 Data Import
0405 Missing Data - Part 1
0406 Missing Data - Part 2
0407 Ordering And Sorting
0408 Subsetting And Indexing
0409 Merging Data
0410 Examining Files And Objects
05. Data Analysis
0501 Descriptive Statistics
0502 Apply Functions
0503 Linear Models
0504 Extracting Model Information
0505 Principal Componant Analysis
06. Time Series Data
0601 XTS Objects
0602 ACF Plots
0603 Decomposition
0604 Exponential Smoothing
0605 Rolling Functions
0606 ARIMA Models
07. Conditional Statements And Loops
0701 If Statements
0702 For Loops
0703 While Loops
0704 Appending Loops
08. User-Defined Functions
0801 Writing Functions
0802 Debugging Functions
0803 Recursive Functions
09. Saving Data
0901 Saving Different Types Of Data
0902 Additional Resources
Course 02. Introduction to Data Science with R
Duration : 8 hours 36 minutes
Introduction to Data Science with R
Introduction to the Course 15m 29s
The R Language 1
Orientation to R 16m 39s
Data Structures and Types 16m 06s
Lists and Data Frames 18m 24s
The R Language 2
Subsetting 1 24m 15s
Subsetting 2 08m 02s
R Packages 05m 48s
Logical Tests 31m 19s
Missing Values 10m 55s
Visualizing Data
Introduction to ggplot2 07m 44s
Aesthetics 13m 45s
Facetting 07m 17s
Geoms 16m 24s
Position Adjustments 13m 06s
Visualizing Distributions 16m 43s
Visualizing Big Data 09m 05s
Saving Graphs 05m 46s
Adjusting Graphs
Visualizing Map Data 10m 14s
Titles and Coordinate Systems 11m 39s
Scales and Color Schemes 12m 12s
Themes 07m 07s
Axis Labels and Legends 09m 44s
Further Learning 03m 12s
Tidy Data
Reading in Data 09m 19s
Melt 12m 55s
dcast 08m 27s
rbind and cbind 02m 13s
Saving Data 04m 59s
Transforming Data
Line Plots 07m 17s
Filter and Select 04m 58s
Arrange, Mutate, and Summarize 07m 28s
Joining Data Sets 10m 53s
Grouping Data 08m 14s
The tbl Format 03m 06s
Advanced Manipulations 11m 28s
Modeling Basics
Introduction to Modeling 06m 22s
Linear Models and Model Syntax 16m 21s
Model Inference 15m 40s
Categorical Variables 07m 45s
Multivariate Models 18m 06s
Advanced Modeling
Introduction to Variable Selection 11m 17s
Best Subsets Selection 07m 21s
Stepwise Selection 11m 31s
Penalized Regression 04m 15s
Non-linear Models 19m 09s
Logistic Regression 10m 23s
Modeling Resources 02m 39s
Further Learning
Resources for R 03m 39s
Course 03. Expert Data Wrangling with R
Duration : 3 hours 50 minutes
Introduction 06m 30s
Two New Conventions 08m 17s
Data Science for Data Wranglers 14m 20s
Data Manipulation
The dplyr Package 02m 23s
Select Variables 08m 28s
Filter Observations 10m 02s
Derive Variables 06m 03s
Summarize Observations 08m 41s
Group Observations 17m 37s
Re-Arrange Observations 05m 08s
Case Study 1 - TB Counts 08m 26s
Data Science for Data Wranglers, Part 2 - Units of Analysis 14m 32s
Data Tidying
Data Science for Data Wranglers, Part 3 - Tidy Data 10m 45s
Reshape the Layout of Your Data 18m 13s
Separate and Unite Variables 06m 51s
Data Science for Data Wranglers, Part 4 - The Best Format 17m 42s
Combine Data Sets 16m 33s
Case Study 2 - TB Rates 09m 08s
Data Visualization
Data Science for Data Wranglers, Part 5: The Structure of Visualizations 05m 53s
Visualize Observations 08m 28s
Visualize Variables 17m 04s
Conclusion
How to Learn More 09m 35s
Course 04. Writing Great R Code
Duration : 59 minutes
Writing Great R Code - 59 minutes
Course 05. Data Science with Microsoft Azure and R
Duration: 7 hours - 72 tutorial videos
01. Introduction
Introduction
About The Author
02. Overview Of Azure ML
Introduction To Azure ML Studio
Experiments And Workflows In Azure ML Studio
Azure ML Modules
Data I/O In Azure ML
Creating And Evaluating A First Machine Learning Model
Documentation And Examples
03. Introduction To R In Azure ML
Editing, Debugging And Executing R In Azure ML
An Execute R Script Example
The Create R Model Module
04. Data Science Examples
0401 Overview Of Data Science Examples
0402 The Data Science Process
05. Data Munging In Azure ML
0501 Introduction To Data Transformation And Cleaning
0502 Dealing With Metadata
0503 Duplicate And Missing Data
0504 Standardization And Transformation
0505 Errors And Outliers
0506 Quantization And Categories
0507 Combining Data Joins
06. SQL In Azure ML
0601 Introduction To Apply SQL Transformation Module
0602 Apply SQL Transformation Exercise
07. Using The dplyr Package
0701 Intro To dplyr
0702 dplyr Example - Part 1
0703 dplyr Example - Part 2
08. Installing R Packages In Azure ML
0801 Installing R Packages
09. Reshaping Data With tidyr
0901 Reshaping Data With tidyr
10. Time Series Data In Azure ML
1001 Date-Time Classes In Azure ML
1002 POSIXct Example
11. The ggplot2 Package
1101 Intro To ggplot2
1102 ggplot2 Exercise
12. Feature Selection And Dimensionality Reduction
1201 Introduction To Feature Selection And Dimensionality Reduction
1202 Exercise - Filter Based Feature Selection
1203 Exercise - randomForest Feature Selection
1204 Projection Methods for Dimensionality Reduction
13. Functional Programming With R
1301 Introduction To Functional Programming With R
1302 Functional Programming Example
14. Regression Example
1401 Introduction To Regression Example
1402 Data Preparation Example
1403 Examining Correlations
1404 Time Series Plots
1405 Understanding Features With Box Plots
1406 Other Exploratory Plots
1407 Feature Selection
1408 Model Evaluation With Time Series Plots
1409 Model Evaluation Of Residuals - Part 1
1410 Model Evaluation Of Residuals - Part 2
15. Regression Example - Improving the Model
1501 Introduction To Improving the Model
1502 Using An R Model
1503 Creating A New Azure ML Model
1504 Trimming Outliers
1505 Optimizing Model Parameters
1506 Further Improvements And Summary
16. R Object Communications In Azure ML
1601 Introduction To R Object Serialization
1602 R Object Serialization Example
17. Classification Example
1701 Introduction To Classification Example
1702 Data Preparation - Part 1
1703 Data Preparation - Part 2
1704 Exploring The Data
1705 Balance Cases
1706 Feature Selection
1707 Building Initial Models
1708 Model Evaluation
1709 First R Model
1710 Improving the R Model
1711 Summary
18. Azure ML Web Services
1801 Overview Of Publishing Azure ML Models As Web Services
1802 Creating An Azure ML Web Service
1803 Updating An Azure ML Web Service
1804 R Model Publishing
1805 Summary
19. Conclusion
1901 Wrap-Up
Course 06_Using R and Hadoop for Statistical Computation at Scale
Duration : 2.44 Hours
PART -1 : 1 H 20 M
PART -2 : 1 H 24 M
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