## Video Description

Learn R and get comfortable with data science

In Detail

Excited by the endless possibilities offered by the fields of data science and data analysis? Let R set you on your way!

Data scientists, statisticians and analysts use R for statistical analysis, data visualization and predictive modeling. R gives aspiring analysts and data scientists the ability to represent complex sets of data in an impressive way.

Make yourself comfortable in R and get deep into data science using R with this Learning Path.

Prerequisites: Requires no programming knowledge - we’re covering basics of R too!

• Introduction to R Programming

• Getting Started with R for Data Science

• Learning Data Mining with R

• Learning R for Data Visualization

• R for Data Science Solutions

• PATH PRODUCTS

This path navigates across the following products (in sequential order):

• Introduction to R Programming (3h 46m)

• Learning Data Mining with R (2h 17m)

• R for Data Science Solutions (5h 32m)

1. Chapter 1 : Introduction to R Programming
1. The Course Overview 00:04:54
2. Installing R 00:03:46
3. Installing RStudio 00:04:36
4. Installing Packages 00:04:50
5. Data Types and Data Structures 00:03:05
6. Vectors 00:05:44
7. Random Numbers, Rounding, and Binning 00:04:00
8. Missing Values 00:02:47
9. The which() Operator 00:03:11
10. Lists 00:04:35
11. Set Operations 00:02:09
12. Sampling and Sorting 00:02:52
13. Check Conditions 00:02:17
14. For Loops 00:02:34
15. Dataframes 00:08:30
16. Importing and Exporting Data 00:06:30
17. Matrices and Frequency Tables 00:03:41
18. Merging Dataframes 00:02:26
19. Aggregation 00:02:48
20. Melting and Cross Tabulations with dcast() 00:03:58
21. Dates 00:05:35
22. String Manipulation 00:05:14
23. Functions 00:05:34
24. Debugging and Error Handling 00:04:30
25. Fast Loops with apply() 00:04:27
26. Fast Loops with sapply(), lapply() and vapply() 00:02:00
27. Creating and Customizing an R Plot 00:07:03
28. Drawing Plots with 2 Y Axes 00:02:23
29. Multiplots and Custom Layouts 00:03:08
30. Creating Basic Graph Types 00:04:47
31. Univariate Analysis 00:06:16
32. Normal Distribution, Central Limit Theorem, and Confidence Intervals 00:05:32
33. Correlation and Covariance 00:03:03
34. Chi-sq Statistic 00:04:42
35. ANOVA 00:04:54
36. Statistical Tests 00:05:14
37. Project 1 – Data Munging and Summarizing 00:11:31
38. Project 2 – Visualization with Base Graphics 00:05:42
39. Project 3 – Statistical Inference 00:03:50
40. Pipes with Magrittr 00:05:21
41. The 7 Data Manipulation Verbs 00:05:19
42. Aggregation and Special Functions 00:03:36
43. Two Table Verbs 00:02:43
44. Working With Databases 00:05:30
45. Understanding Basics, Filter, and Select 00:07:34
46. Understanding Syntax, Creating and Updating Columns 00:04:06
47. Aggregating Data, .N, and .I 00:04:21
48. data.table 00:04:17
49. Fast Loops with set(), Keys, and Joins 00:09:13
2. Chapter 2 : Getting Started with R for Data Science
1. The Course Overview 00:04:15
2. What is R? 00:02:34
3. The Structure of the Language 00:03:52
4. Data Structures within R 00:05:57
5. Writing a Simple Program in R 00:04:33
6. The Structure of a DataFrame 00:05:35
7. Creating a DataFrame from a CSV File 00:02:41
8. Creating a DataFrame from a Zip File 00:03:03
9. Creating a DataFrame from a Database 00:06:55
10. The Tools Available for Cleaning Data 00:06:50
11. Dealing with Null Values 00:04:03
12. Standardizing Date Formats 00:03:13
13. Blending Multiple DataFrames 00:04:22
14. What Is a Codebook and Why Create One? 00:03:50
15. Creating the Codebook Using Standard R API Functionality 00:02:28
16. Manually Creating a Custom Codebook 00:03:32
17. Introduction to Data Mining and Analysis 00:03:49
18. The Tools and Techniques for Creating the Story 00:03:32
19. Regression Analysis with R 00:02:24
20. Clustering Data with R 00:03:20
21. Classifying Data with R 00:04:01
22. Data Visualization Tools 00:03:09
23. Creating Static Visualization Plots 00:03:47
24. Creating Interactive Plots 00:02:01
25. Publishing the Graphics 00:02:06
26. What's Next? 00:03:12
3. Chapter 3 : Learning Data Mining with R
1. The Course Overview 00:03:31
2. Getting Started with R 00:05:06
3. Data Preparation and Data Cleansing 00:04:10
4. The Basic Concepts of R 00:05:46
5. Data Frames and Data Manipulation 00:05:29
6. Data Points and Distances in a Multidimensional Vector Space 00:03:59
7. An Algorithmic Approach to Find Hidden Patterns in Data 00:06:24
8. A Real-world Life Science Example 00:04:29
9. Example – Using a Single Line of Code in R 00:04:00
10. R Data Types 00:05:44
11. R Functions and Indexing 00:04:15
12. S3 Versus S4 – Object-oriented Programming in R 00:04:45
14. Introduction to Graphs 00:02:09
15. Different Association Types 00:05:27
16. The Apriori Algorithm 00:06:38
17. The Eclat Algorithm 00:03:54
18. The FP-Growth Algorithm 00:03:48
19. Mathematical Foundations 00:06:01
20. The Naive Bayes Classifier 00:03:50
21. Spam Classification with Naïve Bayes 00:03:33
22. Support Vector Machines 00:04:29
23. K-nearest Neighbors 00:03:21
24. Hierarchical Clustering 00:05:45
25. Distribution-based Clustering 00:06:55
26. Density-based Clustering 00:03:12
27. Using DBSCAN to Cluster Flowers Based on Spatial Properties 00:02:25
28. Introduction to Neural Networks and Deep Learning 00:06:09
29. Using the H2O Deep Learning Framework 00:02:28
30. Real-time Cloud Based IoT Sensor Data Analysis 00:06:17
4. Chapter 4 : Learning R for Data Visualization
1. The Course Overview 00:05:32
2. Preview of R Plotting Functionalities 00:03:16
3. Introducing the Dataset 00:03:21
6. Exporting Data 00:04:19
7. Creating Histograms 00:05:01
8. The Importance of Box Plots 00:03:44
9. Plotting Bar Charts 00:02:43
10. Plotting Multiple Variables – Scatterplots 00:03:07
11. Dealing with Time – Time-series Plots 00:02:38
12. Handling Uncertainty 00:04:15
13. Changing Theme 00:03:07
14. Changing Colors 00:03:20
15. Modifying Axis and Labels 00:02:40
17. Adding Text Inside and Outside of the Plot 00:05:02
18. Multi-plots 00:03:59
19. Exporting Plots as Images 00:03:24
20. Adjusting the Page Size 00:02:33
21. Getting Started with Interactive Plotting 00:02:44
22. Creating Interactive Histograms and Box Plots 00:04:55
23. Plotting Interactive Bar Charts 00:03:12
24. Creating Interactive Scatterplots 00:02:58
25. Developing Interactive Time-series Plots 00:03:47
26. Getting Started with Shiny 00:04:09
27. Creating a Simple Website 00:04:52
28. File Input 00:03:09
29. Conditional Panels – UI 00:03:45
30. Conditional Panels – Servers 00:05:31
31. Deploying the Site 00:05:38
5. Chapter 5 : R for Data Science Solutions
1. R Functions and Arguments 00:06:25
2. Understanding Environments 00:02:59
3. Working with Lexical Scoping 00:02:49
4. Understanding Closure 00:02:17
5. Performing Lazy Evaluation 00:01:56
6. Creating Infix Operators 00:02:51
7. Using the Replacement Function 00:02:17
8. Handling Errors in a Function 00:04:31
9. The Debugging Function 00:04:05
11. Reading and Writing CSV Files 00:01:13
12. Scanning Text Files 00:02:21
13. Working with Excel Files 00:01:56
14. Reading Data from Databases 00:04:04
15. Scraping Web Data 00:05:17
16. Renaming the Data Variable 00:02:27
17. Converting Data Types 00:04:03
18. Working with Date Format 00:02:36
20. Filtering Data 00:02:09
21. Dropping Data 00:03:29
22. Merging and Sorting Data 00:01:42
23. Reshaping Data 00:04:00
24. Detecting Missing Data 00:02:42
25. Imputing Missing Data 00:03:15
26. Enhancing a data.frame with a data.table 00:04:50
27. Managing Data with data.table 00:01:40
28. Performing Fast Aggregation with data.table 00:01:14
29. Merging Large Datasets with a data.table 00:01:54
30. Subsetting and Slicing Data with dplyr 00:02:11
31. Sampling Data with dplyr 00:04:14
32. Selecting Columns with dplyr 00:02:10
33. Chaining Operations in dplyr 00:02:41
34. Arranging Rows with dplyr 00:02:09
35. Eliminating Duplicated Rows with dplyr 00:01:26
36. Adding New Columns with dplyr 00:02:40
37. Summarizing Data with dplyr 00:02:10
38. Merging Data with dplyr 00:01:22
39. Creating Basic Plots with ggplot2 00:04:15
40. Changing Aesthetics Mapping 00:03:09
41. Introducing Geometric Objects 00:03:13
42. Performing Transformations 00:03:27
44. Faceting 00:02:07
46. Combining Plots 00:02:04
47. Creating Maps 00:04:39
48. Creating R Markdown Reports 00:02:47
49. Learning the Markdown Syntax 00:03:14
50. Embedding R Code Chunks 00:02:19
51. Creating Interactive Graphics with ggvis 00:02:39
52. Understanding Basic Syntax and Gramma 00:01:57
53. Controlling Axes and Legends and Using Scales 00:02:55
54. Adding Interactivity to a ggvis Plot 00:03:41
55. Creating an R Shiny Document 00:02:16
56. Publishing an R Shiny Report 00:02:29
57. Generating Random Samples 00:02:52
58. Understanding Uniform Distributions 00:01:39
59. Generating Binomial Random Variates 00:02:30
60. Generating Poisson Random Variates 00:02:06
61. Sampling from a Normal Distribution 00:04:08
62. Sampling from a Chi-Squared Distribution 00:02:00
63. Understanding Student's t- Distribution 00:02:11
64. Sampling from a Dataset 00:01:52
65. Simulating the Stochastic Process 00:02:29
66. Getting Confidence Intervals 00:05:54
67. Performing Z-tests 00:03:12
68. Performing Student's t-Tests 00:02:15
69. Conducting Exact Binomial Tests 00:02:09
70. Performing Kolmogorov-Smirnov Tests 00:02:17
71. Working with the Pearson's Chi-Squared Tests 00:01:40
72. Understanding the Wilcoxon Rank Sum and Signed Rank Tests 00:01:48
73. Conducting One-way ANOVA 00:02:39
74. Performing Two-way ANOVA 00:03:02
75. Transforming Data into Transactions 00:05:12
76. Displaying Transactions and Associations 00:03:03
77. Mining Associations with the Apriori Rule 00:04:19
78. Pruning Redundant Rules 00:02:15
79. Visualizing Association Rules 00:02:36
80. Mining Frequent Itemsets with Eclat 00:03:08
81. Creating Transactions with Temporal Information 00:02:53
82. Mining Frequent Sequential Patterns with cSPADE 00:02:42
83. Creating Time Series Data 00:05:12
84. Plotting a Time Series Object 00:02:26
85. Decomposing Time Series 00:02:11
86. Smoothing Time Series 00:05:21
87. Forecasting Time Series 00:02:31
88. Selecting an ARIMA Model 00:03:19
89. Creating an ARIMA Model 00:02:20
90. Forecasting with an ARIMA Model 00:02:11
91. Predicting Stock Prices with an ARIMA Model 00:04:24
92. Fitting a Linear Regression Model with lm 00:05:35
93. Summarizing Linear Model Fits 00:02:14
94. Using Linear Regression to Predict Unknown Values 00:01:38
95. Measuring the Performance of the Regression Model 00:03:46
96. Performing a Multiple Regression Analysis 00:02:54
97. Selecting the Best-Fitted Regression Model with Stepwise Regression 00:03:57
98. Applying the Gaussian Model for Generalized Linear Regression 00:03:23
99. Performing a Logistic Regression Analysis 00:04:17
100. Building a Classification Model with Recursive Partitioning Trees 00:02:42
101. Visualizing Recursive Partitioning Tree 00:02:19
102. Measuring Model Performance with a Confusion Matrix 00:04:31
103. Measuring Prediction Performance Using ROCR 00:03:59
104. Clustering Data with Hierarchical Clustering 00:06:10
105. Cutting Tree into Clusters 00:01:51
106. Clustering Data with the k-means Method 00:01:20
107. Clustering Data with the Density-Based Method 00:02:54
108. Extracting Silhouette Information from Clustering 00:01:45
109. Comparing Clustering Methods 00:02:09
110. Recognizing Digits Using the Density-Based Clustering Method 00:03:12
111. Grouping Similar Text Documents with k-means Clustering Method 00:01:50
112. Performing Dimension Reduction with Principal Component Analysis (PCA) 00:02:12
113. Determining the Number of Principal Components Using a Scree Plot 00:01:52
114. Determining the Number of Principal Components Using the Kaiser Method 00:02:15
115. Visualizing Multivariate Data Using a biplot 00:02:51

## Product Information

• Title: Learning Path: Data Science with R
• Author(s): Taabish Khan - Curator
• Release date: November 2016
• Publisher(s): Packt Publishing
• ISBN: 9781787289192