R for Data Science

(DS-R.AJ1) / ISBN : 978-1-64459-310-3
This course includes
Lessons
TestPrep
Hands-On Labs
AI Tutor (Add-on)
11 Review
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About This Course

Get hands-on experience of R for Data Science with the comprehensive course and lab. The lab provides hands-on learning of R programming language with a firm grip on some advanced data analysis techniques. The course and lab deal with the evaluation of data by using available R functions and packages. The course will help you to discover different patterns in datasets with the use of the R language, like cluster analysis, anomaly detection, and association rules. You will also learn to produce data and visual analytics through customizable scripts and commands.

Get the support you need. Enroll in our Instructor-Led Course.

Lessons

13+ Lessons | 110+ Exercises | 76+ Quizzes | 113+ Flashcards | 113+ Glossary of terms

TestPrep

45+ Pre Assessment Questions | 45+ Post Assessment Questions |

Hands-On Labs

38+ LiveLab | 37+ Video tutorials | 01:59+ Hours

1

Preface

  • What this course covers?
  • What you need for this course?
  • Who this course is for?
  • Conventions
2

Data Mining Patterns

  • Cluster analysis
  • Anomaly detection
  • Association rules
  • Questions
  • Summary
3

Data Mining Sequences

  • Patterns
  • Questions
  • Summary
4

Text Mining

  • Packages
  • Questions
  • Summary
5

Data Analysis – Regression Analysis

  • Packages
  • Questions
  • Summary
6

Data Analysis – Correlation

  • Packages
  • Questions
  • Summary
7

Data Analysis – Clustering

  • Packages
  • K-means clustering
  • Questions
  • Summary
8

Data Visualization – R Graphics

  • Packages
  • Questions
  • Summary
9

Data Visualization – Plotting

  • Packages
  • Scatter plots
  • Bar charts and plots
  • Questions
  • Summary
10

Data Visualization – 3D

  • Packages
  • Generating 3D graphics
  • Questions
  • Summary
11

Machine Learning in Action

  • Packages
  • Dataset
  • Questions
  • Summary
12

Predicting Events with Machine Learning

  • Automatic forecasting packages
  • Questions
  • Summary
13

Supervised and Unsupervised Learning

  • Packages
  • Questions
  • Summary

0

Preface

  • R Studio Sandbox
1

Data Mining Patterns

  • Plotting a Graph by Performing k-means Clustering
  • Calculating K-medoids Clustering
  • Displaying the Hierarchical Cluster
  • Plotting Graphs By Performing Expectation-Maximization
  • Plotting the Density Values
  • Computing the Outliers for a Set
  • Calculating Anomalies
  • Using the apriori Rules Library
2

Data Mining Sequences

  • Using eclat to Find Similarities in Adult Behavior
  • Finding Frequent Items in a Dataset
  • Evaluating Associations in a Shopping Basket
  • Determining and Visualizing Sequences
  • Computing LCP, LCS, and OMD
3

Text Mining

  • Manipulating Text
  • Analyzing the XML Text
4

Data Analysis – Regression Analysis

  • Performing Simple Regression
  • Performing Multiple Regression
  • Performing Multivariate Regression Analysis
5

Data Analysis – Correlation

  • Performing Tetrachoric Correlation
6

Data Analysis – Clustering

  • Estimating the Number of Clusters Using Medoids
  • Performing Affinity Propagation Clustering
7

Data Visualization – R Graphics

  • Grouping and Organizaing Bivariate Data
  • Plotting Points on a Map
8

Data Visualization – Plotting

  • Displaying a Histogram of Scatter Plots
  • Creating an Enhanced Scatter Plot
  • Constructing a Bar Plot
  • Producing a Word Cloud
9

Data Visualization – 3D

  • Generating a 3D Graphic
  • Producing a 3D Scatterplot
10

Machine Learning in Action

  • Finding a Dataset
  • Making a Prediction
11

Predicting Events with Machine Learning

  • Using Holt Exponential Smoothing
12

Supervised and Unsupervised Learning

  • Developing a Decision Tree
  • Producing a Regression Model
  • Understanding Instance-Based Learning
  • Performing Cluster Analysis
  • Constructing a Multitude of Decision Trees

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