R for Data Science

(DS-R.AJ1)/ISBN:978-1-64459-310-3

This course includes
Lessons
TestPrep
LiveLab
Mentoring (Add-on)

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.

Lessons

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

TestPrep

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

Hand on lab

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

Here's what you will learn

Download Course Outline

Lessons 1: Preface

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

Lessons 2: Data Mining Patterns

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

Lessons 3: Data Mining Sequences

  • Patterns
  • Questions
  • Summary

Lessons 4: Text Mining

  • Packages
  • Questions
  • Summary

Lessons 5: Data Analysis – Regression Analysis

  • Packages
  • Questions
  • Summary

Lessons 6: Data Analysis – Correlation

  • Packages
  • Questions
  • Summary

Lessons 7: Data Analysis – Clustering

  • Packages
  • K-means clustering
  • Questions
  • Summary

Lessons 8: Data Visualization – R Graphics

  • Packages
  • Questions
  • Summary

Lessons 9: Data Visualization – Plotting

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

Lessons 10: Data Visualization – 3D

  • Packages
  • Generating 3D graphics
  • Questions
  • Summary

Lessons 11: Machine Learning in Action

  • Packages
  • Dataset
  • Questions
  • Summary

Lessons 12: Predicting Events with Machine Learning

  • Automatic forecasting packages
  • Questions
  • Summary

Lessons 13: Supervised and Unsupervised Learning

  • Packages
  • Questions
  • Summary

Hands-on LAB Activities

Preface

  • R Studio Sandbox

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

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

Text Mining

  • Manipulating Text
  • Analyzing the XML Text

Data Analysis – Regression Analysis

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

Data Analysis – Correlation

  • Performing Tetrachoric Correlation

Data Analysis – Clustering

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

Data Visualization – R Graphics

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

Data Visualization – Plotting

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

Data Visualization – 3D

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

Machine Learning in Action

  • Finding a Dataset
  • Making a Prediction

Predicting Events with Machine Learning

  • Using Holt Exponential Smoothing

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