R for Data Science

(DS-R.AJ1) / ISBN: 9781644593103
This course includes
Lessons
TestPrep
LiveLab
DS-R.AJ1_pro.png?1639452267 DS-R.AJ1_pro.png?1639452267
Share
$279.99
Try This Course
Are you an instructor?
Request a free evaluation copy

R for Data Science

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.
Here's what you will get

Lessons
  • 13+ Lessons
  • 110+ Exercises
  • 76+ Quizzes
  • 113+ Flashcards
  • 113+ Glossary of terms
TestPrep
  • 45+ Pre Assessment Questions
  • 45+ Post Assessment Questions
LiveLab
  • 38+ LiveLab
  • 37+ Video tutorials
  • 01:59+ Hours
Here's what you will learn
Download Course Outline
  • What this course covers?
  • What you need for this course?
  • Who this course is for?
  • Conventions
  • Cluster analysis
  • Anomaly detection
  • Association rules
  • Questions
  • Summary
  • Patterns
  • Questions
  • Summary
  • Packages
  • Questions
  • Summary
  • Packages
  • Questions
  • Summary
  • Packages
  • Questions
  • Summary
  • Packages
  • K-means clustering
  • Questions
  • Summary
  • Packages
  • Questions
  • Summary
  • Packages
  • Scatter plots
  • Bar charts and plots
  • Questions
  • Summary
  • Packages
  • Generating 3D graphics
  • Questions
  • Summary
  • Packages
  • Dataset
  • Questions
  • Summary
  • Automatic forecasting packages
  • Questions
  • Summary
  • Packages
  • Questions
  • Summary

Hands on Activities (Live Labs)

  • R Studio Sandbox
  • 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
  • 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
  • Manipulating Text
  • Analyzing the XML Text
  • Performing Simple Regression
  • Performing Multiple Regression
  • Performing Multivariate Regression Analysis
  • Performing Tetrachoric Correlation
  • Estimating the Number of Clusters Using Medoids
  • Performing Affinity Propagation Clustering
  • Grouping and Organizaing Bivariate Data
  • Plotting Points on a Map
  • Displaying a Histogram of Scatter Plots
  • Creating an Enhanced Scatter Plot
  • Constructing a Bar Plot
  • Producing a Word Cloud
  • Generating a 3D Graphic
  • Producing a 3D Scatterplot
  • Finding a Dataset
  • Making a Prediction
  • Using Holt Exponential Smoothing
  • Developing a Decision Tree
  • Producing a Regression Model
  • Understanding Instance-Based Learning
  • Performing Cluster Analysis
  • Constructing a Multitude of Decision Trees
×