R for Data Science
(DSR.AJ1)
/ ISBN: 9781644593103
R for Data Science
Get handson experience of R for Data Science with the comprehensive course and lab. The lab provides handson 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
LiveLab

38+ LiveLab

37+ Video tutorials

01:59+ Hours
 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
 Kmeans 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 kmeans Clustering
 Calculating Kmedoids Clustering
 Displaying the Hierarchical Cluster
 Plotting Graphs By Performing ExpectationMaximization
 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 InstanceBased Learning
 Performing Cluster Analysis
 Constructing a Multitude of Decision Trees
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