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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
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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
Lesson 1: Preface
  • What this course covers?
  • What you need for this course?
  • Who this course is for?
  • Conventions
Lesson 2: Data Mining Patterns
  • Cluster analysis
  • Anomaly detection
  • Association rules
  • Questions
  • Summary
Lesson 3: Data Mining Sequences
  • Patterns
  • Questions
  • Summary
Lesson 4: Text Mining
  • Packages
  • Questions
  • Summary
Lesson 5: Data Analysis – Regression Analysis
  • Packages
  • Questions
  • Summary
Lesson 6: Data Analysis – Correlation
  • Packages
  • Questions
  • Summary
Lesson 7: Data Analysis – Clustering
  • Packages
  • K-means clustering
  • Questions
  • Summary
Lesson 8: Data Visualization – R Graphics
  • Packages
  • Questions
  • Summary
Lesson 9: Data Visualization – Plotting
  • Packages
  • Scatter plots
  • Bar charts and plots
  • Questions
  • Summary
Lesson 10: Data Visualization – 3D
  • Packages
  • Generating 3D graphics
  • Questions
  • Summary
Lesson 11: Machine Learning in Action
  • Packages
  • Dataset
  • Questions
  • Summary
Lesson 12: Predicting Events with Machine Learning
  • Automatic forecasting packages
  • Questions
  • Summary
Lesson 13: Supervised and Unsupervised Learning
  • Packages
  • Questions
  • Summary

Hands on Activities (Live Labs)

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
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