# R for Data Science

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

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

Need guidance and support? __Click here to check our Instructor Led Course__.

# 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