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 learn
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
Lessons 3: Data Mining Sequence
- Patterns
- Questions
Lessons 4: Text Mining
- Packages
- Questions
Lessons 5: Data Analysis – Regression Analysis
- Packages
- Questions
Lessons 6: Data Analysis – Correlation
- Packages
- Questions
Lessons 7: Data Analysis – Clustering
- Packages
- K-means clustering
- Questions
Lessons 8: Data Visualisation – R Graphics
- Packages
- Questions
Lessons 9: Data Visualisation – Plotting
- Packages
- Scatter plots
- Bar charts and plots
- Questions
Lessons 10: Data Visualisation – 3D
- Packages
- Generating 3D graphics
- Questions
Lessons 11: Machine Learning in Action
- Packages
- Dataset
- Questions
Lessons 12: Predicting Events with Machine Learning
- Automatic forecasting packages
- Questions
Lessons 13: Supervised and Unsupervised Learning
- Packages
- Questions
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-Maximisation
- 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 Visualising Sequences
- Computing LCP, LCS, and OMD
Text Mining
- Manipulating Text
- Analysing 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 Visualisation – R Graphics
- Grouping and Organising Bivariate Data
- Plotting Points on a Map
Data Visualisation – Plotting
- Displaying a Histogram of Scatter Plots
- Creating an Enhanced Scatter Plot
- Constructing a Bar Plot
- Producing a Word Cloud
Data Visualisation – 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
Exam FAQs
Summary
Standard:
R for Data Science
Lessons:
24 Classes
Delivery Method:
Online
Language:
English