More Data Mining with R

More Data Mining with R, available at $39.99, has an average rating of 3, with 67 lectures, based on 111 reviews, and has 2598 subscribers.

You will learn about Understand the conceptual foundations of association analysis and perform market basket analyses. Be able to create visualizations of social (and other) networks using the iGraph package. Understand how to examine and mine social network data to understand all of the implicit relationships. Mine text data to create word association visualizations, term documents with word frequency counts and associations, and create word clouds. Learn how to process text and string data, including the use of 'regular expressions'. Extract prototypical information about cycles from time series data. This course is ideal for individuals who are This course would be useful for undergraduate and graduate students wishing to broaden their skills in data mining. or This course would be helpful to analytics professionals who wish to augment their data mining skills toolset. or Anyone who is interested in learning about association analysis (also called 'market basket analysis'), analyzing and mining data from social networks, text (such as Twitter) data, or time series data should take this course. It is particularly useful for This course would be useful for undergraduate and graduate students wishing to broaden their skills in data mining. or This course would be helpful to analytics professionals who wish to augment their data mining skills toolset. or Anyone who is interested in learning about association analysis (also called 'market basket analysis'), analyzing and mining data from social networks, text (such as Twitter) data, or time series data should take this course.

Enroll now: More Data Mining with R

Summary

Title: More Data Mining with R

Price: $39.99

Average Rating: 3

Number of Lectures: 67

Number of Published Lectures: 67

Number of Curriculum Items: 67

Number of Published Curriculum Objects: 67

Original Price: $159.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the conceptual foundations of association analysis and perform market basket analyses.
Be able to create visualizations of social (and other) networks using the iGraph package.
Understand how to examine and mine social network data to understand all of the implicit relationships.
Mine text data to create word association visualizations, term documents with word frequency counts and associations, and create word clouds.
Learn how to process text and string data, including the use of 'regular expressions'.
Extract prototypical information about cycles from time series data.

Who Should Attend

This course would be useful for undergraduate and graduate students wishing to broaden their skills in data mining.
This course would be helpful to analytics professionals who wish to augment their data mining skills toolset.
Anyone who is interested in learning about association analysis (also called 'market basket analysis'), analyzing and mining data from social networks, text (such as Twitter) data, or time series data should take this course.

Target Audiences

This course would be useful for undergraduate and graduate students wishing to broaden their skills in data mining.
This course would be helpful to analytics professionals who wish to augment their data mining skills toolset.
Anyone who is interested in learning about association analysis (also called 'market basket analysis'), analyzing and mining data from social networks, text (such as Twitter) data, or time series data should take this course.

More Data Mining with R presents a comprehensive overview of a myriad of contemporary data mining techniques. More Data Mining with Ris the logical follow-on course to the preceding Udemy course Data Mining with R: Go from Beginner to Advanced although it is not necessary to take these courses in sequential order. Both courses examine and explain a number of data mining methods and techniques, using concrete data mining modeling examples, extended case studies, and real data sets. Whereas the preceding Data Mining with R: Go from Beginner to Advancedcourse focuses on: (1) linear, logistic and local polynomial regression; (2) decision, classification and regression trees (CART); (3) random forests; and (4) cluster analysis techniques, this course, More Data Mining with R presents detailed instruction and plentiful “hands-on” examples about: (1) association analysis (or market basket analysis) and creating, mining and interpreting association rules using several case examples; (2) network analysis, including the versatile iGraph visualization capabilities, as well as social network data mining analysis cases (marriage and power; friendship links); (3) text mining using Twitter data and word clouds; (4) text and string manipulation, including the use of ‘regular expressions’; (5) time series data mining and analysis, including an extended case study forecasting house price indices in Canberra, Australia.

Course Curriculum

Chapter 1: Introduction to R and to Data Mining

Lecture 1: Welcome to More Data Mining with R !

Lecture 2: Course Preliminaries

Lecture 3: Data Input and Output (part 1)

Lecture 4: Data Input and Output (part 2)

Lecture 5: More R Scripting and Visualizations (part 1)

Lecture 6: More R Scripting and Visualizations (part 2)

Lecture 7: More Input and Output (part 1)

Lecture 8: More Input and Output (part 2)

Lecture 9: Homework Exercise: Execute Second Set of Scripts on your Own

Chapter 2: Association Analysis (part 1)

Lecture 1: Introduction to Association Analysis (part 1)

Lecture 2: Introduction to Association Analysis (part 2)

Lecture 3: Preparing the Titanic Dataset

Lecture 4: Rule Mining with Titanic Dataset (part 1)

Lecture 5: Rule Mining with Titanic Dataset (part 2)

Lecture 6: Interpreting Rules

Lecture 7: Visualizing Association Rules (part 1)

Lecture 8: Visualizing Association Rules (part 2)

Chapter 3: Association Analysis: Online Radio and Predicting Income

Lecture 1: Association Rules and Lift Reviewed

Lecture 2: Association Rules Reviewed (part 2)

Lecture 3: Online Radio Predictor Example (part 1)

Lecture 4: Online Radio Predictor Example (part 2)

Lecture 5: Predicting Income Example (part 1)

Lecture 6: Predicting Income Example (part 2)

Lecture 7: Predicting Income Example (part 3)

Chapter 4: Social Network Analysis: iGraph Visualizations

Lecture 1: Introduction to iGraph

Lecture 2: iGraph Visualization Examples (part 1)

Lecture 3: iGraph Visualization Examples (part 2)

Lecture 4: iGraph Measurement Examples (part 3)

Lecture 5: iGraph Measurement Examples (part 4)

Lecture 6: iGraph Visualization Examples (part 5)

Lecture 7: iGraph Visualization Examples (part 6)

Lecture 8: iGraph Visualization Examples (part 7)

Chapter 5: Social Network Analysis (part 2)

Lecture 1: Visual Network Basics Revisited

Lecture 2: Visual Network: Marriage and Power in 15th Century Florence (part 1)

Lecture 3: Visual Network: Marriage and Power in 15th Century Florence (part 2)

Lecture 4: Example: Friendship Network (part 1)

Lecture 5: Example: Friendship Network (part 2)

Lecture 6: Example: Friendship Network (part 3)

Chapter 6: Text Mining Twitter Data

Lecture 1: Preprocessing Twitter Data

Lecture 2: Transforming Twitter Data

Lecture 3: Stemming and Frequency Counts

Lecture 4: Building a Text Term Document

Lecture 5: Frequent Terms and Associations

Lecture 6: Word Cloud and Word Clustering

Lecture 7: K-Means and K-Medoids Clustering

Lecture 8: Using Lists for Text Processing (part 1)

Lecture 9: Using Lists for Text Processing (part 2)

Lecture 10: Using Lists for Text Processing (part 3)

Chapter 7: Text (String) Manipulation

Lecture 1: Introduction to String Manipulation (slides, part 1)

Lecture 2: Introduction to String Manipulation (slides, part 2)

Lecture 3: Text and String Manipulation Script Demos (part 1)

Lecture 4: Text and String Manipulation Demos (part 2)

Lecture 5: Text and String Manipulation Demos (part 3)

Lecture 6: Text and String Manipulation Demos (part 4)

Lecture 7: Regular Expression Basics (slides and script)

Lecture 8: More Advanced Regular Expression Capabilities (slides and script)

Chapter 8: Time Series Data Mining

Lecture 1: Maine Unemployment Data (part 1)

Lecture 2: Maine Unemployment Data (part 2)

Lecture 3: Airline Travel Example

Lecture 4: Electric Consumption in Australia (part 1)

Lecture 5: Electric Consumption in Australia (part 2)

Lecture 6: Time Series Clustering (part 1)

Lecture 7: Time Series Clustering (part 2)

Lecture 8: Time Series Classification

Chapter 9: Case Study: Forecasting House Price Indices in Canberra, Australia

Lecture 1: Forecasting House Prices: Exploring the Data (part 1)

Lecture 2: Forecasting House Prices: Exploring the Data (part 2)

Lecture 3: Forecast House Prices: Use Trend and Seasonal Components

Instructors

Geoffrey Hubona, Ph.D.
Associate Professor of MIS and Data Analytics

Rating Distribution

1 stars: 7 votes
2 stars: 11 votes
3 stars: 29 votes
4 stars: 24 votes
5 stars: 40 votes

Frequently Asked Questions

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