More Data Mining with R

Updated on

Course overview

Course type
Paid course
All Levels
11 hours
67 lessons
Available on completion
Course author
Geoffrey Hubona, Ph.D.
  • 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.


How to perform market basket analysis, analyze social networks, mine Twitter data, text, and time series data.

More Data Mining with R presents a comprehensive overview of a myriad of contemporary data mining techniques. More Data Mining with R is 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 Advanced course 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.

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