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More Data Mining with R

How to perform market basket analysis, analyze social networks, mine Twitter data, text, and time series data.
(110 reviews)
2,555 students
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Platform: Udemy
Video: 10h 34m
Language: English
Next start: On Demand

Table of contents


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.

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.


• Students will need to install the no-cost R console software and the no-cost RStudio IDE suite (instructions are provided).

This course is for

• 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.
Associate Professor of Information Systems
Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). In previous academic faculty positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master’s and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling.
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Platform: Udemy
Video: 10h 34m
Language: English
Next start: On Demand

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