R and Data Mining Course

This is a short course on data mining with R. It consists of 9 sessions below. Each session will be of 1.5 hours, incl. a 1-hour tutorial and a 30m exercise.

Course Outline

Part 1 – R Programming, Data Transformation, Data Visualisation, Classification and Clustering

• • R Programming

  • basics of R language and programming, parallel computing, and data import and export

  • Data Exploration and Visualisation

  • summary, stats and various charts with Base R

  • Data Transformation and Visualisation with Tidyverse

  • Data transformation with dplyr and tidyr and data visualisation with ggplot2

  • Regression and Classification

  • linear regression and logistic regression, decision trees and random forest

  • Data Clustering

  • k-means clustering, k-medoids clustering, hierarchical clustering and density-based clustering

Part 2 – Time Series Analysis, Network Analysis, Association Rules and Text Mining

• • Time Series Analysis

  • time series decomposition, forecasting, classification and clustering

  • Network Analysis and Graph Mining

  • graph construction, graph query, centrality measures, and graph visualisation

  • Association Rule Mining and Sequence Mining

  • mining and selecting interesting association rules, redundancy removal, and rule visualisation, sequential pattern mining

  • Text Mining

  • text mining, word cloud, topic modelling, and sentiment analysis

  • Big Data (optional)

  • Hadoop, Spark and R

Prerequisites

• Knowledge and experience of R, or similar programming languages

  • Basic knowledge of data mining and machine learning, such as

    • Regression

    • Classification

    • Data clustering

    • Association rules

    • Text mining

    • Time series analysis

    • Social network analysis

Software and Course Materials

You will need to bring your own laptop, if computers are not provided in the classroom. Please install the required software and R packages and download the datasets, slides and scripts below before coming to the course. Note that the slides are subject to change and therefore, please download its latest version when getting close to the course days.

• Software

  • • R

    • http://www.r-project.org/

    • RStudio (desktop edition)

    • http://www.rstudio.com/products/rstudio/download/

• • Course Materials

    • A ZIP archive [RDM-course.zip], containing all datasets, slides and scripts for this course.

• R Packages

• • Install the required R packages by running the R script provided in file “Install-R-packages.R” in folder “code” in above ZIP archive

Contact

If you have any questions or feedback, please do not hesitate to contact me on yanchang <at> RDataMining.com. Thanks.