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Data Mining with R
Categories:
IT
Description

ABOUT THIS COURSE

This course introduces learners to data mining using R, a free software environment for statistical computing and graphics. It provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Learn to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequences analysis, text mining, social network analysis, sentiment analysis, and more.

Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. The course focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.

Rated Top 3 in Analytics programs in the country, Praxis Business Schools has designed the program through its Industry veterans in the Analytics domain. 

LEARNING OBJECTIVES

  • Learn to use R to import and manipulate data
  • Understand clustering and data classification.
  • Learn model building through specific case studies in different industry situations

KEY TOPICS COVERED

  • Introduction to R, R Studio and Basic R Operations
  • Import, Export and Data Manipulation with R
  • Concept of the data structure in R
  • Concept of Index in R
  • Creating Boolean index in R based on conditions
  • Hands on exercise
  • Understanding Loops in R
  • Building User functions in R
  • Basic R Graphics
  • Understanding data clustering concepts and techniques
  • Running clustering in R with Silhouette distance measure for cluster validity
  • Understanding data classification concepts and techniques using decision trees
  • Model building concepts and techniques
  • Concepts of Association Rule Mining
  • Building association rules and interpretation


PRE-REQUISITES TO REGISTER FOR THE COURSE: 

  • R programming and  Statistics 

Faculty Profiles
Frequently Asked Questions