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Analytics Using R

INTRODUCTION TO DATA ANALYTICS

  • Understand Business Analytics and R
  • Knowledge on the R language
  • community and ecosystem
  • Understand the use of ‘R’ in the industry
  • Compare R with other software in analytics
  • Install R and the packages useful for the course
  • Perform basic operations in R using command line
  • Learn the use of IDE R Studio and Various GUI
  • Use the ‘R help’ feature in R
  • Knowledge about the worldwide R community collaboration

INTRODUCTION TO R PROGRAMMING

  • The various kinds of data types in R and its appropriate uses
  • The built-in functions in R like: seq(), cbind (), rbind(), merge()
  • Knowledge on the various Subsetting methods
  • Summarize data by using functions like: str(), class(), length(), nrow(), ncol()
  • Use of functions like head(), tail(), for inspecting data
  • Indulge in a class activity to summarize data

DATA MANIPULATION IN R

  • The various steps involved in Data Cleaning
  • Functions used in Data Inspection
  • Tackling the problems faced during Data Cleaning
  • Uses of the functions like grepl(), grep(), sub()
  • Coerce the data
  • Uses of the apply() functions

DATA IMPORT TECHNIQUES IN R

  • Import data from spreadsheets and text files into R
  • Import data from other statistical formats like sas7bdat and spss
  • Packages installation used for database import
  • Connect to RDBMS from R using ODBC and basic SQL queries in R
  • Basics of Web Scraping

EXPLORATORY DATA ANALYSIS

  • Understanding the Exploratory Data Analysis(EDA)
  • Implementation of EDA on various datasets
  • Boxplots
  • Understanding the cor() in R
  • EDA functions like summarize(), llist()
  • Multiple packages in R for data analysis
  • The Fancy plots like Segment plot
  • HC plot in R

DATA VISUALIZATION IN R

  • Understanding on Data Visualization
  • Graphical functions present in R
  • Plot various graphs like tableplot
  • Nhistogram
  • boxplot
  • Customizing Graphical Parameters to improvise the plots
  • Understanding GUIs like Deducer and R Commander
  • Introduction to Spatial Analysis

DATA MINING: CLUSTERING TECHNIQUES

  • Introduction to Data Mining
  • Understanding Machine Learning
  • Supervised and Unsupervised Machine Learning Algorithms
  • K-means Clustering

DATA MINING: ASSOCIATION RULE MINING AND SENTIMENT ANALYSIS

  • Association Rule Mining
  • Sentiment Analysis

LINEAR AND LOGISTIC REGRESSION

  • Linear Regression
  • Logistic Regression

ANOVA AND PREDICTIVE ANALYSIS

  • Anova
  • Predictive Analysis

DATA MINING: DECISION TREES AND RANDOM FOREST

  • Decision Trees
  • Algorithm for creating Decision Trees
  • Greedy Approach: Entropy and Information Gain
  • Creating a Perfect Decision Tree
  • Classification Rules for Decision Trees
  • Concepts of Random Forest
  • Working of Random Forest
  • Features of Random Forest

PREPARING REPORTS REPORTS

  • Creating an application using Shiny
  • Accessing R objects through Shiny interface