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Business Analytics with R

Live Online (VILT) & Classroom Corporate Training Course

The open source programming language R has increased in popularity in recent years, and is now universally accepted by statisticians and data miners as the number one language for data science.

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Overview

This training will take you through the basics of this powerful language R. From the ground up, you will learn how to develop data for analysis and apply statistical measures to create data visualisations. By exploring the characteristics of data sets, you can analyse and achieve optimum results based on past data.

Objectives

At the end of Business Analytics with R training course, participants will

  • Learn to explore and visualize data and polish your skills in techniques such as Predictive Analytics, Association Rule Mining and much more
  • Derive meaning from custom created charts that are used to represent complex data, manipulate this data and create statistical models for predictive analysis
  • Learn to use R, not just as a statistical tool but to create your own functions, objects and packages

Prerequisites

Basic knowledge of a programming language such as Python or Java. A background in Mathematics will be beneficial

Course Outline

  • R tools and their uses in Business Analytics
  • Objectives
  • Analytics
  • Where is analytics applied?
  • Responsibilities of a data scientist
  • Problem definition
  • Summarizing data
  • Data collection

  • Difference between R and other analytical languages
  • Different data types in R
  • Built in functions of R: seq(), cbind (), rbind(), merge()
  • Subsetting methods
  • Use of functions like str(), class(), length(), nrow(), ncol(),head(), tail()

  • Steps involved in data cleaning
  • Problems and solutions for Data cleaning
  • Data inspection
  • Use of functions grepl(), grep(), sub()
  • Use of apply() function
  • Coerce the data

  • How R handles data in a variety of formats
  • Importing data from csv files, spreadsheets and text files
  • Import data from other statistical formats like sas7bdat and sps
  • Packages installation used for database import
  • Connect to RDBMS from R using ODBC and basic SQL queries in R
  • Basics of Web Scraping

  • 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
  • Segment plot HC plot in R

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

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

  • Association Rule Mining
  • Sentiment Analysis

  • Linear Regression
  • Logistic Regression

    • 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

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