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Big Data Analytics (Programming with R)

  • Duration 3h 60m
  • Total Enrolled 0
  • Last Update December 18, 2020

Description

Module 1 – Introduction to Business Analytics
• Introduction to Business Analytics & its Features
• Types of Business Analytics
• Business Analytics Case Studies
• Business Decisions
• Business Intelligence
• Data Science and its importance
Module 2 – Introduction to R
• Introduction to R
• Understanding R
• Using R to illustrate the basic concepts
• Installing R and RStudio
• Integrated Development Environments (IDEs) for R
• Using R Console
• Scripting in R
• R Workplace and Packages
• Distributed R
o Introduction
o Installation
o Programming Concepts
Module 3 – R Programming
• Introduction
• Operators in R (Arithmetic, Relational, Logical, Assignment)
• Basic and Advance Data Types
• Loops and Conditional Statement in R
• Commands to Run an R Script and a Batch Script
• Functions in R
• String Manipulation in R
• Dplyr Package – An Overview
• Installing Dplyr
• Functions of the Dpylr package
Module 4 – R Data Structure
• Types of Data Structures in R
• Vectors
• Scalars
• Matrices
• Arrays
• Data Frames
• Factors
• Lists
• Elements of the Different Data Structures in R
• Acceptable Formats to Import and Export Data in R
Module 5 – Data Visualization
• Graphics in R
• Types of Graphics
• Basic elements of graph
• Methods to Save Graphics as Files
• Procedure to Export Graphs in RStudio
Module 6 – R Connection with Database
• Introduction to RDBMS
• Introduction to MySql
• R packages to connect to database
• Data analysis of data from database
Module 7 – Debugging in R
• Introduction to Debugging
• Important Function to Debug
Module 8 – Statistics in R
• Introduction to Statistics
• Types of Data
• Qualitative vs Quantitative Analysis
• Hypothesis Testing in R
• Need of Hypothesis Testing in Businesses
• Test of mean
• Test of variance
• Chi-square Test
• Non-parametric Test
• Linear Regression
• Basics of Classification
• Basics of Clustering

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