Business Analytics with R

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

What is Business Analyticsedforce2021-06-29T16:20:39+05:30

What is Business Analytics

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

About Redforce2021-06-29T16:24:05+05:30

About R

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()

Data Manipulation in Redforce2021-06-29T16:24:11+05:30

Data Manipulation in R

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

Data Import techniquesedforce2021-06-29T16:24:17+05:30

Data Import techniques

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

Exploratory Data analysisedforce2021-06-29T16:24:23+05:30

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
Segment plot HC plot in R

Data Visualization in Redforce2021-06-29T16:24:28+05:30

Data Visualization 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

Data Mining: Clustering Techniquesedforce2021-06-29T16:24:34+05:30

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 Analysisedforce2021-06-29T16:24:38+05:30

Data Mining: Association Rule Mining and Sentiment Analysis

Association Rule Mining
Sentiment Analysis

Linear and Logistic Regressionedforce2021-06-29T16:24:46+05:30

Linear and Logistic Regression

Linear Regression
Logistic Regression

Anova, Predictive Analysisedforce2021-06-29T16:24:55+05:30

Anova, Predictive Analysis

More on Data Miningedforce2021-06-29T16:25:13+05:30

More on Data Mining

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

2023-01-06T15:22:33+05:30

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Business Analytics with R

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