Data Science Using SAS & R

Introduction to Business Analytics

Introduction

Objectives

Need of Business Analytics

Business Decisions

Introduction to Business Analytics

Features ofBusiness Analytics

Types of Business Analytics

Descriptive Analytics

Predictive Analytics

Prescriptive Analytics

Supply Chain Analytics

Health Care Analytics

Marketing Analytics

Human Resource Analytics

Web Analytics

Business Decisions

Business Intelligence (BI)

Data Science

Importance of Data Science

Data Science as a Strategic Asset

Big Data

Analytical Tools

Introduction to R

Introduction

Objectives

An Introduction to R

Comprehensive R Archive Network (CRAN)

Cons of R

Companies Using R

Understanding R

Installing R on Various Operating Systems

Installing R on Windows from CRAN Website

Install R

Introduction to R

Introduction

Objectives

An Introduction to R

Comprehensive R Archive Network (CRAN)

Cons of R

Companies Using R

Understanding R

Installing R on Various Operating Systems

Installing R on Windows from CRAN Website

Install R

R Programming

Introduction

Objectives

Operators in R

Arithmetic Operators

Use Arithmetic Operations

Relational Operators

Use Relational Operators

Logical Operators

Use Logical Operators

Assignment Operators

Use Assignment Operator

Conditional Statements in R

If else() Function

Use Conditional Statements

Use Switch Function

Loops in R

Break Statement

Next Statement

Use Loops

Scan() Function

Running an R Script

Running a Batch Script

R Functions

Use Commonly Used Functions

Use Commonly-USed String Functions

R Data Structure

Introduction

Objectives

Types of Data Structures in R

Vectors

Create a Vector

Scalars

Colon Operator

Accessing Vector Elements

Matrices

Accessing Matrix Elements

Create a Matrix

Arrays

Accessing Array Elements

Create an Array

Data Frames

Elements of Data Frames

Create a Data Frame

Factors

Create a Factor

Lists

Create a List

Importing Files in R

Importing an Excel File

Importing a Minitab File

Importing a Table File

Importing a CSV File

Read Data from a File

Exporting Files fromR

Apply Functions

Introduction

Objectives

Types of Apply Functions

Apply() Function

Use Apply Function

Lapply() Function

Use Lapply Function

Sapply() Function

Use Sapply Function

Tapply() Function

Use Tapply Function

Vapply() Function

Use Vapply Function

Mapply() Function

Dplyr Package-An Overview

Dplyr Package-The Five Verbs

Installing the Dplyr Package

Functions of the Dplyr Package

Functions of the Dplyr Package-Select()

Use the Select Function

Functions of Dplyr-Package-Filter()

Use Select Function

Functions of Dplyr Package-Arrange()

Use Arrange Function

Functions of Dplyr Package-Mutate()

Functions of Dply Package-Summarise()

Use Summarise Function

Data Visualization

Introduction

Objectives

Graphics in R

Types of Graphics

Bar Charts

Creating Simple Bar Charts

Editing a Simple Bar Chart

Create a Bar Chart

Create a Stacked Bar Plot and Grouped Bar Plot

Pie Charts

Editing a Pie Chart

Create a Pie Chart

Histograms

Creating a Histogram

Kernel Density Plots

Creating a Kernel Density Plot

Create Histograms and a Density Plot

Line Charts

Creating a Line Chart

Box Plots

Creating a Box Plot

Create Line Graphs and a Box Plot

Heat Maps

Creating a Heat Map

Create a Heatmap

Word Clouds

Creating a Word Cloud

Create a Word Cloud

File Formats for Graphic Outputs

Saving a Graphic Output as a File

Save Graphics to a File

Exporting Graphs in RStudio

Exporting Graphs as PDFs in RStudio

Save Graphics Using RStudio

Introduction to Statistics

Introduction

Objectives

Basics of Statistics

Types of Data

Qualitative vs. Quantitative Analysis

Types of Measurements in Order

Nominal Measurement

Ordinal Measurement

Interval Measurement

Ratio Measurement

Statistical Investigation

Statistical Investigation Steps

Normal Distribution

Example of Normal Distribution

Importance of Normal Distribution in Statistics

Use of the Symmetry Property of Normal Distribution

Standard Normal Distribution

Use Probability Distribution Functions

Distance Measures

Distance Measures-A Comparison

Euclidean Distance

Example of Euclidean Distance

Manhattan Distance

Minkowski Distance

Mahalanobis Distance

Cosine Similarity

Correlation

Correlation Measures Explained

Pearson Product Moment Correlation (PPMC)

Dist() Function in R

Perform the Distance Matrix Computations

Hypothesis Testing

Introduction

Objectives

Hypothesis

Need of Hypothesis Testing in Businesses

Null Hypothesis

Alternate Hypothesis

Null vs. Alternate Hypothesis

Chances of Errors in Sampling

Types of Errors

Contingency Table

Decision Making

Critical Region

Level of Significance

Confidence Coefficient

Bita Risk

Power of Test

Factors Affecting the Power of Test

Types of Statistical Hypothesis Tests

An Example of Statistical Hypothesis Tests

Upper Tail Test

Test Statistic

Factors Affecting Test Statistic

Critical Value Using Normal ProbabilityTable

Hypothesis Testing II

Introduction

Objectives

Parametric Tests

Z-Test

T-Test

Use Normal and Student Probability Distribution Functions

Testing Null Hypothesis

Objectives of Null Hypothesis Test

Three Types of Hypothesis Tests

Hypothesis Tests About Population Means

Decision Rules

Hypothesis Tests About Population Proportions

Chi-Square Test

Steps ofChi-Square Test

Degree of Freedom

Chi-Square Test for Independence

Chi-Square Test for Goodness of Fit

Use Chi-Squared Test Statistics

Introduction to ANOVA Test

One-Way ANOVA Test

The F-Distribution and F-Ratio

F-Ratio Test

Perform ANOVA

Regression Analysis

Introduction

Objectives

Introduction to Regression Analysis

Types Regression Analysis

Simple Regression Analysis

Multiple Regression Models

Simple Linear Regression Model

Simple Linear Regression Model Explained

Perform SimpleLinear Regression

Correlation

Correlation Between X and Y

Find Correlation

Method of Least Squares Regression Model

Coefficient of Multiple Determination Regression Model

Standard Error of the Estimate Regression Model

Dummy Variable Regression Model

Interaction Regression Model

Non-Linear Regression

Non-Linear Regression Models

Perform Regression Analysis with Multiple Variables

Non-Linear Models to Linear Models

Algorithms for Complex Non-Linear Models

Classification

Introduction

Objectives

Introduction to Classification

Examples of Classification

Classification vs. Prediction

Classification System

Classification Process

Classification Process-Model Construction

Classification Process-Model Usage inPrediction

Issues Regarding Classification and Prediction

Data Preparation Issues

Evaluating Classification Methods Issues

Decision Tree

Decision Tree-Dataset

Classification Rules of Trees

Overfitting in Classification

Tips to Find the Final Tree Size

Basic Algorithm for a Decision Tree

Statistical Measure-Information Gain

Calculating Information Gain for Continuous-Value Attributes

Enhancing a Basic Tree

Decision Trees in Data Mining

Model a Decision Tree

NaiveBayes Classifier Model

Features of Naive Bayes Classifier Model

Bayesian Theorem

Naive Bayes Classifier

Applying Naive Bayes Classifier-Example

Naive Bayes Classifier-Advantages and Disadvantages

Perform Classification Using the Naive Bayes Method

Nearest Neighbor Classifiers

Computing Distance and Determining Class

Choosing the Value of K

Scaling Issues in Nearest Neighbor Classification

Support Vector Machines

Advantages of Support Vector Machines

Geometric Margin in SVMs

Linear SVMs

Non-Linear SVMs

Support a Vector Machine

Clustering

Introduction

Objectives

Introduction to Clustering

Clustering vs. Classification

Use Cases of Clustering

Clustering Models

K-means Clustering

K-means Clustering Algorithm

Pseudo Code of K-means

K-means Clustering Using R

Perform Clustering Using Kmeans

Hierarchical Clustering

Hierarchical Clustering Algorithms

Requirements of Hierarchical Clustering Algorithms

Agglomerative Clustering Process

Perform Hierarchical Clustering

DBSCAN Clustering

Concepts of DBSCAN

DBSCAN Clustering Algorithm

DBSCAN in R

Association

Introduction

Objectives

Association Rule Mining

Application Areas of Association Rule Mining

Parameters of Interesting Relationships

Association Rules

Association Rule Strength Measures

Limitations of Support and Confidence

Apriori Algorithm

Applying Aprior Algorithm

Step 1-Mine All Frequent Item Sets

Algorithm to Find Frequent Item Set

Ordering Items

Candidate Generation

Step 2-Generate Rules from Frequent Item Sets

Perform Association Using the Apriori Algorithm

Perform Visualization on Associated Rules

Problems with Association Mining

Basic Analytic Techniques-Using SAS and Excel

Basic Analytic Techniques-Using SAS

Data Exploration

Data Visualization

Diagnostic Analytics

Predictive Modeling Techniques-Using SAS and Excel

Predictive Modelling Techniques

Linear Regression

Logistic Regression

Cluster Analysis

Time SeriesAnalysis