Data Science with R- Programming – 5 Days

                     Data Science with R- Programming – 5 Days

Pre-Requisites:

• Understanding of Data Type, scales of measurement
• Measures of Summary: Mean, mode median, standard deviation, variance, covariance, skewness and kurtosis and its significance
• Describing Data: bar, pie, box and whiskers plot, scatter plot
• Probability
• Discrete Probability Distribution: Binomial distribution
• Continuous Probability Distribution: Normal distribution
• Binomial to Normal
• Normal probability plot
• Various Sampling methods
• Sampling Distribution
• Estimation
• Test of hypothesis
• Correlation
• Regression

(It Is mandatory to accomplish the training prerequisite conditions before nominating for the session)                    

Day 1           

                     Overview of Analytics

                     ·         What is Analytics?

                     ·         Types of Business Analytics

                     o    Descriptive Analytics

                     o    Predictive Analytics

                     o    Prescriptive Analytics

                     ·         Application Of Business Analytics

                     o    Used Cases from Banking

                     ·         Popular Tools

                     ·         Role of Data Scientist

                     ·         Analytics Methodology

                     ·         Problem Definition

                     Introduction to R

                     ·         Introduction to R

                     ·         R Set-up

                     ·         Advantages of R

                     ·         IDEs for R

                     ·         Setting the Workspace

                     ·         Benefits of Workspace

                     ·         Packages in R

                     Programming in R

                     ·         R Syntax and Objects

                     ·         Arithmetic Operators

                     ·         Relational Operators

                     ·         Logical Operators

                     ·         Assignment Operators

                     ·         Conditional Statements in R

                     ·         Ifelse() Function

                     ·         Loops in R

                     ·         Break Statement

                     ·         Next Statement

                     ·         Scan Function

                     ·         Running an R Script

                     ·         Running a Batch Script

                     ·         R Functions

                     Data Structure & Apply Functions in R

                     ·         Objectives

                     ·         Types of Data Structures in R

                     ·         Vectors

                     ·         Scalers

                     ·         Colon Operator

                     ·         Accessing Vector Elements

                     ·         Matrices

                     ·         Accessing Matrix Elements

                     ·         Arrays

                     ·         Accessing Array Elements

                     ·         Data Frames

                     ·         Elements of Data Frames

                     ·         Factors

                     ·         Lists

                     ·         Importing Files in R

                     ·         Importing an Excel File

                     ·         Importing a Minitab File

                     ·         Importing a Table File

                     ·         Importing a CSV File

                     ·         Exporting Files from R

                     ·         Types of Apply Functions

                     o    Apply() Function

                     o    Apply() Function (contd.)

                     o    Apply() Function (contd.)

                     o    Lapply() Function

                     o    Sapply() Function

                     o    Tapply() Function

                     o    Tapply() Function (contd.)

                     o    Tapply() Function (contd.)

                     o    Vapply() Function

                     o    Mapply() Function

                     ·         Basic Data Manipulation

                     o    In built function for data manipulation

                     o    Sub-setting and slicing data

                     o    Modifying structure of the data

                     ·         Advanced Data Manipulation

                     o    Dplyr Package—An Overview

                     o    Dplyr Package—The Five Verbs

                     o    Installing the Dplyr Package

                     o    Functions of the Dplyr Package

                     o    Functions of the Dplyr Package — Select()

                     o    Functions of Dplyr Package—Filter()

                     o    Functions of Dplyr Package—Arrange()

                     o    Functions of Dplyr Package—Mutate()

Day 2           

                     o    Reshape package for data structure manipulations

                     ·         Function in R

                     Data Visualization in R

                     ·         Graphics in R

                     ·         Types of Graphics

                     ·         Bar Charts

                     ·         Creating Simple Bar Charts

                     ·         Pie Charts

                     ·         Histograms

                     ·         Kernel Density Plots

                     ·         Line Charts

                     ·         Box Plots

                     ·         Heat Maps

                     ·         Saving a Graphic Output as a File

                     ·         Exporting Graphs in RStudio

                     ·         Exporting Graphs as PDFs in RStudio

                     ·         Advance charting with GGPLOT

                     R Inbuilt Functions & Loops

                     Mathematical Functions: sum, mean, table, colsums etc

                     Aggregate Function

                     Head & Summary Function

                     Basic functions like

                     grep

                     gsub

                     Paste

                     substr

                     replace

                     strsplit

                     Merging data sets

                     Date functions and formats

                     o Format Function

                     o Date Function

                     o Extracting Month & Year

                     Loops

                     o For

                     o While

                     Conditional Statements

                     R User Defined Function

                     Writing our own function

                     User defined function with APPLY family

                     Connecting R to MySQL

                     R package for MySQL connection

                     DPLYR Package

                     GGPLOT Package

                     Case Study

                    

Day 3           

                     Introduction to Machine Learning

                     ·         Supervised Learning

                     ·         Unsupervised Learning

                     Application Area

                     Basic Ststistics

                     Types of Data

                     Summarization Techniques

                     Probability

                     Different types of Probability Distribution

                     Quiz

                     Introduction to Regression Analysis

                     Use of Regression Analysis—Examples

                     Use of Regression Analysis—Examples (contd.)

                     Types Regression Analysis

                     Simple Regression Analysis

                     Multiple Regression Models

                     Simple Linear Regression Model

                     Simple Linear Regression Model Explained

                      

                     Correlation

                     Correlation Between X and Y

                     Correlation Between X and Y (contd.)

                     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

                     Non-Linear Regression Models (contd.)

                     Non-Linear Regression Models (contd.)

                     Non-Linear Models to Linear Models

                     Algorithms for Complex Non-Linear Models

                     Summary and quizzes

                     Recap

                     Introduction to Classification

                     Examples of Classification

                     Classification vs. Prediction

                     Classification System

                     Classification Process

                     Classification Process—Model Construction

                     Classification Process—Model Usage in Prediction

                     Issues Regarding Classification and Prediction

                     Data Preparation Issues

                     Evaluating Classification Methods Issues

                     Decision Tree

                     Decision Tree—Dataset

                     Decision Tree—Dataset (contd.)

                     Classification Rules of Trees

                     Overfitting in Classification

Day 4           

                     Tips to Find the Final Tree Size

                     Basic Algorithm for a Decision Tree

                     Statistical Measure—Information Gain 

                     Calculating Information Gain—Example 

                     Calculating Information Gain—Example (contd.) 

                     Calculating Information Gain for Continuous-Value Attributes

                     Enhancing a Basic Tree

                     Decision Trees in Data Mining

                     Case Study

                    

                     Nearest Neighbor Classifiers

                     Nearest Neighbor Classifiers (contd.)

                     Nearest Neighbor Classifiers (contd.)

                     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

                      

                     Summary and quizzes

                     Recap

                     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

                     K-means Clustering—Case Study

                     K-means Clustering—Case Study(contd.)

                     K-means Clustering—Case Study (contd.)

Day 5           

                     Hierarchical Clustering

                     Hierarchical Clustering Algorithms

                     Requirements of Hierarchical Clustering Algorithms

                     Agglomerative Clustering Process

                     Hierarchical Clustering—Case Study

                     Hierarchical Clustering—Case Study (contd.)

                     Hierarchical Clustering—Case Study (contd.)

                     Hierarchical Clustering—Case Study (contd.)

                      

                     Summary and quizzes

                     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

                     Apriori Algorithm—Example

                     Applying Apriori Algorithm

                     Step 1—Mine All Frequent Item Sets

                     Algorithm to Find Frequent Item Set

                     Finding Frequent Item Set—Example

                     Ordering Items

                     Ordering Items (contd.)

                     Candidate Generation

                     Candidate Generation (contd.)

                     Candidate Generation—Example

                     Step 2—Generate Rules from Frequent Item Sets

                     Generate Rules from Frequent Item Sets—Example

                      

                      

                     Problems with Association Mining

                     Summary and quizzes

                      

                     Factor Analysis

                     a. Definition and examples

                     b. Factor Analysis

                     c. Communality

                     d. Rotation Of Factors

                     e. Implementation

                     f. Evaluation