Data Science with R Training

• Explore R data structures and syntaxes
• Read and write data from a local file to a cloud-hosted database
• Work with data, get summaries, and transform them to fit your needs
• Explore R language fundamentals, including basic syntax, variables, and types
• Create functions and use control flow
• Read, write and work with data in R

• Professionals working as Data and Business Analysts
• Software professionals willing to change career path into analytics field
• Individuals having an interest in the field of Data Science
• Graduates willing to make career in Analytics and Data Science

• Should have any of these degrees in the STEM fields: Master’s /PhD/Graduate Degree
• Know the fundamentals of programming
• Know the basics of SQL
• Familiar with the basic math and statistic concepts

Module 1: Essential to R programming

Module 2: Data Manipulation Techniques using R programming

DATA SCIENCE

• An Introduction to R
  • History of  R 
  • Introduction to R 
  • The R environment  What is Statistical Programming? 
  • Why use a command line?  
  • Your first R session
• Introduction to the R language 
  • Starting and quitting R  
    • Recording your work  
  • Basic features of R 
    • Calculating with R  
    • Named storage 
    • Functions  
    • Exact or approximate?  
    • R is case-sensitive  
    • Listing the objects in the workspace  
    • Vectors 
    • Extracting elements from vectors  
    • Vector arithmetic  
    • Simple patterned vectors  
    • Missing values and other special values 
    • Character vectors  
    • Factors  
    • More on extracting elements from vectors  
    • Matrices and arrays  
    • Data frames 
    • Dates and times
• Import and Export data in R
  • Importing data in to R
    • CSV File
    • Excel File
    • Import data from text table
    • SAS and SPSS datasets
  • Exporting Data from R
    • CSV File
    • Text Table
    • Excel File
    • SAS dataset
• Merge / Join
  • Inner Join
  • Left Join
  • Right Join
  • Full Join
  • Anti Join
  • Semi Join
• Programming statistical graphics
  • High-level plots
    • Bar charts and dot charts
    • Pie charts
    • Histograms
    • Box plots
    • Scatterplots
    • QQ plots
    • Density Plot
  • Choosing a high-level graphic
  • Low-level graphics functions
    • • The plotting region and margins
      • Adding to plots
      • Setting graphical parameters
  • Programming with R  
    • Flow control
      • The for() loop
      • The if() statement
      • The while() loop
      • The repeat loop, and the break and next statements
      • Apply
      • Sapply
      • Lapply
    • Managing complexity through functions ï‚· What are functions?  
      • Scope of variables
  • Data in R 
    • Modes and Classes 
    • Data Storage in R  
    • Testing for Modes and Classes 
    • Structure of  R Objects
    • Conversion of Objects 
    • Missing Values  
    • Working with Missing Values
  • Reading and Writing Data
    • Reading Vectors and Matrices 
    • Data Frames: read.table 
    • Comma- and Tab-Delimited Input Files 
    • Fixed-Width Input Files  
    • Extracting Data from R Objects  
    • Connections  
    • Reading Large Data Files 
    • Generating Data 
      • Sequences 
      • Random Numbers  
      • Permutations  
      • Random Permutations 
      • Enumerating All Permutations  
    • Working with Sequences  v Spreadsheets  
      • The RODBC Package on Windows  
      • The gdata Package (All Platforms)
    • Saving and Loading R Data Objects 
    • Working with Binary Files  
    • Writing R Objects to Files in ASCII Format  
      • The write Function  
      • The write.table function 
      • Reading Data from Other Programs  
  • Dates
    • as.Date 
    • The chron Package  
    • POSIX Classes 
    • Working with Dates 
    • Time Intervals 
    • Time Sequences
    • Current time
    • Present date
  • Factors
    • Using Factors 
    • Numeric Factors  v  Manipulating Factors  
    • Creating Factors from Continuous Variables
  • Subscripting
    • Basics of Subscripting  
    • Numeric Subscripts  
    • Character Subscripts  
    • Logical Subscripts 
    • Subscripting Matrices and Arrays
    • Specialized Functions for Matrices  
    • Lists 
    • Subscripting Data Frames
  • Character Manipulation
    • Basics of Character Data 
    • Displaying and Concatenating Character  
    • Working with Parts of Character Values
    • Regular Expressions in R 
    • Basics of Regular Expressions 
    • Breaking Apart Character Values
    • Using Regular Expressions in R 
    • Substitutions and Tagging
  • Reshaping Data
    • Modifying Data Frame Variables  
    • Recoding Variables  
    • The recode Function 
    • Reshaping Data Frames  
    • The reshape Package 
    • Combining Data Frames 
  • Data Manipulation
    • Random Selection of rows and columns
    • Summarization
    • Sort, Arrange
    • Group by
    • Filter
  • Missing Value and Outlier
    • Identify Missing values
    • Impute missing values
    • Identify Outliers
    • Capping outliers
  • Introduction to Statistics:
    • Types of Statistics
    • Types of Data
  • Descriptive Statistics
    • Measures of Central Tendency
    • Measures of Central Tendency – Usage Chart
    • Measures of Dispersion / Variability
    • Measures of Shape
    • Application of Variance/Std Deviation
  • Hypothesis Testing
    • Applications of Hypothesis Testing (Called T Test or Z Test)
    • Steps in Hypothesis Testing 
  • Anova (Analysis of Variance)
    • What is Anova
    • Anova Steps
    • Simple One-Way Anova
    • Simple Two-Way Anova With Multiple Variables
  • Chi Square Tests
    • What is Chi-Square
    • Applications of Chi-Square
  • Correlation
    • Types of Correlation
    • Properties of Correlation
    • Methods of Calculating Correlation
    • Steps to Calculate Correlation
  • Regression Analysis
    • What is Regression
    • Types of Regression Analysis
    • Properties of The Regression Line
    • Validating the Model
    • Regression Assumptions
  • Data Transformation for Regression
  • Dummy Variable Analysis
  • Variable Selection Procedure for Regression
    • Forward Selection Procedure
    • Backward Elimination Procedure
    • Stepwise Regression Method
  • Logistic Regression
    • Likelihood Profiling
    • Assumption
    • Variable Selection Method :- Woe And Iv
    • Model Validation
    • Model Performance
    • Prediction
  • Cluster Analysis
    • What is cluster
    • Application of clustering
    • Types of clustering
    • K Means 
    • Dendrogram
    • Validation of Cluster
  • Decision Tree
    • What is decision Tree
    • How decision tree works
    • Cart
    • Pruning
    • Overfitting
    • Underfitting
    • Model validation
    • Model performance
  • Market Basket Analysis
    • What is MBA
    • Application of MBA
    • Support
    • Confidence
    • Lift
    • Rules
  • Random Forest
    • What is random forest
    • Application of random forest
    • Tune parameters
    • How to tune parameters
    • Model validation
    • Model performance
  • Support Vector Machine
    • What is support vector machine
    • Why to use SVM
    • Hyperplane
    • Kernel
    • Cost
    • Gamma
    • Model validation
    • Model performance
  • Naïve bayes
    • What is Naïve bayes
    • Bayes theorem
    • Conditional probability
    • Prior probability
    • Posterior probability
    • Application of Naïve bayes
    • Model validation
    • Model performance
  • ARIMA
    • What is time series
    • What is Arima
    • Stationary
    • Seasonality
    • Trend
    • How to find p,d,q
    • What are p,d,q
    • Find best model
    • Forecasting
  • GBM