Data Science with R Training Course

  Part 1: Essential to R Programming
Lecture-1 R Fundamentals 
·      History of  R

·      Introduction to R

·      The R environment

·      What is Statistical Programming?

·      Why use a command line? 

·      Your first R session

·      Practical Exercise              

Lecture-2 Basics of 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

·      Practical Exercise              

Lecture-3 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

·      Practical Exercise              

Lecture-4 Merge / Join 
·      Inner Join

·      Left Join

·      Right Join

·      Full Join

·      Anti Join

·      Semi Join

·      Practical Exercise              

Lecture-5 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

·      Practical Exercise              

Lecture-6 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 • What are functions? 

·      Scope of variables

·      Practical Exercise

  Part 2: Data Manipulation Techniques using R Programming
Lecture-7 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

·      Practical Exercise              

Lecture-8 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 

·      Practical Exercise              

Lecture-9 Dates 
·      as.Date

·      The chron Package 

·      POSIX Classes

·      Working with Dates

·      Time Intervals

·      Time Sequences

·      Current time

·      Present date

·      Practical Exercise              

Lecture-10 Factors 
·      Using Factors

·      Numeric Factors  vs.  Manipulating Factors 

·      Creating Factors from Continuous Variables

·      Practical Exercise              

Lecture-11 Subscripting 
·      Basics of Subscripting 

·      Numeric Subscripts 

·      Character Subscripts 

·      Logical Subscripts

·      Subscripting Matrices and Arrays

·      Specialized Functions for Matrices 

·      Lists

·      Subscripting Data Frames

·      Practical Exercise              

Lecture-12 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

·      Practical Exercise              

Lecture-13 Reshaping Data 
·      Modifying Data Frame Variables 

·      Recoding Variables 

·      The recode Function

·      Reshaping Data Frames 

·      The reshape Package

·      Combining Data Frames

·      Practical Exercise              

Lecture-14 Data Manipulation 
·      Random Selection of rows and columns

·      Summarization

·      Sort, Arrange

·      Group by

·      Filter

·      Practical Exercise              

Lecture-15 Missing Value and Outlier 
·      Identify Missing values

·      Impute missing values

·      Identify Outliers

·      Capping outliers

·      Practical Exercise

  Part 3: Data Science with R

Lecture-16 Introduction to Statistics: 
·      Types of Statistics

·      Types of Data

·      Practical Exercise              

Lecture-17 Descriptive Statistics 
·      Measures of Central Tendency

·      Measures of Central Tendency – Usage Chart

·      Measures of Dispersion / Variability

·      Measures of Shape

·      Application of Variance/Std Deviation

·      Practical Exercise              

Lecture-18 Hypothesis Testing 
·      Applications of Hypothesis Testing (Called T Test or Z Test)

·      Steps in Hypothesis Testing

·      Practical Exercise              

Lecture-19 Anova (Analysis of Variance) 
·      What is Anova

·      Anova Steps

·      Simple One-Way Anova

·      Simple Two-Way Anova With Multiple Variables

·      Practical Exercise              

Lecture-20 Chi Square Tests 
·      What is Chi-Square

·      Applications of Chi-Square

·      Practical Exercise              

Lecture-21 Correlation 
·      Types of Correlation

·      Properties of Correlation

·      Methods of Calculating Correlation

·      Steps to Calculate Correlation

·      Practical Exercise              

Lecture-22 Regression Analysis 
·      What is Regression

·      Types of Regression Analysis

·      Properties of The Regression Line

·      Validating the Model

·      Regression Assumptions

·      Data Transformation for Regression

·      Practical Exercise              

Lecture-23 Variable Selection Procedure for Regression 
·      Forward Selection Procedure

·      Backward Elimination Procedure

·      Stepwise Regression Method

·      Dummy Variable Analysis

·      Practical Exercise              

Lecture-24 Logistic Regression 
·      Likelihood Profiling

·      Assumption

·      Variable Selection Method :- Woe And Iv

·      Model Validation

·      Model Performance

·      Prediction

·      Practical Exercise              

Lecture-25 Cluster Analysis 
·      What is cluster

·      Application of clustering

·      Types of clustering

·      K Means

·      Dendrogram

·      Validation of Cluster

·      Practical Exercise              

Lecture-26 Decision Tree 
·      What is decision Tree

·      How decision tree works

·      Cart

·      Pruning

·      Overfitting

·      Underfitting

·      Model validation

·      Model performance

·      Practical Exercise              

Lecture-27 Market Basket Analysis 
·      What is MBA

·      Application of MBA

·      Support

·      Confidence

·      Lift

·      Rules

·      Practical Exercise              

Lecture-28 Random Forest 
·      What is random forest

·      Application of random forest

·      Tune parameters

·      How to tune parameters

·      Model validation

·      Model performance

·      Practical Exercise              

Lecture-29 Support Vector Machine 
·      What is support vector machine

·      Why to use SVM

·      Hyperplane

·      Kernel

·      Cost

·      Gamma

·      Model validation

·      Model performance

·      Practical Exercise              

Lecture-30 Naïve bayes 
·      What is Naïve bayes

·      Bayes theorem

·      Conditional probability

·      Prior probability

·      Posterior probability

·      Application of Naïve bayes

·      Model validation

·      Model performance

·      Practical Exercise              

Lecture-31 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

·      Practical Exercise              

Lecture-32 Principal Component Analysis 
·         Types of unsupervised learning,

·         Types of clustering

·         Introduction to k-means clustering

·         Math behind k-means

·         Dimensionality reduction with PCA

Lecture-33 Natural Language Processing and Text Mining 
·         Introduction to Natural Language Processing (NLP)

·         Introduction to text mining

·         Importance and applications of text mining

·         How NPL works with text mining

·         Writing and reading to word files

·         Language Toolkit (NLTK) environment

·         Text mining: Its cleaning, pre-processing, and text classification

·         Text mining use cases

·         Understanding and manipulating the text with ‘tm’ and ‘stringr’

·         Text mining algorithms

·         The quantification of the text

·         TF-IDF and after TF-IDF

Lecture-34 Introduction to Deep Learning 
·         Introduction to Deep Learning with neural networks

·         Biological neural networks vs artificial neural networks

·         Understanding perception learning algorithm,

·         Introduction to Deep Learning frameworks,

·         Tensorflow constants, variables, and place-holders

  Case Studies