#### DATA SCIENCE & ML USING R PROGRAMMING Training

This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science!.R is a free programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. R is an implementation of the S programming language combined with lexical scoping semantics inspired by Scheme.S was created by John Chambers while at Bell Labs. There are some important differences, but much of the code written for S runs unaltered. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team, of which Chambers is a member. R is named partly after the first names of the first two R authors and partly as a play on the name of S. The project was conceived in 1992, with an initial version released in 1994 and a stable beta version in 2000

## FUNDAMENTAL OF STATISTICS.

• Population and sample
• Descriptive and Inferential Statistics
• Statistical data analysis
• Variables
• Sample and Population Distributions
• Interquartile range
• Central Tendency
• Normal Distribution
• Skewness.
• Boxplot
• Five Number Summary
• Standard deviation
• Standard Error
• Emperical Formula
• central limit theorem
• Estimation
• Confidence interval
• Hypothesis testing
• p-value
• Scatterplot and correlation coefficient
• Standard Error
• Scales of Measurements and Data Types
• Data Summarization
• Visual Summarization
• Numerical Summarization
• Outliers & Summary

## Module 1- Introduction to Data Analytics

• Objectives:
• This module introduces you to some of the important keywords in R like Business Intelligence, Business
• Analytics, Data and Information. You can also learn how R can play an important role in solving complex analytical problems.
• This module tells you what is R and how it is used by the giants like Google, Facebook, etc.
• Also, you will learn use of 'R' in the industry, this module also helps you compare R with other software
• in analytics, install R and its packages.
• Topics:
• Understanding Business Analytics and R
• Compare R with other software in analytics
• Install R
• Perform basic operations in R using command line

## Module 2- Introduction to R programming

• Starting and quitting R
• Basic features of R.
• Calculating with R
• Named storage
• Functions
• 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
• DATA SCIENCE USING
• R-PROGRAMMING
• Topics
• Variables in R
• Scalars
• Vectors
• R Matrices
• List
• R – Data Frames
• Using c, Cbind, Rbind, attach and detach functions in R
• R – Factors
• R – CSV Files
• R – Excel File
• NOTE-:
• Assignments
• R Nuts and Bolts-:
• Entering Input. – Evaluation- R Objects- Numbers- Attributes- Creating Vectors- Mixing Objects-
• Explicit Coercion- Summary- Names- Data Frames.

## Module 3- Managing Data Frames with the dplyr package

• The dplyr Package
• Installing the dplyr package
• select()
• filter()
• arrange()
• rename()
• mutate()
• group_by()
• %>%
• NOTE-:
• Assignments

## Module 4- Loop Functions

• Looping on the Command Line
• lapply()
• sapply()
• tapply()
• apply()
• NOTE-:
• Assignments

## Module 5- Data Manipulation in R Objectives:

• In this module, we start with a sample of a dirty data set and perform Data Cleaning on it, resulting
• in a data set, which is ready for any analysis.
• Thus using and exploring the popular functions required to clean data in R.
• Topics
• Data sorting
• Find and remove duplicates record
• Cleaning data
• Merging data
• Statistical Plotting-:
• Bar charts and dot charts
• Pie charts
• Histograms
• Box plots
• Scatterplots
• QQ plots

## Objectives:

• Control Structure Programming with R
• The for() loop
• The if() statement
• The while() loop
• The repeat loop, and the break and next statements
• Apply
• Sapply
• Lapply

## Factors

• Using Factors
• Manipulating Factors
• Numeric Factors
• Creating Factors from Continuous Variables
• Convert the variables in factors or in others.

## Reshaping

• Data Modifying
• Data Frame Variables
• Recoding Variables
• The recode Function
• Reshaping Data Frames
• The reshape Package

## Module 6- Statistical Learning-:

• What Is Statistical Learning?
• Why Estimate f?
• How Do We Estimate f?
• The Trade-Off Between Prediction Accuracy and Model Interpretability
• Supervised Versus Unsupervised Learning
• Regression Versus Classification Problems
• Assessing Model Accuracy

## Module 7- Basics of Statistics & Linear & Multiple Regression

• This module touches the base of Descriptive and Inferential Statistics and Probabilities &
• 'Regression Techniques'.
• Linear and logistic regression is explained from the basics with the examples and it is
• implemented in R using two case studies dedicated to each type of Regression discussed.
• Assessing the Accuracy of the Coefficient Estimates.
• Assessing the Accuracy of the Model.
• Estimating the Regression Coefficients.
• Some Important Questions
• Lab: Linear Regression.
• Libraries .
• Simple Linear Regression
• Multiple Linear Regression
• Interaction Terms
• Qualitative Predictors
• Writing Functions
• NOTE-:
• Assignments with Different Datasets.

## Module 8- Classification-:

• An Overview of Classification.
• Why Not Linear Regression?
• Logistic Regression
• The Logistic Model
• Estimating the Regression Coefficients
• Making Predictions
• Logistic Regression for >2 Response Classes
• Lab: Logistic Regression.
• The Stock Market Data
• Logistic Regression
• NOTE-:
• Assignments with Different Datasets.

## Module 9- Variance Inflation Factor-:

• Introduction
• Multicolinearity.
• How we can detect the multicolinearity.
• Effects of multicolinearity
• Lab: VIF
• Applications.
• Reduce the features.
• NOTE-:
• Assignments with Different Datasets.
• Correlation
• Types of Correlation
• Properties of Correlation
• Methods of Calculating Correlation

## Module 10- Best Model Selection-:

• Subset Selection
• Best Subset Selection
• Stepwise Selection
• Choosing the Optimal Model
• Lab 1: Subset Selection Methods
• Best Subset Selection
• Forward and Backward Stepwise Selection
• Choosing Among Models Using the Validation Set Approach and Cross-Validation
• NOTE-:
• Assignments with Different Datasets.

## Explore many algorithms and models:

• Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
• Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. Get ready to do more learning than your machine!

## Module 11 - Machine Learning vs Statistical Modeling Supervised vs Unsupervised Learning

• Machine Learning Languages, Types, and Examples
• Machine Learning vs Statistical Modelling
• Supervised vs Unsupervised Learning
• Supervised Learning Classification
• Unsupervised Learning

## Module 12 - Supervised Learning I

• K-Nearest Neighbors
• Decision Trees
• Random Forests
• Reliability of Random Forests

## Module 13 - Supervised Learning II

• Regression Algorithms
• Model Evaluation
• Model Evaluation: Overfitting Underfitting
• Understanding Different Evaluation Models

## Module 14 - Unsupervised Learning

• Measuring the Distances Between Clusters - Single Linkage Clustering
• Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering
• Density-Based Clustering

## Module 15 - Dimensionality Reduction Collaborative Filtering

• Dimensionality Reduction: Feature Extraction Selection
• Collaborative Filtering Its Challenges

## Module 16 - Tree-Based Methods-:

• The Basics of Decision Trees
• Regression Trees
• Classification Trees
• Trees Versus Linear Models
• Bagging, Random Forests, Boosting
• Bagging
• Random Forests
• Lab: Decision Trees
• Fitting Classification Trees
• Fitting Regression Trees
• NOTE-:
• Assignments with Different Datasets.

## Module 17 - Time Series Forcasting-:

• Time series
• Estimating and Eliminating the Deterministic Components if they are present in the Model.
• Estimating and Eliminating Seasonality if it is present in the Model
• Modeling the Remainder using Auto Regressive Moving Average (ARMA) Models
• Identify 'order' of the ARMA model
• 'Forecast' or Predict for Future Values
• Practise on R
• NOTE-:
• Assignments with Different Datasets.

## Module 18 - Support Vector Machines – Outline

• Understand when the Support Vector family of methods are an appropriate method of analysis.
• Understand what a hyperplane is and how they are used with the Support Vector methods.
• Identify the differences between Maximal Margin Classifiers, Support Vector Classifiers, and Support Vector Machines.
• Know how each of the algorithms determines the best separating hyperplane.
• Distinguish between hard and soft margins and when each is to be used.
• Know how to extend the method for nonlinear cases.
• NOTE-:
• Assignments with Different Datasets.

## Module 19 - Principal Component Analysis –Outline

• Understand what principal components are and when principal component analysis is appropriate.
• Describe eigenvalues and eigenvectors and how they are used to calculate principal components.
• Know how to decide how many principal components to use in the analysis.
• Be able to use principal component analysis for regression.
• NOTE-:
• Assignments with Different Datasets.

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