DATA SCIENCE USING R PROGRAMMING Training

This is a complete tutorial to learn data science and machine learning using R. By the end of this tutorial, you will have a good exposure to building predictive models using machine learning on your own.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:
  • Business Analytics, Data, Information
  • 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
  • Recording your work
  • 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
  • NOTE:-
  • Assignments with Datasets

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
  • Business Scenerio/Group Discussion.
  • 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
  • Business Scenerio/Group Discussion.

Module 4- Loop Functions

  • Looping on the Command Line
  • lapply()
  • sapply()
  • tapply()
  • apply()
  • NOTE-:
  • Assignments
  • Business Scenerio/Group Discussion.

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
  • NOTE:-
  • Assignments with Datasets

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
  • NOTE:-
  • Assignments with Datasets

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
  • NOTE:-
  • Assignments with Datasets

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.
  • Business Scenerio/Group Discussion

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.
  • Business Scenerio/Group Discussion.

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.
  • Business Scenerio/Group Discussion.
  • 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.
  • Business Scenerio/Group Discussion.

Module 11- Tree-Based Methods-:

  • The Basics of Decision Trees
  • Regression Trees
  • Classification Trees
  • Trees Versus Linear Models
  • Advantages and Disadvantages of Trees
  • Bagging, Random Forests, Boosting
  • Bagging
  • Random Forests
  • Lab: Decision Trees
  • Fitting Classification Trees
  • Fitting Regression Trees .
  • NOTE:-
  • Assignments with Different Datasets.
  • Business Scenerio/Group Discussion

Module 15- 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.
  • Business Scenerio/Group Discussion.
  • NOTE:-
  • Assignments with Different Datasets.
  • Business Scenerio/Group Discussion
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