DATA SCIENCE USING R PROGRAMMING TRAINING

FUNDAMENTAL OF STATISTICS

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
• Multi colinearity. 
• How we can detect the multi colinearity
• Effects of multi colinearity
• 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
•