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

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

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.
• 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.

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
• Advantages Disadvantages of Decision Trees

Module 13 - Supervised Learning II

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

Module 14 - Unsupervised Learning

• K-Means Clustering plus Advantages Disadvantages
• Hierarchical Clustering plus Advantages Disadvantages
• 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
• 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 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.
• Business Scenerio/Group Discussion.

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.
• Business Scenerio/Group Discussion.

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.
• Understand loading and loading vectors.
• 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.
• Business Scenerio/Group Discussion.