1. Descriptive  More..
   • Central Statistics
   • Central Limit Theorem
   • Summary Statistics
   • Basic Statistical Concept Building 
2. Inferential Statistics  More..
   • Introduction to Inferential Statistics
   • What is Inferential Statistics?
   • Drawing inference from data examples
   • Probability
   • Correlation and Regression
   • Fundamental Inferential Statistics
   • Hypothesis Testing
   • Working with multiple data-sets and drawing inferences
   • Estimation of population parameters from sample statistics
   • Confidence intervals and hypothesis testing within confidence intervals
   • Chi-squared test for association and goodness of fit estimation for categorical data
   • T-test
   • Z-Scores and
   • ANOVA (Manova) 

1. Linear regression  More..
   • Introduction to linear regression, usage examples
   • Regression equation and components
   • Assumptions regarding linear regression
   • Basics of Linear Regression
   • Understanding concepts of Slope, Intercept & Error
   • Calculation of parameters
   • Validation of Regression model
   • Creation of R dataset to be used for regression
   • Examination of data prior to modeling
   • Creating the regression model using relevant procedures in R
   • Understanding the output of a fundamental regression procedure
   • Common problems during model building and tests of a good model
   • Creating regression and residual plots to analyze patterns
   • Using data transformation to remedy lack of normal assumptions
   • PROC UNIVARIATE, diagnostic statistics from REG
   • Getting inputs from the final regression equation
   • Introduction to Multicollinearity, auto-correlation and outliers
   • Using VIF to test for multicollinearity
   • Using DW statistic test to test for multicollinearity
   • Outlier identification using Cook’s D/RSTUDENT/DFFITS
   • Testing model fit using F value and p value
   • Multi-variate Linear Regression, an extension of Simple Linear Regression
   • Creation of Multivariate Regression model
   • Comparison with Simple Linear Regression
   • Validation of Model- Heteroscedasticity concepts
   • Normal Distribution Curve 
2. Logistic regression  More..
   • Introduction to Logistic Regression, usage examples
   • The Likelihood Ratio Test
   • Finding a Confidence Interval for β and π
   • The Multiple Logistic Regression Model
   • Fitting the Multiple Logistic Regression Model
   • Interpretation of Coefficients
   • Dichotomous Independent Variables
   • Overview to the modelling process
   • Data loading/variable labelling/data transformation
   • Potential predictors variable selection/use visual tool to discover relationship pattern between predictors and target variable
   • Transformation of variables and final model development
   • Model validation
   • Estimating Odd Ratios in the Presence of Interaction
   • Assessing the Fit of the Logistic Regression Model
   • Summary Measures of Goodness-of-Fit
   • Area Under the ROC Curve
   • AIC
   • Weight of Evidence
   • VIF 
3. Clustering  More..
   • Clustering : Principal component analysis
   • K-means clustering
   • Factor Analysis using PCA
   • DB-SCAN introduction
   • Hierarchical clustering 
4. Decision trees  More..
   • Introduction to decision trees – simple and complex/multi-node
   • Variance
   • Gini Index
   • Entropy
   • Regression tree
   • Over-fitting
   • Pruning 
5. Time Series and Forecasting  More..
   • Introduction to Time Series Forecasting
   • Assumptions
   • Decomposition of Time Series
   • Seasonality & Stationarity
   • Forecasting Techniques
     • Moving Averages
     • Exponential Smoothing Techniques
     • Pattern Based Time series analysis
     • Step AR Techniques
     • Holt Winter Method
     • ARIMA Model
     • Box Jenkins Procedure
     • Accuracy Analysis
     • Method selection Process 

History of machine learning and current trends in industry.

What is machine learning: supervised and unsupervised learning.

What to expect and not to expect from machine learning possibilities.

1. K-Nearest neighbor classifier  More..
   • KNN introduction
   • Data Normalization
   • Examples and Application 
2. Naïve Bayes  More..
   • Introduction
   • Examples and Application
   • Limitation 
3. Random Forest  More..
   • Introduction
   • Examples and Application
   • Limitation
   • Features of Random Forests
   • How random forests work
   • he out-of-bag (oob) error estimate
   • Variable importance; Gini importance; Interactions;
   • Balancing prediction error
   • Detecting novelties 
4. Gradient boosting machines  More..
   • Introduction to bagging and boosting
   • Understanding Underlyning Mathematics
   • Practical usage examples 
5.  SVM (Support Vector Machine)  More..
   • Introduction
   • Examples and Application
   • Limitation
   • Linear Kernels
   • Cross validation
   • Radial kernels (all with examples) 
6.  Neural networks:  More..
   • Introduction to neural networks and training neural networks
   • Error and gradient calculation, backpropagation
   • Application of neural networks
   • Deep learning
   • Practical usage examples 

A comparative introduction to  R

Comparative introduction/capabilities/strong-weak areas for R

Introduction of dataset to be used for regression demonstration

Setting up R  and getting ready for classroom activity

• R
• Excel

• Dissecting Data-sets
• Industry Case Study Simulation 1
• Industry Case Study Simulation 2
• Industry Case Study Simulation 3
• Industry Case Study Simulation 4

• Interim Practical Assessment
• Final Practical Assessment
• Mock Interview

• Stress Interview Handling
• Resume Preparation
• Group Discussion

• 180 Hours
• 23 Weeks