Our videos are structured in a way that gives you hands on experience on real time industry practices. We take you through end to end learning which starts from picking the data, cleaning it, building the model, finding the output, and finally interpreting and using it for the purpose in hand. One of the most outstanding feature which makes us different from the others is that we provide excel illustrations for a deeper understanding of the logic which goes behind algorithms
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CERTIFICATE IN BUSINESS ANALYTICS – EXCEL
Section 1 – Data Visualization, Data Summarization & Data Processing
Section 2 – Probability distributions, Estimation and Hypothesis Testing
Section 3 – Predictive Modelling using Stepwise Regression
Section 4 – Building Classification models using General Linear Modelling
Section 5 – Tree Based Method & Segmentation
Section 6 – Modern Supervised Learning – SVM, LDA & Naive Bayes
Section 7 – Segmentation using Clustering Techniques & PCA
Section 8 – Black Box – Neural Networks & Deep learning
CERTIFICATE IN BUSINESS ANALYTICS – R
Section 1 – Introduction to R Software & Packages
Section 2 – Data Visualization, Data Summarization & Data Processing
Section 3 – Probability distributions, Estimation and Hypothesis Testing
Section 4 – Predictive Modelling using Stepwise Regression
Section 5 – Building Classification models using General Linear Modelling
Section 6 – Tree Based Method & Segmentation
Section 7 – Modern Supervised Learning – SVM, LDA & Naive Bayes
Section 8 – Segmentation using Clustering Techniques & PCA
Section 9 – Black Box – Neural Networks & Deep learning
Section 10 – Hands-on with Industry Projects
CERTIFICATE IN BUSINESS ANALYTICS – PYTHON
Section 1 – Introduction to Python Software and Packages
Section 2 – Data Visualization, Data Summarization & Data Processing
Section 3 – Probability distributions, Estimation and Hypothesis Testing
Section 4 – Predictive Modelling using Stepwise Regression
Section 5 – Building Classification models using General Linear Modelling
Section 6 – Tree Based Method & Segmentation
Section 7 – Modern Supervised Learning – SVM, LDA & Naive Bayes
Section 8 – Segmentation using Clustering Techniques & PCA
Section 9 – Black Box – Neural Networks & Deep learning
Section 10 – Hands-on with Industry Projects
CERTIFIED MARKET RISK MODELLING
Section 1 – Monte Carlo Simulation in Excel
Section 2 – Fixed Income Analytics in Excel
Section 3 – Equity Analytics in Excel
Section 4 – Interest Rate Analytics in Excel
Section 5 – Volatility Estimation Techniques
Section 6 – Calculation of VAR & Expected Shortfall
Section 7 – Basel 4 – Fundamental Review of Trading Book
Section 7 – Stress Testing Risk Exposures
CERTIFIED CREDIT SCORECARDS DEVELOPMENT
Section 1 – Variable Exploration & Creating Data Dictionary
Section 2 – Banking Terminologies – Default Definition, Dependent Variable definition, Snapshot Data, Observation Period, Performance Period, Out- of-sample-validation, Out-of-time validation
Section 3 – Data Preparation – Creating the base dataset, creating derived variables and performing data quality checks
Section 4 – Segmentation Analysis: Identify segments which contains homogeneous pools of loans
Section 5 – Variable Selection: Information Value and Weight of Evidence
Section 6 – Model Development and Validation: Data splitting into Model development and validation datasets, Logistic Regression and Score calibration
Section 7 – Model Validation – Population Stability Index, Variable Density Index, Characteristics Stability Index,Divergence Index
Section 8 – Hands-on on Python using actual Bank data
CERTIFIED CREDIT RISK BASEL
Section 1 – Understanding Banking Products – Retail Portfolios & Commercial Products
Section 2 – Introduction to Basel Regulatory Framework
Section 3 – Banking Terminologies – Default Definition, Dependent Variable definition, Snapshot Data, Observation Period, Performance Period, Out- of-sample-validation, Out-of-time validation
Section 4 – Variable assessment, Variable Source-To-Target Mapping & Data quality check
Section 5 – Model Design & Co-variate creation
Section 6 – Building a segmentation driven model
Section 7 – Model Development – PD, LGD & EAD
Section 8 – Model Validation
Section 9 – Model Implementation – Expected Loss & Unexpected Loss Calculation
CERTIFIED CREDIT RISK IFRS
Section 1 – Introduction to IFRS 9 Framework – Understanding concepts of Staging & 12 Months/Lifetime ECL Calculation
Section 2 – Impairment Models – Simplified Approach & Generalised approach
Section 3 – Data Preparation & Data Quality Checks
Section 4 – Model Development – Roll rate analysis under Simplified approach & Transition Matrix approach under Generalised approach
Section 5 – Incorporating Forward Looking Information – Converting TTC PD to PIT PD
Section 6 – Modelling LGD & EAD & Calculating ECL
Section 7 – Model Validation – Calculate the Percentage of Error in the prediction using measures like Mean Absolute Error, Mean Absolute Percentage Error etc.
CERTIFIED COUNTERPARTY CREDIT RISK MODELLING
Section 1 – Introduction to CVA, DVA & FVA
Section 2 – Exposure Modelling – Expected MTM, EE, PFE, EPE, EEPE
Section 3 – EE & PFE of Interest Rate Swap using Vasicek model
Section 4 – EE & PFE of FRA
Section 5 – EE & PFE of FX forward
Section 6 – EE & PFE of Option
Section 7 – Concept of Netting & Collateral
Section 8 – Modelling Wrong Way Risk
Section 9 – Calculating CVA Capital Charge
CERTIFIED INSURANCE RISK MODELLING
Section 1 – Introduction to Claims loss modeling
Section 2 – Basic concepts related to probability distributions
Section 3 – Modeling number of claims using Excel: Frequency distributions
Section 4 – Modeling size of losses using Excel: Severity distributions
Section 5 – Fitting distributions to data: Parameter estimation
Section 6 – Examining goodness of fit
Section 7 – Aggregate loss modeling
Section 8 – Modelling Copulas
Section 9 – Aggregating loss estimates across Business Lines and Event types
CERTIFIED FINANCIAL ENGINEERING EXPERT
Section 1 – Brownian Motion & Martingales in Excel
Section 2 – Stochastic Calculus & Ito Process in Excel
Section 3 – Ornstein Uhlenbeck Process using Excel
Section 4 – Option Greeks calculations using Excel
Section 5 – Binomial Model & Black scholes model using Excel
Section 6 – Utility theory & Portfolio theory in Excel
Section 7 – Merton model & Credit Risk modelling using Excel
CERTIFICATE IN BUSINESS FORECASTING
Section 1 – Introduction to time series data and components of time series models
Section 2 – Estimate simple forecasting methods such as arithmetic mean, random walk, seasonal random walk and random walk with drift
Section 3 – Approximate simple moving averages and exponential smoothing methods with no trend or seasonal patterns such as Brown simple exponential smoothing method.
Section 4 – Approximate exponential smoothing methods with trend and seasonal patterns such as Holt-Winters additive, Holt-Winters multiplicative and Holt-Winters damped methods
Section 5 – Stationary Series & Unit Root test
Section 6 – Importance of differencing
Section 7 – Auto correlation (ACF) and partial auto correlation functions (PACF)
Section 8 – Box Jenkins methods (ARIMA models)
Section 9 – Model diagnostics and residual analysis
Section 10 – Models Forecasting Accuracy
Section 11 – Recurrent Neural Networks
Section 12 – Long Short Time Memory Neural Networks