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